Department: Mechanical Engineering
Name: Michael Hayes
Date Time: Monday, April 29th, 2024 - 2:00 p.m.
Advisor: Dr. André Benard
The intermittency of renewable energy sources necessitates storage technologies that can help to provide consistent output on-demand. A promising area of research is thermochemical energy storage (TCES), which utilizes high-temperature chemical reactions to absorb and release heat. While promising, TCES technologies often rely on storing chemically charged materials at high temperatures, complicating handling and posing serious challenges to long-duration storage. A pioneering approach known as SoFuel (solid state solar thermochemical fuel) proposed using counterflowing solid and gas streams in a particle-based moving-bed reactor to achieve heat recuperation and allow flows to enter and exit the reactor at ambient temperatures. Previous work has successfully demonstrated operation of a reduction (charging) reactor based on this concept; this dissertation describes the development of a companion oxidation (discharging) reactor.
The countercurrent, tubular, moving bed oxidation setup permits solids to enter and exit at ambient temperatures, but the system also features a separate extraction port in the middle of the reactor for producing high-temperature process gas. A bench-scale experimental apparatus was fabricated for use with 5 mm particles comprised of a 1:1 molar ratio of MgO to MnO, a redox material that exhibits high oxidation temperatures (around 1000° C) and excellent cyclic stability. The experimental reactor system successfully demonstrated self-sustaining thermochemical oxidation at temperatures exceeding 1000° C. Many trials achieved largely steady operation, showcasing excellent operational stability during hours-long experiments. With the aid of user-manipulated inputs, the reactor produced extraction temperatures in excess of 950° C and demonstrated efficiencies as high as 41.3%. An extensive experimental campaign revealed thermal runaway in the upper reaches of the particle bed as a risk to safe, stable reactor operation.
To better understand reactor dynamics and evaluate potential control schemes, a three phase, one-dimensional finite-volume computational model was developed. The model successfully emulated behavior from the on-reactor experiments and further illustrated the impacts of the three system inputs - solid flow rate, gas extraction flow rate, and gas recuperation flow rate - on overall behavior. A five-zone adaptive model predictive controller (MPC) was developed using a linearized control-volume model as its basis. The controller sought to regulate the size, temperature, and position of the chemically reacting region of the particle bed through several novel approaches. These approaches were tuned and refined iteratively using the 1D computational model, after which they were successfully deployed on the experimental setup. Future work concerns scaling up the oxidation system for larger rates of energy extraction, further analysis of optimal reactor startup procedures, and alternative controller formulations.
Department: Mechanical Engineering
Name: Anshul Tomar
Date Time: Friday, April 26th, 2024 - 11:00 a.m.
Advisor: Dr. Ranjan Mukherjee
Bernoulli pads can create a significant normal force on an object without contact, which allows them to be traditionally used for non-contact pick-and-place operations in industry. In addition to the normal force, the pad produces shear forces, which can be utilized in cleaning a workpiece without contact. The motivation for the present work is to understand the flow physics of the Bernoulli pad such that they can be employed for non-contact biofouling mitigation of ship hulls. Numerical investigations have shown that the shear stress distribution generated by the action of the Bernoulli pad on the workpiece is concentrated and results in maximum shear stress very close to the neck of the pad. The maximum value of wall shear stress is an important metric for determining the cleaning efficacy of the Bernoulli pad. We use numerical simulations over a range of parameter space to develop a relationship between the inlet fluid power and the maximum shear stress obtained on the workpiece. To increase the shear force distribution, we explore the possibility of adding mechanical power to the system in addition to the fluid power. The flow field between the Bernoulli pad and the workpiece involves a transition from laminar to turbulent flow and a recirculation region. The maximum shear stress occurs in the vicinity of the recirculation region and to gain confidence in the numerical solver's ability to estimate these stresses accurately, experiments were conducted with a hot-film sensor.
A direct relationship was obtained between the maximum shear stress on the workpiece and inlet fluid power using dimensional analysis. A relationship between the maximum shear stress and the inlet Reynolds number is also obtained, and implications of these scaling relationships are studied. A direct relationship between the inlet fluid power and the shear losses motivates us to explore other methods of providing power to the system with the objective of increasing shear forces and thereby improving cleaning efficacy. We numerically investigate a Bernoulli pad in which additional mechanical power is added by rotating the pad. This additional power increases both the normal and shear forces on the workpiece for the same inlet fluid power. In the context of the rotating Bernoulli pad, it was found that for a given normal attractive force, a stable equilibrium configuration can exist for two different mass flow rates, with the higher mass flow rate resulting in a higher stiffness of the flow field. This phenomenon has not been reported in the literature. The shear stress distribution, obtained using numerical simulations, is validated using experiments for the first time. A constant temperature anemometer is used with a hot-film sensor and water as the working fluid; the sensor is calibrated using a fully developed channel flow. An experimental setup is designed to calibrate and later measure the wall shear stress in a Bernoulli pad assembly. The maximum wall shear stress is observed very close to the neck of the pad due toflow constriction and separation; the hot-film experiments accurately capture the magnitude of the maximum shear stress and its location. This provides us with confidence in the numerical solver, which can be used to optimize the Bernoulli pad design to improve its cleaning efficacy.
Department: Chemical Engineering and Materials Science
Name: Christopher Herrera
Date Time: Monday, April 23rd, 2024 - 10:30 a.m.
Advisor: Dr. Richard Lunt
Interest in photovoltaics (PV) is steadily increasing with the development of building-integrated photovoltaics (BIPV). To accelerate BIPV integration, transparent PVs (TPV) have emerged to enable deployment over vision glass where visible transparency and power conversion efficiency (PCE) are equally important. Transparent luminescent solar concentrators (TLSCs) offer a promising approach to achieving high visible transparency due to a simpler module structure in the incident light path. By selectively harvesting ultraviolet (UV) and near-infrared (NIR) wavelengths, TPVs and TLSCs have a theoretical PCE limit of 20.6% for human vision. To date, TLSCs have only reported moderate PCE values with often poor or unreported operational lifetimes. This thesis focuses on modification of various luminophore classes (organic molecules, organic salts, and metal halide nanocluster salts) to provide routes to improve the performance and lifetime of TLSCs and demonstrate future applications in the agriculture sector.
Organic cyanine salts are popular luminophore candidates in TLSCs due to highly tunable, selective absorption bands with high demonstrated photoluminescent quantum yield (PLQY) in the visible region. However, they commonly suffer from poor photostability and low PLQY in the NIR region. Here, we demonstrate the surprising impact of anion exchange to dramatically enhance the lifetime of cyanine salts in a dilute environment without significantly altering the bandgap or PLQY. This enhancement results in an extrapolated lifetime increase from 10s of hours to over 65,000 hours under illumination. Using a combination of experiment and DFT computation, we demonstrate that lower absolute cation-anion binding energies generally lead to greater photostability. We then used this model to predict the stability of other anions.
Next, a class of donor-acceptor-donor (DAD) molecules are investigated to begin understanding the relationship between chemical structure and PLQY. Within this DAD class, we demonstrate a dramatic correlation between solvent environment and DAD PLQY, resulting in dramatic enhancements in PLQY with values close to 1.0. We fabricate LSCs using these DADs to report the highest single-component device performance to date.
Metal halide nanoclusters, which are precisely defined in their chemical structure, have recently been shown by our group to be a promising UV-absorbing luminophore. By changing transition metal from Mo (group 6) to Ta or Nb (group 5), the bandgap and absorption bands shift dramatically with distinct transitions present in the NIR, making them of even greater interest for TPVs and TLSCs. We explore the photophysical properties of these new compounds, contrasting them with the Mo-based clusters, and discuss pathways for TPV and TLSC integration.
Finally, we demonstrate the first plant-transparent PVs highly suitable for agricultural applications. This will initiate a new field of “transparent agrivoltaics” where the tradeoff between plant yield and power production can effectively be eliminated. We first studied the effects of varying light intensity and wavelength-selective cutoffs on commercially important crops (basil, petunia, and tomato). Despite the differences in TPV harvester absorption spectra, photon transmission of photosynthetically active radiation (PAR; 400-700 nm) is the most dominant predictor of crop yield and quality, indicating that the blue, green, and red wavebands are all essentially equally important to these plants. When the average photosynthetic daily light integral exceeds ~12 mol·m-2·d-1, basil and petunia yield and quality are acceptable for commercial production. However, even modest decreases in TPV transmission of PAR reduce tomato growth and fruit yield. The results identify the necessity to maximize transmission of PAR to create the most broadly applicable TPV agrivoltaic panels for diverse crops and geographic locations. We determine that the deployment of 10% PCE, plant-optimized TPVs over approximately 10% of total agricultural and pastureland in the U.S. would generate 7TW, nearly double the entire energy demand of the U.S.
Department: Mechanical Engineering
Name: Saima Alam
Date Time: Monday, April 23th, 2024 - 10:00 a.m.
Advisor: Dr. Norbert Mueller
Air-conditioning systems consume significant portions of energy in an automotive system, hence any improvement in performance or efficiency of automotive air-conditioning systems contribute to the energy efficiency and design economy of the vehicle. There has been massive research interest in improving the design of individual components of HVAC systems for efficiency and many of these improvements have already been implemented. However, due to the non-linear and dynamic nature of automotive air-conditioning and cooling systems, there is still room for improving the efficiency of the integrated unit by improving the control strategy for such systems instead of focusing on individual components alone.
With the advancement in machine learning and programming capabilities there are now various novel control strategies and algorithms for non-linear systems in general. To apply these algorithms, black box models of the specific air-conditioning system are used from elaborate experimental data. Despite generating optimized control parameters, these methods provide little insight to the inner dynamics of the system and how they impact system behavior. For this reason, robust physics based dynamic model of automotive air-conditioning systems is required to formulate improved control strategies.
The goal of this research is to develop a transient model of the automotive heat pump system for cabin space conditioning including the non-static time delay features of the thermal expansion valve used as expansion device. A modular trans-critical vapor compression system built at MSU sponsored by Ford was developed to run with sub-critical refrigerants for experimental validation of the model and system identification tests. From the understanding of the thermal expansion valve dynamics a method was developed to control an electronic expansion valve to perform exactly like or better than the specimen thermal expansion valve in the system. The heat pump cycle simulation model results matched with experimental results with an acceptable error margin and the system coefficient of performance with the developed controller strategy for the electronic expansion valve was found equivalent of the cycle with the specimen thermostatic expansion valve. This work will enable easy conversion from TXV to EXV systems by recommending hardware features and control parameters for similar performance level in automotive systems. Furthermore, generalized transfer functions of the components were developed for analysis and recommendation of improved control strategy in automotive air-conditioning systems using thermal and electronic expansion valves.
Department: Civil and Environmental Engineering
Name: Brijen Miyani
Date Time: Sunday, April 22nd, 2024 - 1:00 p.m.
Advisor: Dr. Irene Xagoraraki
The recent COVID-19 pandemic has highlighted the importance of wastewater-based-epidemiology (WBE) methods to effectively monitor and predict infectious viral disease outbreaks. Traditional disease detection systems rely on identification of infectious agents by diagnostic analysis of clinical samples, often after an outbreak has been established. Those surveillance systems are lacking in their ability to predict outbreaks, since it is impossible to test every individual in a community for all potential viral infections that may be emerging. Untreated wastewater may serve as a community-based sample that can be tested to identify the diversity of endemic and emerging human viruses prevalent in the community. WBE can help reduce the load of medical systems, guide clinical testing, and provide early warnings. This dissertation presents innovative screening tools based on molecular methods, high throughput sequencing, and bioinformatics analysis that can be applied in the analysis of wastewater samples to identify viral diversity in the corresponding catchment community. Further, population biomarker methods were developed to normalize the signals. The first chapter of the dissertation focuses on an application of a bioinformatics-based screening tool to reveal high abundance of rare human herpesvirus 8 in Detroit wastewater. The second chapter focuses on early warning of the COVID-19 second wave in Detroit MI. The third chapter focuses on surveillance of SARS-CoV-2 in nine neighborhood sewersheds in Detroit Tri-County area, United States and assessing per capita SARS-CoV-2 estimations and COVID-19 incidence. The fourth chapter uses molecular method to identify a wide variety of human viruses in Trujillo-Peru wastewater and confirms COVID-19, monkeypox, and diarrheal disease outbreaks. The fifth chapter reveals signals of polio 1 and 3 detected in municipal wastewater in Trujillo-Peru and discusses the implications of positives results in communities.
Department: Mechanical Engineering
Name: Bryce Thelen
Date Time: Monday, April 15th, 2024 - 10:00 a.m.
Advisor: Dr. Elisa Toulson
Research into technologies directed towards the improvement in the efficiency of the internal combustion engine has been motivated over the past several years by the regulation of the United States automotive market to more stringent standards for fuel economy and emissions. Lean burn operation of spark-ignited (SI) internal combustion engines may have the potential to help meet the high fuel economy goals of the future decade by improving the efficiency of SI engines at partial loads. Although gains in efficiency are found for engines operating with diluted mixtures, these mixtures present difficulties that manifest themselves through the slow flame speeds and poor ignitability associated with lean or diluted air-fuel mixtures. Two types of ignition systems are examined here that attempt to mitigate these negative effects. They are a radio-frequency plasma-enhanced ignition system and a prechamber initiated ignition system called Turbulent Jet Ignition.
First, the effects of a plasma-enhanced ignition system on the performance of a small, single-cylinder, four-stroke gasoline engine are examined. Dynamometer testing of the 33.5 cm3 engine at various operating speeds was performed with both the engine’s stock coil ignition system and a radio frequency plasma ignition system. The radio frequency system is designed to provide a quasi-non-equilibrium plasma discharge that features a high-voltage pulsar that provides 400 mJ of energy for each discharge and voltages of up to 30 kV. Tests show improvement of the engine’s combustion stability at all operating conditions and the extension of the engine’s lean flammability limit with the radio frequency system. Particular attention is given to the improvements that the radio frequency system provides while burning lean air-fuel mixtures. Additionally, gas analysis of the 33.5 cm3 engine’s exhaust and high-speed images of the radio frequency system taken in a separate 0.4 liter optical engine are also presented.
Second, fully three-dimensional computational fluid dynamic simulations with detailed chemistry of a single-orifice turbulent jet ignition device installed in a rapid compression machine are presented. The simulations were performed using the computational fluid dynamics software CONVERGE and its RANS turbulence models. Simulations of propane fueled combustion are compared to data collected in the optically accessible rapid compression machine that the model’s geometry is based on to establish the validity and limitations of the simulations and to compare the behavior of the different air-fuel ratios that are used in the simulations. In addition to being compared to a companion experimental study, investigations into the effect of TJI orifice size and prechamber spark location are performed. The data generated in the simulations is analyzed and insights into the processes that make up the operation of the TJI are given. Finally, CFD analysis tools are applied to the early development and design of a TJI system intended for a heavy-duty diesel engine being converted to run on natural gas.
Department: Computational Mathematics, Science, and Engineering
Name: Tianyu Yang
Date Time: Friday, April 12th, 2024 - 1:00 p.m.
Advisor: Yang Yang
Ultrasound modulated bioluminescence tomography (UMBLT) is a technique for imaging the 3D distribution of biological objects such as tumors by using a bioluminescent source as a biomedical indicator. It uses bioluminescence tomography (BLT) with a series of perturbations caused by acoustic vibrations. UMBLT outperforms BLT in terms of spatial resolution. The current UMBLT algorithm in the transport regime requires measurement at every boundary point in all directions, and reconstruction is computationally expensive. In this talk, we will first introduce the UMBLT model in both the diffusive and transport regimes, and then formulate the image reconstruction problem as an inverse source problem using internal data. Second, we present an improved UMBLT algorithm for isotropic sources in the transport regime. Third, we generalize an existing UMBLT algorithm in the diffusive regime to the partial data case and quantify the error caused by uncertainties in the prescribed optical coefficients.
Department: Computer Science and Engineering
Name: Steven Grosz
Date Time: Thursday, April 11th, 2024 - 1:30 p.m.
Advisor: Dr. Anil Jain
Fingerprint recognition is a long-standing and important topic in computer vision and pattern recognition research, supported by its diverse applications in real-world scenarios such as access control, consumer products, national identity, and border security. Recent advances in deep learning have greatly enhanced fingerprint recognition accuracy and efficiency alongside traditional hand-crafted fingerprint recognition methods, particularly in controlled settings. While state-of-the-art fingerprint recognition methods excel in controlled scenarios, like rolled fingerprint recognition, their performance tends to drop in uncontrolled settings, such as latent and contactless fingerprint recognition. These scenarios are often characterized by extreme degradations and image variations in the captured images. This performance drop is due to the inability of fingerprint embeddings (feature vectors obtained via deep networks) to generalize across variations in the captured fingerprint images between varying controlled and uncontrolled settings.
The challenges in the generalization of fingerprint embeddings, from controlled to uncontrolled settings, encompass issues such as insufficient labeled data, varying domain characteristics (often referred to as “domain gap"), and the misalignment of fingerprint features due to information loss. This thesis proposes a series of methods aimed at addressing these challenges in various unconstrained fingerprint recognition scenarios. We begin in chapter 2 with an examination of cross-sensor and cross-material presentation attack detection (PAD), where the sensing mechanism and encountered presentation attack instruments (PA) may be unknown. We present methods to augment the given training data to include a wider diversity of possible domain characteristics, while simultaneously encouraging the learning of domain-invariant representations. Next, we turn our attention in chapter 3 to the challenging scenario of contact to contactless fingerprint matching, where misaligned fingerprint features due to differences in contrast, perspective differences, and non-linear distortions are corrected via a series of deep learning-based preprocessing techniques to minimize the domain gap between contact and corresponding contactless fingerprint images. In chapter 4, we aim to improve the sensor-interoperability of fingerprint recognition by leveraging a diversity of deep learning representations, integrating convolutional neural network and attention-based vision transformer architectures into a single, multimodel embedding. Similarly, in chapter 5, we further improve the robustness and universality of fingerprint representations by fusing multiple local and global embeddings and demonstrate a marked improvement in latent to rolled fingerprint recognition performance, both in terms of accuracy and efficiency. Next, chapter 6 presents a method for synthetic fingerprint generation, capable of mimicking the distribution of real (i.e., bona fide) and PA (i.e., spoof) fingerprint images, to alleviate the lack of publicly available data for building robust fingerprint presentation attack detection algorithms. Finally, in chapter 7 we extend our fingerprint generation capabilities toward generating universal fingerprints of any fingerprint class, acquisition type, sensor domain, and quality, all to improve fingerprint recognition training and generalization performance across diverse scenarios.
Department: Chemical Engineering and Materials Science
Name: Chase Bruggerman
Date Time: Wednesday, April 10th, 2024 - 9:00 a.m.
Advisor: David Hickey
About 15% of enzymes rely on the cofactor nicotinamide adenine dinucleotide (phosphate) (NAD(P)+). The cofactor has a redox-active nicotinamide site, which can undergo a reversible two-electron-one-proton reduction to form NAD(P)H. The ability to control reactions involving NAD(P)H is a potential market opportunity, enabling the transformation of biological feedstocks with high safety (near room temperature) and selectivity (both regio- and stereoselectivity). However, the cost of NAD(P)+ – tens to hundreds of thousands of dollars per mole – is prohibitively high. An appealing way to lower the cost barrier is to regenerate a catalytic amount of NAD(P)H from electrochemical reduction of NAD(P)+; however, the reduction is often intercepted after the first electron transfer to give an enzymatically-inactive dimer. The ability to design systems for regenerable NADH is hindered by a lack of understanding of which structural features correlate with dimerization, and which features correlate with reduction to NAD(P)H. Cofactor mimetics (mNAD+), which retain the redox active nicotinamide site but have variable molecular structures, have been explored as a platform for understanding the structure-function relationships governing the redox behavior of these cofactors.
The purpose of the present thesis is to explore the electrochemistry of mNAD+, to understand which structural features correlate with dimerization, and how systems can be designed to favor reduction to mNADH over mNAD dimer. First, an overview will be presented of the chemistry and electrochemistry of NAD+ and mNAD+, with a special emphasis on methods of quantifying dimerization rates. The next part of the presentation explores the effect of both the molecular structure and the counterion of mNAD+ on the dimerization rate, using alternating current voltammetry. It is shown that dimerization is faster at lower reduction potentials and, counterintuitively, when sterics at the 1-position are larger; the data suggest the reduction of mNAD+X- ion pairs rather than lone mNAD+ ions. The second half of the talk will explore conditions that favor the reduction of mNAD+ to mNADH, and it is shown that sodium pyruvate favors the reduction of mNAD+ to a product that is electrochemically indistinguishable from mNADH. Evidence is provided in support of an interaction between an mNAD radical and a pyruvate radical, with mNAD increasing the rate of electron transfer to pyruvate. Finally, the impact of pyruvate on product distribution of mNAD+ is explored with bulk electrolysis experiments.
Department: Computer Science and Engineering
Name: Declan McClintock
Date Time: Monday, April 8th, 2024 - 10:00 a.m.
Advisor: Dr. Charles Owen
Serious games research shows that games can increase engagement and improve learning outcomes over traditional instruction, but the impact of specific elements of serious games has yet to be fully explored across many contexts. Additionally, many existing intervention studies omit the details of the game design and development theory that informed the creation of the games used in the study. This abandons an important level of context surrounding why the games were successful and does a disservice to the field by not propagating useful design theory.
Two issues with existing game design theories are that they do not build fully on top of each other and that they leave out practical guidelines for their use in the design and development processes. This leads to further limiting the spread of useful design theory and limiting its impacts in industry and academia. The work in this thesis carefully outlines the influence of existing game design theory on the design and development of a game project built to study the impact of the narrative element of serious games. Additionally, this thesis builds a new framework aimed at being more comprehensive, easily built on top of, and with clear practical guidelines for its use. The main study in this thesis studies the engagement of students playing a single serious game with a cohesive narrative compared against multiple games without a narrative tying those games together. These two cases covered the same set of learning content and differ only in their narratives. The results suggest that either approach is likely to have the same results on engagement but that there is merit to explore learning outcomes further.
This study’s research is supported by design research explaining the design theory behind the games developed for and used in the experiment as well as more specific details of the games’ production. This allows the results to be understood within a larger serious game design and development context that will help inform future work. Additionally, this thesis expands on the lessons learned from the design research and criticisms of existing frameworks to produce the Iterative Game Design and Development framework (IGDD). IGDD provides a broader framework for game design and development with guidelines for its application in practice. The IGDD framework also provides an explanation for how it should be modified and built off of to both allow it to be used across many contexts and to allow future theory building to build collaboratively on top of previous works rather than adjacent to and in assumed competition with other design theory.
Department: Biomedical Engineering
Name: Logan Soule
Date Time: Monday, April 8th, 2024 - 9:00 a.m.
Advisor: Prof. Dana Spence
Red blood cell (RBC) transfusions are life-saving procedures for a wide variety of patient populations, resulting in nearly 30,000 transfusions each day within the United Sates. However, transfusions can also result in complications for patients, including inflammation, edema, infection, and organ dysfunction. These poor transfusion outcomes may be related to irreversible chemical and physical damages that occur to RBCs during storage, called the “storage lesion”. These damages, including diminished ATP production/release, decreased deformability, increased oxidative stress, and increased membrane damage, may result in poor functionality when transfused. The damage that occurs during storage may be due to the hyperglycemic nature of current anticoagulants and additive solutions used for RBC storage. All FDA approved storage solutions contain glucose at concentrations that are over 8x higher than the blood stream of a healthy individual. Previous work has already shown that storing RBCs at physiological concentrations of glucose (4-6 mM), or normoglycemic conditions, resulted in the alleviation of many storage-induced damages, including an increase in ATP release, increased deformability, reduced osmotic fragility, and decreased oxidative stress. However, this storage technique was also accompanied by many limitations in its translation to clinical practice. The manual feeding of glucose to normoglycemic stored RBCs to maintain physiological levels of glucose introduced both a breach in sterility and unreasonable labor requirements that could not be translated to clinical practice. Additionally, the low-volume storage (< 2 mL) method with custom PVC bags used in previous work may not illicit similar benefits when scaled up to larger volumes with commercially available blood collection bags.
This work overcame these limitations through the design and implementation of an autonomous glucose delivery system that maintained normoglycemia of stored RBCs completely autonomously for 39 days in storage, while also maintaining sterility. This system was then used to store RBCs under normoglycemic conditions and monitor key storage lesion indicators, resulting in reduced osmotic fragility, decreased oxidative stress, and reduced morphological changes. There was also no impact on glycolytic activity or hemolysis levels, improving upon previous work which reported significant hemolysis that surpassed the FDA threshold of 1%. These data solidify and improve upon previous results, indicating that normoglycemic RBC storage results in reduced damages in storage that may translate to better in vivo function. The autonomous glucose delivery system also significantly advances the applicability of the normoglycemic storage technique to clinical practice, making large scale studies now possible. Additionally, a novel rejuvenation therapy was investigated, highlighting the capability of albumin, an abundant plasma protein, to reverse the membrane damages seen during RBC storage, resulting in RBCs closer in shape and size to that of fresh RBCs.
Department: Mechanical Engineering
Name: Philipp Schimmels
Date Time: Friday, April 5th, 2024 - 1:00 p.m.
Advisor: Dr. Andre Benard
Large-scale storage of renewable energy is necessary to increase reliability of this intermittently, but abundantly available resource. Of special concern is the storage of energy and its subsequent use in industrial processes requiring high temperature heat. A promising emerging technology is based on using redox reactions of metal oxides at high temperatures. The shelf-stable redox material MgMnO was identified as a potential candidate due to its high energy density, cyclic stability, high reaction temperature and good scalability. This work describes the conception, design, manufacturing, testing and improvement of a solid fuel reduction reactor used to charge the energy storage material MgMnO. The reactor enables continuous charging of the pelletized material via a packed bed moving through a 1500°C furnace. A counter-currently flowing sweep gas is used to separate the released oxygen from the charged material to prevent re-oxidation. It also acts as a heat recuperation carrier that cools charged particles and pre-heats particles before entering the reaction zone. This approach enables high thermal efficiency as the sensible heat is almost entirely recovered. A lab-scale reactor was built and tested successfully. Challenges such as particle flowability at high temperatures, fluidization of the bed, and low extent of reaction were encountered and solved by managing the counter-flowing gas and increasing the residence time of the particles in the reactor. The reactor output reached a maximum of 2500 W of charged chemical potential. Several models were developed and used to design experiments and validate the performance of the system. High energetic cost for separation of oxygen and sweep gas nitrogen was identified as a roadblock to improved efficiencies and potential scale-up of the system. This led to mathematical and experimental investigation of using water vapor as alternative sweep gas. Results show that water vapor is superior to nitrogen as a reducing agent and has a lower energetic cost of production. The proposed reactor can be scaled up and results of this study indicates that using the pelletized MgMnO pelletized material offers thermochemical energy storage at low-cost. The extraction of this energy at high temperature offers a path toward the decarbonization of a variety of industrial processes that are currently relying on the combustion of hydrocarbon fuels for high-grade heat.
Department: Electrical and Computer Engineering
Name: Yu Zheng
Date Time: Friday, April 5th, 2024 - 8:30 a.m.
Advisor: Dr. Mi Zhang
The significant progress of deep learning models in recent years can be attributed primarily to the growth of the model scale and the volume of data on which it was trained. Although scaling up the model with sufficient training data typically provides enhanced performance, the amount of memory and GPU hours used for training provides great challenges for deep learning infrastructures. Another challenge for training a good deep learning model is the quantity of the data it was trained on. To achieve state-of-the-art performance, it has become a standard way to train or fine-tune deep neural networks on a dataset augmented with well-designed augmentation transformations. This introduces difficulties in efficiently identifying the best data augmentation strategies for training. Furthermore, there has been a noticeable increase in the dataset size across many learning tasks, making it the third challenge of modern deep learning systems. The dataset size becomes very large posing great burdens on storage and training cost. Moreover, it can be prohibitive to perform hyperparameter optimization and neural architecture search on networks trained on such massive datasets.
In this dissertation, we address the fist challenges from a model-centric perspective. We propose MSUNet, which is designed with four key techniques: 1) ternary conv layers, 2) sparse conv layers, 3) quantization and 4) self-supervised consistency regularizer. These techniques allow faster training and inference of deep learning models without sacrificing significant accuracy loss. We then look at deep learning systems from a data-centric perspective. To deal with the second challenge, we propose Deep AutoAugment (DeepAA), a multi-layer data augmentation search method which aims to remove the need of crafting augmentation strategies manually. DeepAA fully automates the data augmentation process by searching a deep data augmentation policy on an expanded set of transformations. We formulate the search of data augmentation policy as a regularized gradient matching problem by maximizing the cosine similarity of the gradients between augmented data and original data with regularization. To avoid exponential growth of dimensionality of the search space when more augmentation layers are used, we incrementally stack augmentation layers based on the data distribution transformed by all the previous augmentation layers. DeepAA achieves the best performance compared to existing automatic augmentation search methods evaluated on various models and datasets. To tackle the third challenge, we proposed a dataset condensation method by distilling the information from a large dataset to a small condensed dataset. The data condensation is realized by matching the training trajectories of the original dataset with that of the condensed dataset. Experiments show that our proposed method outperforms the baseline methods. We also demonstrate that the method can benefit continual learning and neural architecture search.
Department: Computer Science and Engineering
Name: Junwen Chen
Date Time: Thursday, April 4th, 2024 - 2:00 p.m.
Advisor: Yu Kong
Action recognition is a crucial aspect of video understanding, with considerable progress being made in studies based on curated short video clips. However, in real-world scenarios, videos are often long-form and untrimmed, providing continuous surveillance of our surroundings. Unfortunately, progress in action recognition for long-form videos lags behind. Unlike short-term videos that concentrate on a single action, the primary challenge in long-form videos lies in understanding multiple actions/events within the footage to perform complex reasoning.
In this thesis, I will introduce my research endeavors in developing models to comprehend long-form videos. The first part of the thesis delves into perceiving the rich dynamics in long-form videos. My research seeks to learn fine-grained motion representation across multiple actions/events over a long-horizon range, by exploiting the potential of multi-modal context. The second part focuses on leveraging the long-range dependencies of the events in boosting temporal reasoning downstream tasks. Finally, considering the wide applications of video models, we work on cultivating trustworthiness in the models for long-form videos from static bias mitigation and interpretable reasoning perspectives.
Department: Civil and Environmental Engineering
Name: Hao Dong
Date Time: Thursday, April 4th, 2024 - 1:30 p.m.
Advisor: Dr. Kristen Cetin
In the United States, the residential and commercial sectors have consumed increasingly more energy over the past 70 years. As the U.S. shifts towards a carbon-neutral electric grid, electrification using fossil fuel-free, renewable energy resources such as wind and solar will help to reduce greenhouse gas (GHG) emissions. To reduce the need for fossil fuels and utilize energy more efficiently, technologies and policies are introduced to help decrease the demand-side intensity of building sectors. Three issues are addressed in this research to support the goals of smart buildings or net energy-zero buildings (NEZB) to achieve human comfort and demand-side management (DSM): sensing technology sensitivity for smart building controls, occupants’ patterns and correlations in residential buildings, and appliance use in residential buildings.
First, there has been a lack of studies and guidance on the appropriate placement of various sensors within a building and how this sensor placement impacts building control performance. This research thus first investigates (i) how sensitive controls of buildings are to sensor placement, in particular, sensor location and orientation. Sensor placement impact analysis helps to investigate the impact on energy use and demand for an integrated lighting and shading control system. Second, various studies have shown that occupancy-related factors in energy modeling can create significant differences in building energy consumption. Human-related factors, especially occupants’ activities and behavior, are less well understood, especially in the wake of lifestyle changes that have occurred as a result of the pandemic. This research thus (ii) assesses and quantifies the changes to occupancy patterns and the relationship to the socioeconomic factors that have occurred due to the COVID-19 pandemic. Finally, the third topic focuses on demand-side management (DSM), which enables the ability to control the quantity and timing of electricity consumption. Approximately one-third of this consumption is from large appliances, many of which are occupancy-driven loads. Historically, energy use information for estimating the energy use of individual appliances has originated from a combination of field-collected and simulated data. However, this data originates from sources assessing pre-pandemic energy consumption patterns, thus there is a need to (iii) assess how energy use patterns of appliances have changed during and post-pandemic. This research thus helps to estimate demand reduction opportunities from the use of appliances in DSM applications.
Department: Chemical Engineering and Materials Science
Name: Lincoln Mtemeri
Date Time: Thursday, April 4th, 2024 - 1:00 p.m.
Advisor: Dr. David P. Hickey
Cell-free bioelectrocatalysis has drawn significant research attention as the world transitions towards sustainable bioenergy sources. This technology utilizes electrodes to drive challenging enzymatic redox reactions, such as CO2 reduction and selective oxidation of lignin biomass. At these bioelectrochemical interfaces, enzymes are rarely capable of direct exchange of electrons with the electrode surface because many redox enzymes harbor cofactors that are buried within protein matrices that acts as an electrical insulator. In such cases, electrochemically active small molecules, called redox mediators, have proven effective in enabling efficient electron transfer by acting as electron shuttles between the electrode and enzyme cofactor. However, the task of selecting suitable redox mediators remains challenging due to lack of a comprehensive design criteria. Presently, their design relies on a trial-and-error approach that emphasizes redox potential as the only parameter while overlooking the significance of other structural features. It is crucial to acknowledge that while the redox potential of the mediator serves as a thermodynamic descriptor, it falls short in fully describing the kinetic behavior of redox mediators. In this seminar, I present our efforts in developing strategies for designing and understanding the behavior of redox species using quinone-mediated glucose oxidation by glucose oxidase as a model system.
This seminar will begin by describing the application of parameterized modeling – specifically, supervised machine learning – to identify which structural components of quinone redox mediators correlate to enhanced reactivity with a model enzyme, glucose oxidase (GOx). Through this analysis, we identified redox potential and mediator area (or molecular size) as crucial chemical parameters to optimize when designing mediators. We further explored the role of the steric parameter (i.e. redox mediator projected area) when accessing GOx via its active site tunnel. Using two complementary computational techniques, steered molecular dynamics and umbrella sampling, a rate-limiting step was identified from a series of elementary steps. Specifically, we determined that the transport of redox species in the protein tunnel constitutes the rate-limiting step in the overall process.
Utilizing molecular docking and molecular dynamics simulations, we examined a specific quinone-functionalized polymer with the goal of determining why it exhibits activity with glucose dehydrogenase (FAD-GDH) but not with GOx, despite both structurally similar enzymes exhibiting activity to the corresponding freely diffusing mediator. Docking simulations coupled with MD refinement reveal that the active site of GOx is inaccessible to the polymer-bound redox mediator due to the added steric bulk; this is in contrast to FAD-GDH which has a wider molecular tunnel to its active site.
Although, these strategies for redox mediator design and engineering were developed using GOx as a model system, a similar approach holds promise for designing systems involving other redox mediators. This work demonstrates that this technique of employing parameterized modeling in designing mediators has the potential to be applied in other bioelectrocatalytic platforms. Moreover, the computational simulations can effectively address fundamental questions where continuum models are inadequate. This integrated effort brings us closer to design of next-generation effective bioelectrodes for mediated bioelectrocatalysis.
Department: Electrical and Computer Engineering
Name: Daniel Chen
Date Time: Thursday, April 4th, 2024 - 9:00 a.m.
Advisor: Dr. Jeffrey A. Nanzer
The need for fast and reliable sensing at millimeter-wave frequencies has been increasing dramatically in recent years for a wide range of applications including non-destructive evaluation, medical imaging, and security screening such as concealed contraband detection. Imaging based approaches have been of particular interest since the wavelengths at millimeter-wave frequencies provide good resolution and are capable of propagating through clothing with negligible attenuation allowing the identification of concealed contraband. While various implementations for millimeter-wave imaging have been developed, the new technique of active incoherent millimeter-wave (AIM) imaging, developed in our research group, is of particular interest because it solves fundamental limitations inherent in other approaches. Furthermore, AIM enables imaging with significantly fewer elements than phased arrays and costs less than passive imagers. This is enabled by actively transmitting noise signals, allowing the system to capture scene information in the spatial Fourier domain. When the received signal at each of the array elements are spatio-temporally incoherent, the spatial coherence function of the captured signals represent samples of the measured visibility which can be further processed via an inverse Fourier transformation to recover the measured scene. With a good quality recovered image, additional processing can be applied for detection and/or classification on specific spatial features. However, images often contain more than the required information necessary for effective classification results which means that unnecessary resources are used for the collection and processing of redundant information.
In this dissertation, I present on the design and analysis of array dynamics for radar and remote sensing applications. Specifically, I investigate approaches to measure specific spatial Fourier information which can be useful for direct classification therefore eliminating the need of full image recovery. I present an adapted formulation of the spatial coherence function by considering individual antenna trajectories within a dynamic antenna array. The measured visibility, hence, becomes a function of array trajectory over a slow time dimension. The use of array dynamics further reduces the hardware requirements in the AIM technique by introducing a new degree of freedom in the array design. By allowing the receiving elements of the antenna array to dynamically move across the measurement plane, the spatial Fourier domain can be efficiently sampled using as few as two receiving antennas. Discussion of the effects of trajectory approaches on the measured spatial Fourier information are presented. Furthermore, I expand on a specific array trajectory where as few as two antennas can generate a ring filter (i.e., spatial Fourier sampling function exhibiting a form of a ring) that can efficiently identify spatial Fourier artifacts pertaining to sharp edges in the scene. This approach enables an imageless approach to differentiate scenes containing objects with sharp-edge that are generally made artificially. I then present a real-time rotational dynamic antenna array operating at 75 GHz with two noise-transmitting sources as required by the AIM technique and two receivers to generate the ring filter. Compared to traditional millimeter-wave imaging, this non-imaging approach further reduces the required number of antennas. Experimental measurements using the AIM based rotational dynamic antenna array demonstrate the possibility of detecting concealed contraband via the direct measured spatial Fourier domain information.
Department: Mechanical Engineering
Name: Ru Tao
Date Time: Monday, April 1th, 2024 - 2:30 p.m.
Advisor: Dr. Michele Grimm
Vaginal childbirth, also known as delivery or labor, is the ending phase of pregnancy where one or more fetuses pass through the birth canal from the uterus, which is a biomechanical process. However, the risky process can cause significant injuries to both the fetus and the mother, such as brachial plexus injury, pelvic floor disorders, or even death. Due to technical and ethical reasons, experiments are difficult to conduct on laboring women and their fetuses. The use of computer modeling has become a very promising and rapidly growing way to perform research to improve our knowledge of the biomechanical processes of labor and delivery. The developed simulation models in this field have either focused on the uterine active contraction or the pelvic floor muscles, individually. In addition, there are many limitations existing in the current uterus models.
The goal of the project is to develop an integrated model system including the uterus, the fetus, the pelvic bones, and the pelvic muscle floor, which will allow advanced simulation and investigation within the field of biomechanics of fetal delivery. For the first step, a computational model in LS-DYNA simulating the active contraction behaviors of muscle tissue was developed, where the muscle tissue was composed of active contractile fibers using the Hill material model and the passive portion using elastic and hyperelastic material models. The model was further validated with experimental results, which demonstrated the accuracy and reliability of the modeling methodology to describe a muscle’s active contraction and relaxation behaviors. Second, a simulation model of a whole uterus during the second stage of labor was developed, which included active contractile fibers and a passive muscle tissue wall. The effects of the fiber distribution on uterine contraction behaviors were investigated and the delivery of a fetus moving through the uterus due to the contraction was simulated. The developed uterus model included several important uterine mechanical properties, such as the propagation of the contraction wave, the anisotropy of the fiber distribution, contraction intensity variation within the uterus, and the pushing effect on the fetus. Finally, an integrated model system of labor was established by incorporating the pelvic structures with the uterus and fetus models. The model system successfully delivered the fetus from the uterus and through the birth canal. The simulation results were validated based on available data and clinically observed phenomena, such as stress distribution within the uterus, values of Von Mises stress and principal stress of the pelvic floor muscles, rotation and movement of the fetus. Overall, a Finite Element Method model system simulating the labor process was developed in LS-DYNA, which will be used to investigate disorders related to labor, such as neonatal brachial plexus injury and maternal pelvic floor muscle injuries.
Department: Mechanical Engineering
Name: Eli Broemer
Date Time: Monday, April 1th, 2024 - 11:30 a.m.
Advisor: Dr. Sara Roccabianca
Bladder health and dysfunction is not well understood. Research with mouse models is an effective way to study soft tissue/organ function especially with the genetic tools available in this species. Despite this advantage, bladder research in mice is still lacking compared to other animal models. Particularly, mechanical testing/analysis of the mouse bladder tissue are near non-existent in literature. In this dissertation, experimental ex vivo pressurization of whole mouse bladders was used to analyze the mechanical stresses and stretches in the soft tissue. Bladder filling cycles were digitally reconstructed in 4D. The reconstructions were used to characterize the geometry and mechanics of the bladder as it fills. This work contributes to the bladder mechanics literature as this level of 4D and mechanical analysis of bladder filling in a mouse model has not been shown before.
Department: Mechanical Engineering
Name: Jonathon Winslow Howard
Date Time: Thursday, March 21th, 2024 - 12:00 p.m.
Advisor: Dr. Abraham Engeda
Operation of helium cryogenic systems below the normal boiling point of helium (approximately 4.2 K) has become a common need for modern high-energy particle accelerators. Nominal cooling near 2 K (or a corresponding saturation pressure of approximately 30 mbar) is often required by superconducting radio-frequency niobium resonators (also known as SRF cavities) to achieve the performance targets of the particle accelerator. To establish this cooling temperature, the cryogenic vessel (or cryostat) containing the SRF cavities is operated at the sub-atmospheric saturation pressure by continuously evacuating the vapor from the liquid helium bath. Multi-stage cryogenic centrifugal compressors (‘cold-compressors’) have been proven to be an efficient, reliable, and cost-effective method to achieve sub-atmospheric cryogenic operating conditions for large-scale systems. These compressors re-pressurize the sub-atmospheric cryogenic helium to just above atmospheric conditions before injecting the flow back into the main helium refrigerator. Although multi-stage cryogenic centrifugal compressor technology has been implemented in large-scale cryogenic systems since the 1980’s, theoretical understanding of their operation (steady-state and transient) is inadequate to provide a general characterization of the compressor and establish stable wide-range performance. The focus of this dissertation is two-fold regarding multi-stage centrifugal compressors as used for sub-atmospheric helium cryogenic systems. First, to develop a reliable performance prediction model for a multi-stage cryogenic centrifugal compressor train, validated with measurements from an actual operating system. Capabilities of the model include steady-state performance estimation and prediction of operational envelops that ensure stable and wide-range steady-state operation. Second, to develop and validate a process model of the entire sub-atmospheric system (e.g. FRIB) and establish a simple methodology to obtain a reliable thermodynamic path for the transient (‘pump-down’) process of reducing the helium bath pressure from above 1 bar to the operational steady-state conditions near 30 mbar. The effectiveness of the developed methodology is demonstrated by comparing the estimated and measured process parameters from the sub-atmospheric system studied (i.e. FRIB). The developed model and methodology are intended to benefit the design and operation (both steady-state and transient) of multi-stage cryogenic centrifugal compressor trains used in large-scale cryogenic helium refrigeration systems.
Department: Biomedical Engineering
Name: Meghan Hill
Date Time: Wednesday, March 6th, 2024 - 12:00 p.m.
Advisor: Taeho Kim
Glioblastoma is one of the most aggressive and invasive types of cancer. Unfortunately, due to the overlapping nature of side-effects with other types of neurological diseases and the difficulty to identify them with diagnostic measures, it is not discovered until stage four. At this point, patients have limited options for care and ultimately end up in palliative care not long after diagnosis. The blood-brain barrier (BBB) has proved to be a difficult boundary for current modern medicines as it prevents adequate accumulation within the brain. As gliomas often form in inoperable parts of the brain, conventional FDA-approved therapies prove to be ineffective. Within the past ten years, targeting strategies using RGD peptides have proven effective at transporting drugs, contrast agents, or nanoparticle delivery vehicles across the barrier, but suffer from off-targeting effects due to expression of the peptide-recognizing integrins on the surface of healthy cells. Extracellular vesicles, particularly exosomes, have shown promising specific targeting effects of cells from which the vesicles originate. They have also shown a remarkable ability to pass through the BBB innately. The focus of this project was the development of a glioblastoma derived-exosome coated Prussian Blue nanoparticle (Exo:PB) that could easily accumulate within glioblastoma tissues and provide enhanced diagnostics as well as localized therapy. Prussian Blue nanoparticles are FDA-approved for scavenging heavy metals present within the body after extreme radiation exposure. Based on their exceptional application to photothermal therapy and ability to be used for photoacoustic imaging and MRI, they are an ideal candidate for glioblastoma theranostics. By investigating the distribution and accumulation patterns of these newly developed Exo:PB nanoparticles within preclinical mouse models, earlier diagnosis and treatment intervention can be achieved for glioblastoma.
Department: Computer Science and Engineering
Name: Guangyue Xu
Date Time: Thursday, February 15th, 2024 - 12:00 p.m.
Advisor: Parisa Kordjamshidi
Humans learn concepts in a grounded and compositional manner. Such compositional and grounding abilities enable humans to understand an endless variety of scenarios and expressions. Although deep learning models have pushed performance to new limits on many Natural Language Processing and Computer Vision tasks, we still have a lack of knowledge about how these models process compositional structures and their potential to accomplish human-like meaning composition. The goal of this thesis is to advance the current compositional generalization research on both the evaluation and design of the learning models. In this direction, we make the following contributions.
Firstly, we introduce a transductive learning method to utilize the unlabeled data for learning the distribution of both seen and novel compositions. Moreover, we utilize the cross-attention mechanism to align and ground the linguistic concepts into specific regions of the image to tackle the grounding challenge.
Secondly, we develop a new prompting technique for compositional learning by considering the interaction between element concepts. In our proposed technique called GIPCOL, we construct a textual input that contains rich compositional information when prompting the foundation vision-language model. We use the CLIP model as the pre-trained backbone vision-language model and improve its compositional zero-shot learning ability with our novel soft-prompting approach.
Thirdly, since retrieval plays a critical role in human learning, our work studies how retrieval can help compositional learning. We propose MetaReVision which is a new retrieval-enhanced meta-learning model to address the visually grounded compositional concept learning problem.
Finally, we evaluate the large generative vision and language models in solving compositional zero-shot learning within the in-context learning framework. We highlight their shortcomings and propose retriever and ranker modules to improve their performance in addressing this challenging problem.
Department: Mechanical Engineering
Name: Md Sarower Hossain Tareq
Date Time: Tuesday, January 16th, 2024 - 11:00 a.m.
Advisor: Dr. Patrick Kwon and Dr. Haseung Chung
Nitinol is highly attractive for biomedical applications because of its unique shape memory and superelastic properties as well as acceptable biocompatibility. Additive manufacturing (AM) is getting significant attention in making complex and patient customizable nitinol devices. However, due to its high microstructural and compositional sensitivities, it is still challenging to fabricate functional NiTi devices via AM. It has been widely reported that evaporation of Ni, oxidation of Ti and formation of precipitation phases during fabrication significantly diverts the expected functional properties. To this date, laser powder bed fusion (LPBF) was the choice among many AM techniques to fabricate NiTi devices but successfully fabricated only on a NiTi substrate because of its poor bonding to other substrates (i.e., steel and Ti). In this work, a multi-step printing approach was systematically developed, which enabled printing NiTi on a Ti substrate using a very low laser energy density of 35 J/mm3 without any visible defect. This printing method reduced the high warping issue due to the process induced residual stress , avoided the Ni evaporation issue as well as formation of undesirable precipitation phase during printing. It was also found that a higher oxygen level in the printing chamber reduced the austenite finish (Af) temperature and negatively affected the printability. These results showed the feasibility of LPBF in printing NiTi on a substrate other than nitinol, providing a possible route to reduce the cost of NiTi fabrication via AM.
The as-printed NiTi sample exhibited distinct one-step phase transformation with the Af temperature of 2.1°C. To increase the Af temperature to 30.2°C (within the recommended range of Af temperature for biomedical applications), a heat treatment protocol was developed, which includes a solution cycle (at 900 °C for 1 hour) followed by an aging cycle (at 450°C for 30 minutes). The heat treatment protocol enabled to attain the homogenized microstructure while creating ultrafine metastable Ni-rich precipitate, Ni4Ti3, which facilitated the desirable phase transformation behavior with the increased Af temperature. The heat-treated sample showed narrower and sharper two-step martensitic phase transformation with the formation of intermediate R-phase. The presence of both Ni4Ti3 and the R-phase was confirmed by the transmission electron microscopic (TEM) analysis. In the superelasticity test at the body temperature, these samples, starting from the 2nd cycle, demonstrated a recovery ratio of more than 90% and a recoverable strain of more than 6.5%. After 10th cycles, the stable recoverable strain was 6.52% with a recovery ratio of 96%, which is the highest superelasticity reported for the LPBF processed NiTi to the best of our knowledge. After the initial deformation process, we expect these samples to attain near full superelasticity during service. The micro-hardness study also showed that the hardness of the heat-treated samples is less affected by the cyclic loading.
Nitinol stent is attractive since they are self-expandible and behave superelastically when deployed inside the body. In contrast to the multi-step conventional manufacturing route, AM is attractive in making nitinol stent since it provides one-step processing as well as wide option for customizable design. However, the individual strut of a stent is less than 150 µm which is very challenging to fabricate by LPBF with structural accuracy, mechanical integrity and maintaining proper superelasticity. In this work, the LPBF processing parameter as well as post surface finish has been systematically developed to minimize the porosity, avoid structural failure during deformation and maximize the superelastic property at body temperature. Finally, the processed thin strut showed the Af temperature of 26 °C (which is less than the body temperature) and demonstrated 91% strain recovery with 4.1% recoverable strain at body temperature.
The work presents an important roadmap in making NiTi devices by AM while maintaining excellent functional properties of NiTi for biomedical applications.
Persons with disabilities have the right to request and receive reasonable accommodation. Please call the Department of Mechanical Engineering at 355-5131 at least one day prior to the seminar; requests received after this date will be met when possible.
Department: Computer Science and Engineering
Name: Iliya Miralavy
Date Time: Thursday, December 14th, 2023 - 9:00 a.m.
Advisor: Dr. Wolfgang Banzhaf
Space, while inherent to the natural world, often finds itself omitted in bio-inspired computational system designs. Spatial Genetic Programming (SGP) is a Genetic Programming (GP) paradigm that incorporates space as a fundamental dimension, evolving alongside Linear Genetic Programming (LGP) programs. In SGP, each individual model is represented by a 2D space consisting of one-to-many LGP programs. These programs execute in an order influenced by their spatial position. The contribution of this work is multi-fold: It begins with introducing SGP as a tool for studying evolution of space in GP. Then it applies the proposed system on a various range of problems including Symbolic Regression, Classic Control and Decision-Making problems comparing it with other common GP paradigms. It also focuses on how the spatial dimension influences generational diversity, the emergence of spatially induced localization within the system, and the emergence of iterative structures within the system. The findings of this research open new avenues towards better understanding natural evolution and how the dimension of space could be useful as a handle for controlling important aspects of evolution.
Department: Biomedical Engineering
Name: Sarah Wright
Date Time: Wednesday, December 13th, 2023 - 9:00 a.m.
Advisor: Michele Grimm
Translational applications of biomedical engineering, including work to understand and reduce the risk of injuries, can involve both experimental work – in the laboratory or clinical settings – and computational modeling. This biomechanical project was conducted as part of a larger effort to understand birth-related injuries to the neonatal brachial plexus – a complex set of nerves that begins from the cervical (C5-C8) and thoracic (T1) nerve roots. During both vaginal and cesarean births, these nerves are susceptible to an injury known as Neonatal Brachial Plexus Palsy (NBPP). Conducting clinical or experimental injury analysis of NBPP is challenging due to the vulnerable population involved – infants. The use of computational modeling allows the exploration and analysis of this nerve complex to investigate the effect of maternal and neonatal parameters on brachial plexus stretch during the birth process. To date, there are no anatomically accurate adult or neonatal brachial plexus models published. An anatomically accurate finite element model (FEM) has been developed that will allow in-depth analysis of NBPP injuries by providing a better understanding of stress distribution within the nerves. In this dissertation project, the model was developed, validated, and utilized to provide insight into the progression of injury when force is applied. The outcomes of this project have advanced both computational modeling and knowledge regarding brachial plexus injury in neonates. We anticipate that our novel, three-dimensional neonatal brachial plexus model will be available in the future to simulate and study specific brachial plexus injuries (Erb’s Palsy, Klumpke’s Palsy, etc.) and to further investigate patterns of injury in NBPP. The current and future applications of the model will provide useful insight for researchers, neurosurgeons, and other medical professionals to scientifically evaluate biomechanical aspects of neonatal brachial plexus injuries – in the hope of providing useful insight into ways to lessen the chances of these injuries occurring.
Department: Computer Science and Engineering
Name: Roshanak Mirzaee Mazrae
Date Time: Monday, December 11th, 2023 - 12:30 p.m.
Advisor: Parisa Kordjamshdi
Spatial language understanding plays an essential role in human communication and perception of the physical world. It encompasses how people describe, understand, and communicate spatial relationships between objects and environmental entities, such as location, orientation, distance, and relative position. Spatial language processing presents numerous challenges, which often stem from the inherent ambiguity of natural language in describing spatial relations or the complexity of spatial reasoning to infer indirect relations, in particular, when multi-hop reasoning is needed. This thesis has four main contributions to learning and reasoning over spatial language.
The first contribution is proposing novel question-answering benchmarks to evaluate the spatial reasoning capability of deep neural models. These benchmarks include complex and realistic spatial phenomena not covered in previous work, making it more challenging for state-of-the-art language models (LM). The second contribution is an approach to generate a large distant supervision for spatial question answering and spatial role labeling tasks. We design grammar and reasoning rules to automatically generate a spatial description of scenes and corresponding QA pairs. In this approach, we integrate a diverse set of spatial relation types and expressions, complemented by additional functions, to enhance the flexibility and extensibility of the data generation process. Further training LMs on this data significantly improves their capability on spatial understanding, thereby enabling them to solve other benchmarks and external datasets better.
Furthermore, the third contribution explores the potential benefits of disentangling the processes of information extraction and reasoning in neural models to address the challenges of multi-hop spatial reasoning. To explore this, we design various models that disentangle extraction and reasoning (either symbolic or neural) and compare them with state-of-the-art baselines with no explicit design for these parts. Our experimental results consistently demonstrate the efficacy of disentangling, showcasing its ability to enhance models’ generalizability within realistic data domains.
Ultimately, the fourth contribution probes the role and impact of Large Language Models (LLMs) in spatial reasoning tasks. We evaluate the spatial reasoning capabilities of LLMs with and without in-context learning. In another approach, we integrate LLMs as the extraction module within the pipeline of extraction and symbolic reasoning. Our case studies and previous research on controlled environments demonstrate that incorporating LLMs in this pipeline can yield significant benefits. However, our experiments reveal that the intricacies of spatial language in real-world settings make the pipeline model inefficient, primarily due to escalating errors in the extraction process. We further explore the utilization of probabilistic logical reasoning and LLMs’ commonsense knowledge in real-world settings. These methods improve the model by providing comprehensive rules and relations that deterministic reasoning and the custom-designed symbolic reasoning module may not have captured before. However, even with these modifications, the pipeline model continues to exhibit inferior performance compared to LLMs.
Department: Chemical Engineering and Materials Science
Name: Thanh Tran
Date Time: Thursday, December 7th, 2023 - 1:00 p.m.
Advisor: Qi Hua Fan
In light of the escalating costs of Indium Tin Oxide (ITO), the quest for its sustainable alternatives becomes imperative. This dissertation delves into the utilization of a single-beam ion source in conjunction with magnetron sputtering to manipulate film microstructures, aiming to enhance and fabricate transparent conductive electrodes. Studies of the ion source were conducted to explore its potential applications.
In light of the escalating costs of Indium Tin Oxide (ITO), the quest for its sustainable alternatives becomes imperative. This dissertation delves into the utilization of a single-beam ion source in conjunction with magnetron sputtering to manipulate film microstructures, aiming to enhance and fabricate transparent conductive electrodes. Studies of the ion source were conducted to explore its potential applications.
Through the assistance of the ion source, an extensive range of modulation in the magnetron voltage was achieved, spanning approximately 240 to 130 V, as the ion source's voltage fluctuated from 0 to 150 V. This mechanism led to a low-voltage high-current magnetron discharge, facilitating a 'soft sputtering mode' conducive for thin film growth. Indium tin oxide (ITO) thin films were successfully deposited at room temperature by employing a combined single-beam ion source and magnetron sputtering, resulting in the creation of polycrystalline ITO thin films characterized by significantly reduced resistivity and surface roughness.
Notably, the ion beam treatment played a pivotal role in the growth of a seed layer, approximately 1 nm in thickness, enhancing the subsequent silver film's wettability. This, in turn, led to the creation of a continuous silver film of approximately 6 nm, boasting a resistivity of 11.4 µΩ.cm. This ultra-thin continuous silver film exhibited a transmittance spectrum comparable to simulation results and displayed greatly improved film adhesion on glass, as validated by the standard 100-grid tape test. High-resolution SEM images of the early growth stage show that the ion beam treatment leads to the wide spread of deposited silver whereas the films without the ion beam treatment tend to agglomerate into isolated round islands. The XRD patterns show that the (111) crystallization of silver films is suppressed with the soft ion beam treatment, whereas the growth of (200) planes is fortified. The results indicate that silver films grown on the (200) surface have less tendency to agglomerate than on the (111) surface.
However, the inherent instability of silver films posed a challenge. To address this, an approach involving a cap layer of aluminum on silver was introduced to enhance the thermal and environmental stabilities of deposited ultra-thin continuous silver films, measuring approximately 7 nm thick. The resulting film, composed of a 1 nm buffer layer of ion beamtreated silver, a layer of pure silver sputter-deposited, and a 0.2 nm nominal thick cap layer of aluminum, significantly bolstered the film's stability without a marked compromise on its optical and electrical properties. The improved environmental stability was attributed to the cathodic protection mechanism and reduced surface atom diffusivity, while the thermal stability was credited to the reduction of surface atom mobility in the presence of aluminum atoms. Further, thermal treatment of the duplex film led to an enhancement in its electrical conductivity and optical transmittance owing to an improvement in crystallinity. The annealed aluminum/silver duplex structure exhibited low electrical resistance and high optical transmittance, comparable to simulated results, positioning it among the top films reported.
The stabilized ultra-thin silver films were then leveraged to craft highly transparent and conductive electrodes on glass substrates in a sandwich structure with optimized layers of indium tin oxide (ITO). Notably, exceptional thermal stability was achieved, and annealing at 200°C in vacuum and air enhanced the film's optical and electrical performance. X-ray diffraction analysis validated the enhanced crystallization, manifested by the emergence of a silver (200) peak after air annealing. The resultant electrodes showcased outstanding transparency, conductivity, and thermal stability, positioning them favorably for architectural glass coatings and optoelectronic applications such as photovoltaics and displays.
Further computational works were conducted to study the optical performances of six different sandwich structures on glass, comprising typical transparent conductive oxides with an ultra-thin layer of silver at 6 nm and 7 nm in the middle. The study returned contour maps of average optical transmittances in 300-1200 and 400-800 nm wavelength ranges along the thicknesses of the top and bottom oxides in the 0-100 nm range with a step size of 5 nm. The simulation also provides the optimum designs and their corresponding transmittance spectra for each sandwich structure. Among tested structures, Glass/TiO2/Ag/AZO exhibited the highest average transmittance of 90.8% in the 400-800 nm range, while Glass/TiO2/Ag/SnO2 demonstrated the highest average transmittance of 83.3% in the 300-1200 nm range. These structures, along with Glass/SnO2/Ag/SnO2, are found to have good optical performance and could replace ITO in solar-cell and display applications, theoretically. This dissertation also shows some other examples of optimizing the optical performance of the structures for specific applications.
Furthermore, a case study was conducted to explore the use of tantalum-doped tin oxide (TTO) as a viable alternative to ITO. Employing a room temperature treatment facilitated by a single beam ion source, highly transparent and conductive TTO films were produced. Specifically, DC sputtering of 100 nm TTO thin films, using a TTO target (Sn(1-x)TaxO2 with x=0.02, 99.99% purity) combined with a soft ion beam generated by an ion source at 120 V, revealed that with ion beam assistance, the TTO thin film achieved a resistivity as low as 9.3 mΩ.cm and an average transmittance of 79% in the 400 nm to 1200 nm range. In contrast, without ion beam assistance, the minimum resistivity achieved was 15.9 mΩ.cm, accompanied by an average transmittance of 78% within the same wavelength range.
Department: Electrical and Computer Engineering
Name: Bharath Basti Shenoy
Date Time: Thursday, December 7th, 2023 - 8:00 a.m.
Advisor: Lalita Udpa and Sunil Chakrapani
Part I of this dissertation defense explores the application of Magnetic Barkhausen Noise and Non-Linear Eddy Current techniques for the early-stage detection of fatigue in ferromagnetic materials, with a specific focus on Martensitic Stainless-steel samples. Due to its exceptional mechanical properties at elevated temperatures, stainless steel finds extensive use in various applications. However, material fatigue poses a significant challenge in steel structures, leading to potential catastrophic damage and substantial economic consequences. While conventional nondestructive evaluation techniques excel at detecting macro defects, they often fall short in identifying material degradation at the microstructure level, particularly arising from fatigue.
The Magnetic Barkhausen Noise technique involves capturing signals generated by the movement of domain walls after applying a time-varying magnetic field. Different fatigue stages yield unique Magnetic Barkhausen Noise signatures, facilitating effective classification. In the Non-Linear Eddy Current technique, a robust external magnetic field induces non-linear behavior in the material's magnetization characteristic. The harmonics extracted from the Non-Linear Eddy Current signal provide insights into the material's microstructure, aiding in the classification of samples at various fatigue stages. The research work systematically investigates the feasibility of Magnetic Barkhausen Noise and Non-Linear Eddy Current techniques by employing customized sensor assemblies to capture and analyze signals in both time and frequency domains. Extracted features are further processed using k-medoids clustering algorithm, and Genetic algorithm for robust classification into distinct fatigue stages. The comparative performance of the two magnetic non-destructive evaluation techniques is thoroughly examined.
The research findings indicate that both Magnetic Barkhausen Noise and Non-Linear Eddy Current techniques present promising capabilities for detecting early-stage fatigue in Martensitic Stainless-steel samples and contributes to advancing the fatigue detection in ferromagnetic structures using magnetic non-destructive evaluation techniques.
In Part II of this dissertation defense, the focus is on addressing critical challenges of monitoring the structural health of engineering structures, which are susceptible to damage from both stress and environmental factors. Traditional ultrasonic nondestructive evaluation techniques typically involve contact-based procedures that necessitate the use of a couplant. However, this thesis explores the use of Electromagnetic Acoustic Transducers, which offer a compelling non-contact alternative. Electromagnetic Acoustic Transducers utilize the Lorentz force, acting on induced currents, to excite elastic waves in a sample, eliminating the need for direct contact. The drawback of conventional Electromagnetic Acoustic Transducers being limited to conductive or ferromagnetic samples is addressed through the introduction of a novel Electromagnetic Acoustic Transducer, specifically designed for non-conductive samples.
This novel Electromagnetic Acoustic Transducer presents two distinct configurations: (a) Direct excitation and (b) Non-contact induced excitation, both utilizing the Lorentz force transduction mechanism. A thorough investigation into the metal patch geometry employed in both
configurations is detailed, providing valuable design insights. The numerical model of these Electromagnetic Acoustic Transducer configurations is developed using COMSOL, and simulation results robustly affirm the feasibility of the proposed approach. By successfully extending the applicability of Electromagnetic Acoustic Transducers to non-conductive samples and introducing the innovative embedded Electromagnetic Acoustic Transducer, this research significantly contributes to advancing the field of structural health monitoring and presents a viable nondestructive evaluation approach for the effective detection of damage in engineering structures.
Department: Mechanical Engineering
Name: Mahdieh Tanha
Date Time: Wednesday, December 6th, 2023 - 9:00 a.m.
Advisor: Dr. Brian Feeney
This work is motivated by the undulatory swimming motion of fish, where the fish body is idealized as a mechanical beam, with external forces on the beam are due to fluid-structure interaction, and internal neuromuscular actuation. To this end, the purpose of this thesis is to investigate why a beam with specific properties and excitation can propel inside specific fluids while the same beam in a vacuum cannot propel. In particular, this study investigates whether the fluid-structure interactions in a flow can generate non-synchronicity of body wave, believed to be important in generating thrust, and evaluates the resulting thrust. The study is conducted on two aspects: (1) an investigation into characteristics that lead to thrust and a stable speed, and (2) a study on the influence of the fluid environment on lateral oscillation characteristics compared to the oscillation of the same beam in a vacuum.
The first phase of the thesis is conducted on identifying any relationship between the oscillating beam's slope and the fluid pressure on the beam, as the product equals the thrust distribution along the beam. We focused on the Lighthill force model and the Taylor force model, which are fundamentally different but are well known for this application. We found that non-synchronicity and an appropriate amplitude envelope of the beam's oscillation can lead to thrust production, specifically when the amplitude envelope and its spatial derivative are similar, when there is a single dominant mode which causes a nearly constant phase difference between pressure and body slope.
In the second phase, we seek whether the fluid's effect is naturally conducive to the production of traveling waves in the body. We looked at the transverse damping force of fins, and Taylor and Lighthill models of the fluid force on cylindrical immersed bodies and investigated the effects on the natural modal shapes and frequencies of beam, evaluating the existence of non-synchronicity and a constructive amplitude envelope (to thrust). We found that the resistive nature of the fluid significantly injects damping into the oscillation leading to non-synchronicity of oscillation, a reduced modal frequency, and an amplitude envelope that is a consequence of modal coupling. Application of transverse dampers with suitable damping coefficients and placement on the beam can help to increase these effects. However, the reactive nature of fluid is not seen to inject much damping into the system and does not strongly affect the modal shapes and frequencies unless the propulsion speed is high enough.
We conclude that the presence of fluid surrounding an oscillating beam changes its lateral oscillation properties and creates a pressure field around the beam in a way that can lead to thrust and propulsion with a stable speed if there is appropriate bending beam stiffness, density, dimensions, length, frequency, wavelength, fluid density and damping strength. An earthbound
case of successful propulsion is seen in the swimming of fish as soft beams in a moderately resisting fluid such as water.
Department: Electrical and Computer Engineering
Name: Demetris Coleman
Date Time: Friday, December 1st, 2023 - 3:00 p.m.
Advisor: Xiaobo Tan
Autonomous underwater vehicles have a variety of applications such as environmental monitoring, search and rescue, ocean exploration, and fish tracking. One such class of these vehicles is gliding robotic fish, which realize energy-efficient locomotion and high maneuverability by combining buoyancy-driven gliding and fin-actuated swimming. The goal of this dissertation is to endow gliding robotic fish with advanced control capability and autonomy, to facilitate their ultimate applications in aquatic environments.
First, an overview of the gliding robotic fish platform GRACE is presented and design improvements for the third generation of GRACE are discussed. These include adding Iridium satellite-based communication for remote operation, making the robot more robust for ocean operation, and developing a miniaturized version (Mini-Glider) to enable rapid testing of functionality and control algorithms.
Second, a backstepping-based trajectory tracking controller for the energy-efficient gliding-like motion of gliding robotic fish is proposed. The controller is designed to track the desired pitch angle and reference position in 3D space. In particular, under-actuation is addressed by exploiting the coupled dynamics and introducing a modified error term that combines pitch and horizontal position tracking errors. Two-time-scale analysis of singularly perturbed systems is used to establish the convergence of all tracking errors to a neighborhood around zero. The effectiveness of the proposed control scheme is demonstrated via simulation and experimental results.
Next, incorporating observability into control schemes is discussed. Incorporating observability can enhance an observer's ability to recover accurate estimates of unmeasured states, minimize estimation error, and ultimately, allow the original control objective to be achieved. The use of control barrier functions (CBFs) is proposed to enforce observability and thereby encourage convergence of state estimates to the true state in output feedback control schemes. The proposed approach is compared to a model predictive control (MPC)-based alternative that optimizes a weighted combination of an observability surrogate function and the control objective. Motivated by the applications of fish tracking and navigating in GPS-denied environments, the problem of target tracking, when only the distance to the target is measured, is addressed. It is found that both approaches are comparable in terms of observability and estimation error, but the CBF-based approach has an edge in terms of computational efficiency. Experimental validation of the CBF-based scheme is conducted with a Mini-Glider.
To complete this body of work, a strategy for the exploration of unknown scalar fields under localization uncertainty is proposed. The strategy hinges on the concept of the multi-fidelity Gaussian processes (GPs) and sampling-based motion planning for information gathering. It uses multi-fidelity GPs to approximate the environmental field by assigning location-measurement pairs to a particular fidelity based on the level of uncertainty in the location estimate. An informative trajectory planner is then designed that plans not only where the robot should go, but also what types of motion (e.g. swimming, gliding, etc.) the robot should use to best gather information for the reconstruction of the field.
Experiments are carried out on a Mini-Glider for the task of mapping the light field in an indoor tank. The results show that using a multi-fidelity GP model provides a better reconstruction of the field in terms of the weighted mean squared error when compared to using standard GP regression, where the localization error is ignored.
Department: Mechanical Engineering
Name: Atacan Yucesoy
Date Time: Monday, November 27, 2023 - 3:00 p.m.
Advisor: Thomas Pence and Ricardo Mejia
The Effects of Mechanical Intrinsic Factors Induced by Morphogenesis on Brain Mechanics
The brain soft tissue is subject to large strains due to the shape changes that occur during growth. The process can be viewed as one in which tissue transforms from a locally stress-free reference configuration to a mature state exhibiting large elastic deformations. The biological growth alters the state of stress and leads to residual fields existing in the equilibrium state of the tissue after morphogenesis. Residual stress fields are inhomogeneous and anisotropic. This resulting stress field typically involves compressive and tensile stresses that vary through the material in a complex fashion. Hence, the mechanical response of residually stressed tissues to finite deformations differs from that of stress-free tissues. Furthermore, considering the role of mechanical properties of the tissues on the regulation of the essential behavior of the cellular structure, the residual fields have a potential role in mechanotransduction at the tissue and cellular scale. The residual fields also should be included when seeking to model the micromechanical mechanisms that give rise to brain injury. While the physical mechanisms of acute and secondary injuries led by extreme events (e.g., blunt impact, blast waves, cavitation, etc.) still remain unclear, it does seem clear that residual stresses could have a significant effect. For example, a preexisting tensile residual stress could accelerate the formation of microfissures during an episode of physical trauma, whereas a preexisting compressive residual stress field could provide some benefit in delaying fissure formation. It is issues of this type that motivate much of this research.
Research on residual fields in the cortex is quite limited, despite extensive experimental studies estimating residual stress fields in various tissues. The limited experimental findings generally indicate that gray matter (outer layer) experiences compression, while white matter (inner core) is subject to tension. The findings support the differential growth hypothesis where the gray matter is growing more than the white matter. It should be noted that the experimental insights are limited to specific cutting directions and regions, making a comprehensive assessment of residual stress/strain fields challenging. On the other hand, computational models have been extensively used in order to simulate cortical growth and folding processes to understand various aspects such as folding patterns, developmental abnormalities, underlying growth and folding mechanisms, and the role of physical and material properties on the final morphology. Still, there is a notable scarcity of computational models predicting the mechanical state of brain tissue during cortical growth, particularly including the extended folding regime.
The research work presented in this dissertation concerns the study of morphogenesisinduced residual stress fields in hyperelastic materials and the potential effect of these residual stress fields on the material response. This research is generally based on the non-linear theory of elasticity.
To address the effect of the residual stress field on the material response, the solution of a finite deformation boundary value problem for a residually stressed elastic spherical shell subject to pressure inflation is first provided. To this end, the general constitutive equation for an isotropic Mooney-Rivlin type of hyperelastic material with a background residual stress field is derived. Four residual stress fields with distinct levels of strength are considered. The problem is then expressed as a compact integral expression including the base response of the material and the response arising from the presence of the residual stress field. An asymptotic analysis is conducted to examine the dependence of residual-stress integrals on a dimensionless measure of radial strain. The results are compared with the base response of the Mooney-Rivlin type material to pressure inflation and the potential effect of the residual stress field on the material response is discussed. The numerical analysis shows that the residual stress fields have the potential to alter the qualitative behavior of the pressure-inflation response of the material.
While the just described analysis was quite general for residual stress fields that could arise from a variety of causes, the work then proceeds with the examination of residual stress due to differential growth in adjoining tissue in incompressible isotropic hyperelastic single and bilayer spherical shells. The kinematics of differential volumetric growth utilizing the incompressible hyperelastic framework are presented for each geometry considered, and the growth-induced residual stress fields are computed for five different growth conditions: area, surface, isotropic, and the combination of area and surface growth. Then, the sensitivity of the resultant stress field to the differential growth in adjoining layers is examined for the combination of the five growth conditions. In this analysis, the spherical symmetry is preserved during the growth. To address the residual stress fields generated by the morphogenesis including symmetry-breaking bifurcation and beyond, the study later continues by building an elementary computational model with idealized geometry, boundary conditions, and parameters. This 2D plane strain computational model provides the residual stress/strain fields emerging in a formation resembling sulcus-gyrus structure in a gyrified brain. In the finite element model, an initially flat bilayer rectangular model is utilized, which consists of a relatively stiff outer layer (cortex) and the inner core (subcortex). Following the differential growth hypothesis, the residual stress and strain fields are computed for the domain where the cortex undergoes only tangential (in-plane) growth while the subcortex does not experience any growth. A detailed stress and strain analysis of the resultant sulcus-gyrus formation is performed to understand morphogenesis-induced residual fields specifically for the sulcal floor and gyral crown. The computational results are consistent with previous experimental findings.
Due to the specific attention to physical injuries leading to the neuropathies such as Chronic traumatic encephalopathy (CTE) seen in the depth of the sulcus, the analysis is extended to encompass the response of non-residually stressed sulci subjected to intrasulcal deformations. A 2D plane strain computational model of a single sulcus is built to examine the deformations associated with the expansion of a cavitation bubble in the intrasulcal region. Based on the previously obtained experimental data, the quasi-static and transient pressure loading conditions are implemented to the gray matter-cerebrospinal fluid (CSF) boundary, and the response of the sulcus is investigated in detail. The findings demonstrate that cavitation result in sulcal expansion and the formation of localized high strain and strain rates at the depth of sulci. The strain and strain rate localization regions resemble the tauopathy / neurofibrillary tangles patterns seen in early CTE.
Department: Computer Science and Engineering
Name: Li Liu
Date Time: Monday, November 27th, 2023 - 11:00 a.m.
Advisor: Zhichao Cao
Low-power Artificial Intelligence of Things(AIoT) Systems
Internet-of-Things (IoT) is another excellent innovation after the Internet and mobile networks in the information era, aiming at connecting billions of end-devices across scales. A multitude of IoT applications often operate under conditions of constrained energy resources, which has rendered low-power IoT systems a subject of considerable research interest. The increasing need for AI in complex scenario-based composite tasks has led to the rise of Artificial Intelligence of Things(AIoT), which encompasses research in two major directions: AI for IoT that solves problems in IoT systems with AI techniques and IoT for AI that adopts IoT infrastructure/data to advance the development of AI models. While AIoT systems in low-power scenarios offer significant benefits, they also face specific challenges that are inherent to their design and operational requirements.
This dissertation delves into low-power AIoT from both angles. 1) We endeavor to harness the capabilities of AI to predict and analyze the communication channels of dynamic long links in LoRaWAN, which is one of the Low-power Wide-area Networks(LPWANs). DeepLoRa adopts Deep Neural Networks based on Bi-directional LSTM(Long-Short-Time-Memory) to capture the sequential information of environmental influence on LoRa link performances for accurate LoRa link path-loss estimation. It reduces the path-loss estimation error to less than 4 dB, which is 2x smaller than state-of-the-art models. LoSee extends the contributions of DeepLoRa. It measures the real-world fine-grained performance, including detailed coverage study and feasibility analysis of fingerprint-based localization, of a self-deployed LoRaWAN system with temporal dynamics and spatial dynamics. 2) We design energy-efficient IoT systems that facilitate the deployment of AI models for practical applications. FaceTouch enables accurate face touch detection with a multimodal wearable system consisting of an inertial sensor on the wrist and a novel vibration sensor on the finger. We leverage a cascading classification model, including simple filters and a DNN, to significantly extend the battery life while keeping a high recall. FaceTouch achieves a 93.5% F-1 score and can continuously detect face-touch events for 79 – 273 days using a small 400 mWh battery, depending on usage.
In general, this dissertation studies both theoretical and practical aspects in the field of low-power AIoT systems, including LoRaWAN link behavior analysis and building practical wearable systems. These advancements not only underscore the feasibility of deploying low-power AIoT in real-world settings but also pave the way for future research and development in this domain, aiming to bridge the gap between IoT and AI for the creation of smarter, sustainable, and more efficient technologies.
Department: Electrical and Engineering
Name: Hasanur R. Chowdhury
Date Time: Tuesday, November 21, 2023 - 10:00 a.m.
Advisor: Ming Han
High accuracy temperature and strain measurements are prerequisites for many modern industries to ensure safety, improve efficiency, and reduce greenhouse gas emissions. Traditionalthermocouples or electronic devices often encounter challenges in temperature and strain measurement due to cross-sensitivity to surrounding perturbations, sensor’s drift at elevated temperature, or susceptibility to electromagnetic interference (EMI). To overcome these, fiberoptic sensors have gained popularity due to their unique advantages, including small size, multiplexing capacity, and immunity to EMI. In this work, we reported a novel approach to measure temperature using fiber optic Fabry-Pérot (FP) interferometer, which eliminates cross strain sensitivity, shows linearity at high temperature, and provides high accuracy for a broad range. In addition, we developed another sensor for simultaneous measurement of temperature and strain using cascaded fiber Bragg grating (FBG)- silicon FP interferometer configuration.
Our proposed temperature measurement method is based on an air-filled FP cavity, whose spectral notches shift due to a precise pressure variation in the cavity. For fabrication, a fused silica tube is spliced with a single mode fiber at one end and a side-hole fiber at the other to form the FP cavity. The pressure in the cavity can be changed by passing air through the side-hole fiber causing the spectral shift, which is the measurand of temperature. We have developed two novel approaches based on this setup. The first approach employs two pressure values, their corresponding interferometric valley wavelengths, and the gas material’s constant (ɑ) to obtain temperature. A computer-controlled pressure calibration and sensor interrogation system has been developed with miniaturized instruments for this sensor operation. Experimental results show that the sensor has a high wavelength resolution (<0.2 pm) for minimal pressure fluctuation (2.5×10-3 psi) up to a broad temperature range (over 800 ℃). We analyzed the effect of wavelength noise and pressure fluctuation on temperature resolution, which reveals that our developed system can obtain a high resolution (±0.32 ℃) temperature measurement. The use of gas as the sensing material and the measurement mechanism also implies long-term stability and eliminates the cross sensitivity to strain.
In the second approach, we used a pair of FP cavities filled with gas of identical but variable pressure. One of the FPs (reference FP) is placed in the cold zone with a known temperature. The temperature of the measuring FP can be deduced by the spectral fringe shift vs. pressure of the two FPs. This method does not require measurement of the pressure or the knowledge of the optical properties of the gas. Hence it facilitates to make the instrumentation simpler and cost-effective and data acquisition faster. We have verified this method experimentally up to 800 ℃. The sensor shows good linearity in the range. Long-term test conducted at 800 ℃ exhibited the stability of the sensor with fluctuations of ≤0.3% over a duration exceeding 100 hours. In addition to these air-filled FP interferometers, we have presented another novel sensor based on cascaded fiber Bragg grating (FBG)- silicon FP interferometer (FPI) for simultaneous measurement of temperature and strain. The sensor is composed of a 5 mm grating on a single mode fiber and a 100 µm silicon tip attached to the end of it by UV curable glue. The silicon tip is unbonded, and free from strain whereas the FBG is attached to the host structure. The sensor is tested from room temperature to 100 ℃ with varying strain up to ∼150 µε. The silicon FPI provides high temperature sensitivity of 89 pm/℃ unaffected by strain. On the contrary, the FBG is affected by both thermal and mechanical strain; the sensitivity of these are experimentally obtained as 32 pm/℃ and 1.09 pm/µε, respectively. With a high-speed spectrometer, the temperature and strain resolution of the FPI and FBG are found ±1.9×10-3 ℃ and ±0.042 µε, respectively. Due to the small size, enhanced sensitivity and high resolution, this cascaded FBGFPI sensor can be used in practical applications where accurate measurement of temperature and strain are required.
Department: Electrical and Engineering
Name: Zi Li
Date Time: Tuesday, November 21, 2023 - 10:00 a.m.
Advisor: Yiming Deng
Even after extensive efforts to enhance our understanding of materials, modeling, and system processes, uncertainty continues to be an inevitable factor that impacts system behavior, especially at the operational limits. The evaluation of uncertainty is now a common practice in engineering and scientific fields, encompassing the analysis of experimental data, as well as numerous computational models and process simulations. Non-destructive evaluation (NDE) techniques are widely utilized across a range of industries and applications to guarantee the safety, quality, and dependability of components, systems, and structures. However, NDE processes are often challenged by uncertainties stemming from factors such as material variations, environmental conditions, and measurement limitations, which can introduce complexities into the assessment process. Therefore, there is a need to quantify uncertainties in NDE, which can enhance our comprehension of the constraints and potential inaccuracies linked to NDE inspections and aid in making NDE assessments more robust and reliable. In this thesis, a comprehensive uncertainty quantification (UQ) framework: the Three-Legged Stool (TLS) is proposed to provide systematic guidance in uncertainty analysis for NDE applications.
A Magnetic Flux Leakage-based defect characterization algorithm is proposed to classify the defect and handling uncertainties for pipeline inspection. The research compares Convolutional Neural Network (CNN) and Deep Ensemble (DE) methods for handling input uncertainties from MFL response data, while also employing Autoencoder for data augmentation to address limited experimental data. The study evaluates prediction accuracy and explores uncertainty analysis, emphasizing the importance of reliability assessment in MFL-based NDE decision-making.
To estimate the fatigue life of martensitic-grade stainless-steel turbine blades, a magnetic Barkhausen noise (MBN) technique is applied. This work involves the extraction of time and frequency domain features, followed by the application of techniques such as Principal Component Analysis (PCA) and probabilistic neural network (PNN) for classifying and estimating the remaining fatigue life.
An IMU-assisted robotic SL sensing system was developed for pipeline detection. This system improves registration and defect estimation through a RANSAC assisted cylindrical fitting algorithm, integrates inertial and odometry measurements for precise 3D profiling, and employs customized defect sizing techniques to offer a reliable 3D defect reconstruction solution for various defect shapes and depths.
The proposed TLS-based UQ framework highlights the interdependent dynamics among data, models, and learning when addressing uncertainties in NDE processes. Some advanced and commonly used techniques have been introduced to illustrate how uncertainties in the inputs or parameters of an NDE system, model, or measurement are propagated to the outputs or predictions. The uncertainty propagation is considered in terms of the forward modeling and inverse learning process separately. In order to demonstrate the efficiency and applicability of the proposed framework for NDE applications, the uncertainties in the previously mentioned NDE cases are investigated and quantified using the techniques outlined in the TLS model.
In summary, the proposed UQ framework is able to provide guidance in dealing with uncertainties in NDE inspection with efficient and reliable solutions. It holds great promise and opens up avenues for further research and advancement within the industry.
Department: Electrical and Computer Engineering
Name: Pengyu Chu
Date Time: November 17, 2023 - 11:00 a.m.
Advisor: Zhaojian Li
ROBUST FRUIT DETECTION AND LOCALIZATION FOR ROBOTIC HARVESTING
Automated apple harvesting has attracted significant research interest in recent years due to its potential to revolutionize the apple industry, addressing the issues of shortage and high costs in labor. One key enabling technology towards automated harvesting is robust apple detection and localization, which poses great challenges because of the complex orchard environment that involves varying lighting conditions and foliage/branch occlusions. In this dissertation, we first propose a suppression Mask RCNN to generally improve the accuracy for apple detection. Our developed feature suppression network significantly reduces false detection by filtering non-apple features learned from the feature learning backbone. At the same time, we propose a novel deep learning-based object detection method Occluder-Occludee Relational Network (O2RNet), which addresses the challenge of detecting and isolating clustered apples in apple orchards. Previous object detection techniques have exhibited l imited success in handling fruit occlusion and clustering, which are common issues in agricultural settings. To overcome these challenges, O2RNet employs a two-stage approach. In the first stage, we use a custom deep Feature Pyramid Network (FPN) architecture to generate candidate regions of interest (ROIs) for potential fruit objects. The second stage feeds these candidate ROIs into the occluder branch and occludee branch respectively using a feature expansion structure (FES). By leveraging this two-stage approach, O2RNet can effectively isolate individual apples from clustered regions, thereby facilitating accurate apple detection.
Then, we propose Active Laser-Camera Scanning (ALACS) to achieve a high-precision 3D localization of detected apples and overcome existing localization challenges like varying illumination conditions, complex occlusion scenarios, and limited geometric information. The hardware of ALACS includes a red line laser, an RGB camera, and a linear motion slide. All these components are seamlessly integrated for fruit localization by using an active scanning scheme and laser-triangulation technique. The technique integrates semantic information from O2RNet's detection results with bounding boxes to generate accurate 3D coordinates for each detected apple.
Additionally, we propose Skeleton-lead Segmentation Network (SkeSegNet) and introduce it to the Panoptic-Deeplab. SkeSegNet is used to address the challenges of segmenting complex branches by treating branches as a combination of skeletons. Combined with depth map, SkeSegNet generates 3D branches for efficient obstacle avoidance.
Lastly, we evaluate each approach in the comprehensive experiments and superior experimental results demonstrated the effectiveness of the proposed approaches.
Persons with disabilities have the right to request and receive reasonable accommodation. Please call the Department of Electrical and Computer Engineering at 355-5066 at least one day prior to the seminar; requests received after this date will be met when possible.
Department: Computer Science & Engineering
Name: Mehmet Cagri Kaymak
Date Time: November 16th, 2023 - 10:00am
Advisor: Hasan Metin Aktulga
Molecular dynamics (MD) is a powerful computational method used to simulate the motion of atoms and molecules. MD simulations compute the evolution of a system of interacting particles by applying Newton’s equations of motion, facilitating the study of a range of physical, chemical, and biological phenomena. While quantum mechanical (QM) simulations result in accurate predictions of geometries and energies essential for studying various phenomena, the computational complexity has led to the emergence of new approaches such as classical force fields, reactive force fields, and machine learning potentials (MLPs), each offering unique trade-offs. Classical force fields offer longer simulation times due to assumptions such as static bonds and charges, which prohibit the study of reactive systems. Reactive force fields, such as ReaxFF, bridge the gap between QM methods and classical force fields by allowing dynamic bonds and charges. The improved flexibility results in a higher computat ional load and a more complex functional form that is hand-crafted by domain experts. MLPs are a more recent approach that utilize large datasets to eliminate complex functional forms, while also leveraging the vast ecosystem of machine learning frameworks for enhanced computational efficiency and ease of development.
As the number of methodologies increases, the landscape of MD methods becomes more complex, with each method bringing unique attributes and challenges in simulating molecular systems. We introduce innovative hybridization techniques aiming to leverage the strengths of multiple modeling approaches, improving predictive capabilities and computational efficiency. We introduce a hybrid modeling approach called ReaxFF/AMBER that combines the reactivity and polarization capabilities of ReaxFF with the efficiency of classical force fields, facilitating the simulation of larger reactive regions. Although ReaxFF can offer high fidelity when trained carefully, the existing parameterization tools lack the efficiency and speed essential for creating new ReaxFF parameter sets for different applications of interest. We have proposed a novel parameter optimization approach, JAX-ReaxFF, leveraging the capabilities of a scalable machine learning framework to drastically reduce the training times for ReaxFF, thus enhancing the development of new force fields for various applications. We have also modified JAX-ReaxFF to run end-to-end differentiable simulations on different architectures such as CPUs, GPUs, or TPUs with the help of JAX. JAX is a library known for high-performance numerical computing and it provides features such as automatic differentiation and optimization of Python functions. This approach also allows for improved integration with existing machine learning software infrastructure, offering enhanced flexibility and performance portability.
Lastly, we propose and compare various uncertainty quantification (UQ) methods suitable for MLPs. These methods are essential for active learning-based data generation approaches, which are crucial for training data-intensive machine learning models. While our primary focus is on MLPs, the datasets created using active learning methods could also enhance the parameterization efforts for classical and reactive force fields.
Department: Civil & Environmental Engineering
Name: Soham Vanage
Date Time: November 15, 2023 - 3:30 pm
Advisor: Kristen Cetin
IMPROVING ENERGY USE, DEMAND AND VISUAL COMFORT IN COMMERICAL BUILDINGS USING LIGHTING AND SHADING CONTROLS
Windows provide occupants with natural light and a view of the outside, enhancing productivity, which is important as people spend approximately 90% of their time indoors. This is especially the case during and after the COVID-19 pandemic. Automated controls for window shading systems can be used to control solar radiation and daylight entering the space. Lighting controls can reduce lighting requirements, providing energy savings and better visual comfort for occupants than manual controls, which are seldom used effectively.
Past studies have explored automated lighting and shading control strategies, and reported energy savings and visual comfort improvements over their baselines. However, the assumptions for baseline models differ across different studies, making it difficult to compare these automated controls. Thus, this research uses a multi-step modeling process, including daylighting and energy simulations using RADIANCE and EnergyPlus, respectively (i) to compare existing control strategies using the same building inputs (baseline model) for a prototypical small office building, (ii) to develop and evaluate the effectiveness of a novel integrated control strategy that uses variables such as occupancy, HVAC state, solar radiation entering the space, time of day for control, and others variables. (iii) to develop a parametric model to investgate the impact of different input variables such as building form factor, window-to-wall ratio for all different orientations, shade properties such as opennes s factor, and shade overhang depth on energy performance and visual comfort.
On top of improving energy efficiency and visual comfort in buildings, managing demand at the grid level is becoming more important as renewable energy gets added to the generation mix. Instead of adding more generation to balance the grid, usually using new fossil fuel-based generation, the other approach to balance the grid is to use existing building loads and reduce their demand during specific hours (also known as demand-side Flexibility Services (FS)). As buildings become smarter with the adoption of new technologies for sensing and control, more integration between buildings and the electric grid is possible. Building loads such as air conditioning and lighting in commercial buildings have the potential to provide demand-side FS. In particular, demand-side flexibility using lighting loads is not well studied in the literature. In commercial buildings, lighting accounts for approximately 10-15% of the load at any time. Past studies have shown that lighting can be dimmed by 15-2 0% without causing visual discomfort to the occupants. The forth objective thus of theis study (iv) if to improve the existing literature by providing building level and grid level estimates for using lighting loads for all the common commercial building types as demand-side Flexible Services (FS) for three future scenarios in the Midwest region.
Department: Computer Science & Engineering
Name: Wentao Wang
Date Time: November 15th, 2023 - 1:00pm
Advisor: Jiliang Tang
As a prominent component of artificial intelligence (AI), machine learning (ML) techniques play a significant role in the stunning achievement obtained by AI technologies in human society. ML techniques enable computers to leverage collected data to tackle various kinds of tasks in practice. However, more and more studies reveal that the capability of a ML model will be decreased dramatically if the distribution of collected data used for training this model is imbalanced. As imbalanced data distribution is widespread in many real-world applications, improving the performance of ML models under imbalanced data distribution has attracted considerable attention.
While a growing number of related works have been proposed to make ML models learn from imbalanced data more effectively, the study on this topic is far from complete. In this thesis, we propose several studies to fill up the gaps in this direction. First, most existing data generation based works only consider the local distribution information within classes, while the global distribution is totally ignored. We demonstrate both global and local distribution information are important for producing high-quality synthetic data samples to balance the data distribution. Second, almost all existing studies assume that collected data samples are associated with noisy-free labels, and, hence, they cannot work well when annotated labels are noisy. We investigate the problem of learning from imbalanced crowdsourced labeled data and propose a novel framework as a solution with satisfactory performance. Third, currently the research on investigating the impact of imbalanced data distribution o n the robustness of ML models is rather limited. To this end, we empirically verify the adversarial training (AT) approach alone cannot bring enough robustness for ML models under imbalanced scenarios while integrating the reweighting strategy with AT can be very helpful. In addition, we also propose an effective data augmentation based framework to benefit AT under imbalanced scenarios.
Department: Mechanical Engineering
Name: Lingyun Hua
Date Time: Wednesday, November 15, 2023 at 1:00 p.m.
Advisor: Guoming Zhu
ABSTRACT ECONOMIC ROUTE-SPEED OPTIMIZATION AND CONTROLS FOR CONNECTED ELECTRIC VEHICLES
This dissertation focuses on reducing vehicle energy consumption using optimal control and real-time optimization based on vehicle connectivity. The proposed methods include optimal vehicle transient motion control and eco (economic) route planning, where vehicle route and speed are optimized based on a proposed data-driven Grey-Box model considering vehicle speed, and its driving environment such as temperature, road grade, gust wind, etc. The two methods, optimal transient control and eco route planning form the whole route and speed optimization system. Vehicle transient motion control plays an important role in reducing energy consumption for hybrid and electric vehicles, as well as vehicles powered by internal combustion engines since vehicle acceleration and deceleration can be optimized based on the driving environment.
In this thesis, nonlinear quadratic tracking (NQT) control is used for optimal acceleration and minimal principle for deceleration to optimize energy recovery, where the acceleration control generates the optimal propulsion torque based on the current powertrain states and the error between vehicle speed and given reference provided by the connected system based on the surrounding traffic; and the deceleration (braking) control optimizes regenerative brake to maximize the recovered energy while obeying speed and braking distance constraints. Both control strategies are designed for real-time applications and can be updated online to respond to the rapid change in traffic environment using analytic solutions of optimal control. Computer-in-the-loop (CIL) and Hardware-in-the-loop (HIL) simulations validate their adaptability to reduce energy consumption and update to a changing traffic environment in real-time.
Considering the various system disturbances (e.g., road grade, gust wind, etc.) occurring during drive and model uncertainties due to model simplification and parameter errors ignored by the optimal controls above. In this thesis, a linear quadratic integral tracking (LQIT) control is utilized to generate regulation laws for both acceleration and deceleration operations to reduce tracking error. The LQIT acceleration control tracks the reference speed trajectory generated by the optimal acceleration strategy with minimal tracking error; and the LQIT deceleration control tracks the brake distance reference from the optimal braking control, achieves the target speed, and keeps brake distance below its reference for safety concerns. A unified Kalman filter is used to estimate system state based on noisy measurement. Simulation studies validate the proposed LQIT controls and indicate that the static tracking errors for both speed and distance are reduced with confirmation of being able to handle changing traffic environments.
To perform the vehicle eco route and speed optimization, a vehicle energy consumption model is necessary to estimate the energy usage. In this thesis, a Grey-Box vehicle energy
consumption model is developed based on vehicle dynamics with environmental influence based on the Kriging model. This model retains its high fidelity by utilizing basic vehicle dynamics in the model structure including rolling resistance, aerodynamics, gravity and energy consumption of air conditioning (AC) and heater as functions of environmental conditions such as temperature, wind speed, etc. The proposed data-driven model is trained based on Gaussian process assumption with a modeling error below 2.5\%. After the real-time model is trained, Recursive Least-Squares (RLS) algorithm is used to update the model based on new driving data to reflect the current vehicle status such as aging. The accuracy of the proposed Gray-Box Kriging model is verified in CIL simulation and a case study on vehicle route shows the capability of reducing energy consumption by using the Grey-Box model with changing environments.
Based on the developed Grey-Box energy model, a novel vehicle eco motion planning (VEMP) method is proposed to optimize the vehicle route and speed simultaneously for minimizing its energy usage with a given origin-destination pair and a travel time limit. The proposed VEMP method is based on the modified Dijkstra algorithm and gradient descent speed optimization to find and update the optimal route and corresponding speed profile in real-time based on the changing traffic and driving environment information. Co-simulation studies are conducted for the developed VEMP method in MATLAB with the SUMO traffic model using a real-world map. The simulation results show that for studied driving environments, the VEMP speed optimization is able to reduce energy usage, and results of five scenarios indicate that the VEMP can reduce total energy consumption. A sudden traffic jam study demonstrates the ability of real-time updating for the proposed VEMP method to handle sudden traffic changes such as vehicle cut-in.
Department: Civil & Environmental Engineering
Name: Saeed Memari
Date Time: November 15, 2023 - 11:30 am
Advisor: Phanikumar Mantha
COMBINING REMOTE SENSING, MACHINE-LEARNING AND MECHANISTIC MODELING TO IMPROVE COASTAL HYDRODYNAMIC AND WATER QUALITY MODELING IN THE LAURENTIAN GREAT LAKES
Large lakes often serve as early indicators of shifts in the environment. Observations within the Great Lakes ecosystems continue to highlight a deterioration in water quality, a surge in algal bloom occurrences, and growing threats to indigenous species. Given the inherent complex dynamics of these inland seas and the growing environmental pressures, it is important to understand shifts in the intricate process dynamics governing these systems. Hydrodynamics and temperature, in particular, are fundamental variables that play significant roles as they influence multiple physical, chemical, and biological processes taking place within the lakes and their coastal areas. The goal of this study is to improve coastal hydrodynamic and water quality models of the Laurentian Great Lakes. Extensive field datasets were collected in Lake Huron and Lake Erie over multiple years focusing on diverse factors affecting coastal processes including the roles of oscillating, bidirectional exchange flow s between Lake Michigan and Lake Huron at the Straits of Mackinac, groundwater upwelling and submerged sinkholes in bays of Lake Huron, and contaminant plumes originating from rivers draining into the lakes. The performance of the models heavily depends on the quality of boundary forcing data and how the domain is discretized. Thus, a systematic assessment was done to improve the models by improving domain discretization through depth-adaptive triangular meshes and nested-grid methods. Additionally, detailed meteorological forcing fields were created with reanalysis and in-situ datasets. High-resolution time series data for water quality variables were generated using machine learning models since traditional monitoring data are notorious for their low temporal resolution, especially for microbiological water quality. The accuracy and performance of the models were tested against in-situ observations. This encompassed data on currents, lake levels, water temperature, and water quali ty variables (turbidity and Escherichia coli concentrations). High-resolution remote sensing imagery was also incorporated for a comprehensive evaluation of spatial plume dynamics. Novel insights from this research include an understanding of the crucial role played by the exchange flows in the Straits of Mackinac on transport timescales and biophysical processes in the bays of Lake Huron. The exchange flows significantly influence regions as far down as 50-70 km from the Straits changing, among other things, bottom currents, which have important implications for biogeochemical processes, including the resuspension of bottom sediment, nutrient availability (e.g., nitrogen and phosphorus), and the growth and sloughing events of benthic algae such as Cladophora. Observed vertical velocities close to the lake bed in Thunder Bay, Lake Huron were found to be an order of magnitude higher compared to simulated vertical velocities of the same system using models that did not explicitly acco unt for groundwater inflow from the karst lake bed. Models and data were used to estimate the upwelling groundwater flux and to quantify the impacts of ignoring groundwater in this system. The study highlights the significant benefits of merging best-available techniques and a fusion of mechanistic modeling, machine learning, and remote sensing to push the envelope of model performance in the context of the Great Lakes. This research is expected to aid management efforts aimed at enhancing and preserving the resilience of coastal regions.
Department: Computer Science and Engineering
Name: Manni Liu
Date Time: November 15th, 2023 - 11:00am
Advisor: Zhichao Cao
LOCALIZATION AND SECURITY: PUSH THE LIMIT OF IOT SYSTEM DESIGN
Internet of Things (IoT) utilizes sensors as the information source of Machine Intelligence. Its applications widely exist from Smart Home, Smart City to Wearable Healthcare and Smart Farming. An IoT architecture usually covers four stages: sensor data connection, data transmission, data processing and application model. On top of prediction precision, the interest of IoT research also includes improved efficiency, cost saving and system scalability.
In pursuit of these goals, we push the limit of IoT system design from the following three perspectives. (1) We exploit the potential of sensors of smart devices, including sensor fusion and possibility of new IoT functions. (2) We design Machine Learning models for IoT applications, including feature engineering and model selection. (3) We design and implement lightweight IoT systems for smart devices like laptops, smartphones and smart assistants, under the constraint of computation resource.
In this dissertation, we especially introduce our effort to IoT applications on localization and security. EyeLoc is a smartphone vision enabled localization system designed for large shopping malls. The results show that the 90-percentile errors of localization and heading direction are 5.97 m and 20° in a 70,000 m² mall. Patronus protects acoustic privacy from malicious secret audio recordings using the nonlinear effect of microphones. Our experiments show that only 19.7% of words protected by Patronus can be recognized by unauthorized recorders. SoundFlower is a sound source localization system for voice assistants. It can locate a user in 3D space through the wake-up command with a median error of 0.45 m.
In general, we explore the potential of diverse sensors to IoT services and build machine learning models to exploit the most information from sensor data. The applications we study are specifically about localization and security.
Department: Biomedical Engineering
Name: Cort Thompson
Date Time: November 7, 2023 - 3:00 PM
Advisor: Erin Purcell
A SYSTEMATIC CHARACTERIZATION OF THE TISSUE RESPONSE IN THE BRAIN TO IMPLANTED ELECTRODE ARRAYS
Intracortical brain interfaces are an ever-evolving technology with growing potential for clinical and research applications. However, the tissue response to implanted devices can limit their functional longevity. The chronic tissue response to these devices has been typically characterized by glial scarring, inflammation, oxidative stress, neuronal loss, and blood brain barrier disruptions. To ameliorate or circumvent the tissue response, numerous next generation electrode which feature various biomaterials and novel designs have been developed with some success. However, recordable neuronal signals can still decline in apparently healthy tissue which present with minor glial scarring and normal neuronal densities. Therefore, it is essential that we better understand the tissue response to inform and guide the design of cortical implants with greater biocompatibility. Recent RNA seq datasets have identified hundreds of gene associated with gliosis, neuronal function, myelination, a nd cellular metabolism which are spatiotemporally expressed in neural tissues following the insertion of microelectrodes. There is also evidence to suggest that these differentially expressed genes may be spatiotemporally expressed at the protein level across multiple cell types in the cortical environment. These new understandings of the broader tissue response at the transcriptional level may now allow for more targeted interrogation of the biological pathways involved in the tissue response. By understanding the tissue response of the brain, it may be more possible than ever to precisely identify the biological mechanisms that impact device performance and guide the creation of more biocompatible neural interfaces.
Department: Mechanical Engineering
Name: Aakash Gupta
Date Time: Tuesday, November 7, 2023 - 10:00 a.m.
Advisor: Wei-Che Tai
THE INERTER PENDULUM VIBRATION ABSORBER: WITH APPLICATIONS IN OCEAN WAVE ENERGY CONVERSION AND HYDRODYNAMIC RESPONSE SUPPRESSION
The annual power incident on the ocean-facing coastlines of North America is over 400 GW. Capturing a small fraction of this energy can significantly contribute to meeting energy demands. Therefore, there is a renewed research interest in converting energy from ocean waves. Typically, ocean wave energy capturing devices, known as wave energy converters (WECs), are placed in deep water as the wave energy is higher in the deep water compared to shallow water. To reduce the cost of installing and maintaining WECs in deep water, they can be integrated with existing offshore floating platforms in the ocean. For such integration, traditional WECs, operating on the principle of linear resonance, have a natural period in heave close to a typical wave period to generate a large heave resonant response and hence high-efficiency wave power production, which causes large platform motions. In other words, wave power production and
hydrodynamic stability of the platform are conflicting objectives in traditional linear WECs. Therefore, simultaneous wave energy conversion and response suppression of the platform is necessary. To address this issue, in this work, a device known as an inerter pendulum vibration absorber (IPVA) is proposed combining the inerter with a parametrically excited centrifugal pendulum.
Two system variations are studied: the IPVA and IPVA-PTO, marking the absence and presence of an electromagnetic power take-off (PTO) system. Both the IPVA and the IPVA-PTO are integrated with a single-degree-of-freedom (sdof) structure: a primary mass, and a spar, respectively. The efficacy in suppressing vibrations is studied in the case of the sdof IPVA system, whereas wave energy conversion and response suppression are analyzed for the spar IPVA-PTO. For both systems, a nonlinear energy transfer phenomenon in which the energy is transferred between the primary mass (or spar) and the pendulum vibration absorber. For the sdof IPVA system, it is shown that the energy transfer is associated with the 1:2 internal resonance of the pendulum induced by a period-doubling bifurcation. A perturbation analysis shows that a pitchfork bifurcation and a period-doubling bifurcation are necessary and sufficient conditions for this internal resonance to occur. Harmonic balance analysis, in conjunction with Floquet theory, along with the arc-length continuation scheme, is used to predict the boundary of internal resonance in the parameter space and verify the perturbation analysis. Furthermore, the effects of various system parameters on the boundary are examined. Next, the sdof IPVA is compared with a linear benchmark and an autoparametric vibration absorber and shows more efficacious vibration suppression. For the spar IPVA-PTO system, a similar analysis shows the nonlinear energy transfer, which is used to convert the vibrations of the spar into electricity while reducing its hydrodynamic response. Similar to the IPVA, a period-doubling bifurcation results in 1:2 internal resonance, which is necessary and sufficient for nonlinear energy transfer to occur. The hydrodynamic coefficients of the spar are computed using a commercial boundary element method code. The period-doubling bifurcation is studied using the harmonic balance method. A modified alternating frequency/time (AFT) approach is developed to compute the Jacobian matrix involving nonlinear inertial effects of the IPVA-PTO system. The response amplitude operator (RAO) in heave and the capture width of the spar IPVA-PTO are compared with its linear counterpart, and the spar IPVA-PTO system outperforms the linear energy
harvester with a lower RAO and higher capture width. Experiments containing integration of the IPVA and the IPVA-PTO system with an sdof system (or ``dry" spar in the case of IPVA-PTO) are performed in order to verify the analysis.
Next, both the IPVA and the IPVA-PTO systems are integrated with a spar-floater combination and analyzed for their performance. Near the first resonance frequency, the sparfloater IPVA system shows a period-doubling bifurcation and energy transfer similar to the sdof IPVA system and outperforms the linear benchmark for hydrodynamic response suppression. On the other hand, the spar-floater integrated IPVA-PTO system is analyzed for its performance near both resonance frequencies. It is shown that near the first resonance, the spar-floater IPVA-PTO system's response undergoes a period-doubling bifurcation, and for small electrical damping, shows energy transfer. However, near the second resonance, secondary Hopf bifurcation is observed. A rich set of pendulum responses, such as primary and secondary harmonics, quasiperiodic, non-periodic, and rotation, are observed. Rotation provides the best energy conversion among all the identified responses. Finally, the electrical damping of the system is varied to find the optimal values for which the largest energy conversion occurs in the system, and it is found that the optimal electrical damping for energy transfer is associated with the pendulum's rotation.
Persons with disabilities have the right to request and receive reasonable accommodation. Please call the Department of Mechanical Engineering at 355-5131 at least one day prior to the
seminar; requests received after this date will be met when possible.
Department:
Electrical and Computer Engineering
Name:
Piyush Gupta
Date Time:
Monday, October 26, 2023 - 10:00am
Location:
EB Room 1420
Announcement:
ABSTRACT
Advisor: Dr. Vaibhav Srivastava
Human-in-the-loop systems play a pivotal role in numerous safety-critical applications, ensuring both safety and efficiency in complex operational environments. However, these systems face a significant challenge stemming from the inherent variability in human performance, influenced by factors such as workload, fatigue, task learning, expertise, and individual differences. Therefore, effective management of human cognitive resources is paramount in designing efficient human-in-the-loop systems.
To address this challenge, it is critical to design robust and adaptive systems capable of continuously adapting models of human performance, and subsequently providing tailored feedback to enhance it. Effective feedback mechanisms play a pivotal role in improving the overall system performance by optimizing human workload, fostering skill development, and facilitating efficient collaboration among individuals within diverse human teams, each with their unique skill sets and expertise.
In this dissertation, the primary focus lies in exploring optimal and game-theoretic approaches for feedback design to enhance system performance, particularly in scenarios where humans are integral components. We begin by studying the problem of optimal fidelity selection for a human operator servicing a stream of homogeneous tasks, where fidelity refers to the degree of exactness and precision while servicing the task. Initially, we assume a known human service time distribution model, later relaxing this assumption. We design a human decision support system that recommends optimal fidelity levels based on the operator’s cognitive state and queue length. We evaluate our methods through human experiments involving participants conducting underwater mine searches.
We extend the optimal fidelity selection problem by incorporating uncertainty into the human service-time distribution. This extension involves the development of a robust and adaptive framework that accurately learns the human service-time model and adapts the policy while ensuring robustness under model uncertainty. However, a major challenge in designing adaptive and robust systems arises from the conflicting objectives of exploration androbustness. To mitigate system uncertainty, an agent must explore high-uncertainty state space regions, while robust policy optimization seeks to avoid these regions for worst-case performance. To address this trade-off, we introduce an efficient Deterministic Sequencing of Exploration and Exploitation (DSEE) algorithm for model-based reinforcement learning. DSEE interleaves exploration and exploitation epochs with increasing lengths, resulting in sub-linear cumulative regret growth over time.
In addition to cognitive resource management, enhancing human performance can also be achieved through task learning and skill development. In this context, we study the impact of evaluative feedback on human learning in sequential decision-making tasks. We conducted experiments on Amazon Mechanical Turk, where participants engaged with the Tower of Hanoi puzzle and received AI-generated feedback during their problem-solving. We examined how this feedback influenced their learning and skill transfer to related tasks. Additionally, we explored computational models to gain insights into how individuals integrate evaluative feedback into their decision-making processes.
Lastly, we expand our focus from a single human operator to a team of heterogeneous agents, each with diverse skill sets and expertise. Within this context, we delve into the challenge of achieving efficient collaboration among heterogeneous team members to enhance overall system performance. Our approach leverages a game theoretic framework, where we design utility functions to incentivize decentralized collaboration among these agents.
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Department:
Biomedical Engineering
Name:
Alesa Netzley
Date Time:
Monday, October 16, 2023 - 10:00am
Location:
1404 Interdisciplinary Science and Technology Building and Zoom
Announcement:
ABSTRACT
Advisor: Prof. Galit Pelled
Traumatic brain injury (TBI) is a leading cause of death and disability among children and adolescents in the United States. An estimated 90% of head-injury-related emergency department visits result in a diagnosis of mild TBI (mTBI) also known as concussion. Historically ignored as a major public health concern, concussion can cause lasting neurocognitive changes that can persist for years or even decades; well beyond the typical 2-week clinical recovery period. Postconcussive syndrome (PCS) encompasses a constellation of cognitive and physiological symptoms that continue to occur weeks, months, or years after a concussion. In children and teenagers, these impairments can disrupt an individual’s developmental trajectory, leading to underperformance in academics, poor integration into the workforce, and diminished quality of life in adulthood. Preclinical neuroscience has greatly improved our understanding of the consequences of head injury, however vast architectural differences between rodent and human brains has resulted in dismal translation of therapeutic strategies from the bench to the bedside. In recent decades, the domestic pig (sus scrofa) has attracted considerable attention as a highly promising model animal for studying age-specific responses to mechanical trauma due to striking similarities between pig and human brain anatomy, development, and neuroinflammatory response. To add to the growing body of work utilizing pigs for the study of brain injury, we have developed a model of pediatric concussion in juvenile Yucatan miniature pigs. We conduct an extensive battery of cognitive and behavioral assessments designed to reveal post-concussive complication in pigs. We also conduct clinically relevant live imaging procedures to better understand the effects concussion can have on brain connectivity and function. The utilization of an animal model whose neuroanatomy closely resembles the human brain is critical to the development of therapeutic protocols that are effective and safe.
Department:
Civil and Environmental Engineering
Name:
Omid Bagheri
Date Time:
Friday, October 13, 2023 - 3:00pm
Location:
1234 Engineering Building
Announcement:
ABSTRACT
Advisor: Dr. Yadu Pokhrel
This dissertation investigates the intricate dynamics of hydrologic systems in the Amazon River basin (ARB) in the face of evolving climate patterns and human interventions. The ARB – a pivotal element of the global climate, hydrological, and biogeochemical systems – holds immense biodiversity and profoundly influences global water, energy, and carbon cycles. Climate variations and human activities, especially deforestation in the southern subbasins, have considerably altered the basin's functioning. Despite extensive research, critical gaps persist in understanding key hydrological processes and rainforest resilience. This research disentangles the impacts of climate and land use/land cover (LULC) changes toward devising robust resource management strategies. The dissertation employs state-of-the-art hydrological modeling, examining the pivotal role of shallow groundwater in modulating surface fluxes and potentially averting rainforest transformation. The results indicate that at least 34% of the Amazonian Forest is supported by groundwater during the dry season. This study reveals a two-month lag between seasonal peak evapotranspiration (ET) and river discharge as a crucial mechanism in preventing rainforest tipping into savanna. The ARB is dominantly energy limited; however, the results suggest that in the absence of groundwater support, and with less than ~125 mm/month of precipitation, the ARB could have become water-limited, at least in some regions. The long-term basin-averaged ET—dominated by transpiration—changed with a split pattern of ±9% in the past three decades. Similarly, water table depth (±19%) and runoff (±29%) changed with a heterogeneous patterns across the ARB. Moreover, by quantifying the impact of climate variability and LULC changes this research finds that climate variability remains the dominant influence on WTD dynamics; however, the impacts on ET varied across the basin. Runoff patterns were intricately tied to precipitation and water table dynamics, demonstrating regional variations influenced by both climate variability and LULC changes. Through a comprehensive area fraction analysis, this research identifies tipping points associated with groundwater dynamics. This study provides crucial insights on (i) the dominant hydrological processes, (ii) isolated impacts of climate variability and LULC change on the water cycle of the ARB, and (iii) tipping points in the ARB that are associated with groundwater dynamics. These findings could be used to inform effective water resource management and sustainable environmental practices in this ecologically significant region.
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Department:
Chemical Engineering and Materials Science
Name:
Geeta Kumari
Date Time:
Monday, October 2, 2023 - 9:00am
Location:
Zoom
Announcement:
ABSTRACT
Advisor: Dr. Carl Boehlert
Alloy ATI 718Plus is a relatively new Ni-based superalloy developed to improve upon the properties of Inconel 718. It shows improvement in service temperature up to 704 ºC (55 ºC more than IN718) and formability similar to IN 718 and better than that for Waspaloy because of its chemical composition, microstructure and major strengthening phase, γ'. The microstructure plays a vital role in deciding the mechanism of particle-dislocations interaction during deformation as the mechanism changes with particle size. The understanding of active mechanism with unimodal distribution with average γ' particle size is well studied in the literature, but consideration of smaller and larger precipitates together, called bimodal, is still lacking. The study aims to understand the development of bimodal γ' precipitate size distribution in ATI 718Plus and its stability under various thermal and tensile stress conditions.
In this study, initial optimization of solutionizing temperature was conducted for subsequent aging treatment. The as-processed sample underwent heat treatment at 1000 ºC for 1 hr, followed by water quenching (WQ). The aging process encompassed single-step and two-step methods with varied parameters, including time, temperature, and cooling rate. For the two-step treatment, the sample was heated to 900 ºC for 2 hr, then quenched to room temperature before being heated to 720 ºC for 10 hr and quenched again. The resultant microstructure displayed a uniform bimodal distribution of γ' precipitates, with sizes of 11 nm and 55 nm for smaller and larger precipitates, respectively. The developed microstructures underwent tensile testing to failure to assess their yield strength (YS), ultimate tensile strength (UTS), and elongation-to-failure (εf ). Some of the tensile samples, intentionally unloaded after achieving 2-4 % engineering strain, were evaluated using transmission electron microscopy to investigate the γ' precipitate-dislocation interactions. In the case of unimodal samples, weak-pair shearing was observed to be the dominant mechanism for smaller γ' precipitates (~14 nm), while both strong-pair shearing and dislocation loops were observed for the microstructures containing larger γ' precipitates (~48 nm). The microstructure containing a bimodal distribution of γ' precipitates exhibited shearing as a dominant mechanism and also resulted in the largest strength values. The combined influence of temperature and elastic tensile stress on γ' precipitate stability was examined. Under simultaneous application of temperature and stress (creep), γ' precipitate growth accelerated in contrast to samples exposed only to temperature. The amount of growth varied in different grain orientations in the creep-deformed sample.
Persons with disabilities have the right to request and receive reasonable accommodation. Please call the Department of Chemical Engineering and Materials Science at 355-5135 at least one day prior to the seminar; requests received after this date will be met when possible.
Department:
Chemical Engineering and Materials Science
Name:
Shrirang Sabde
Date Time:
Monday, September 18, 2023 - 8:00am
Location:
3540 Engineering Building
Announcement:
ABSTRACT
Advisors: Dr. Ramani Narayan and Dr. Ganapati D. Yadav
Plastics wastes on land and in oceans has become major societal issues. Articles in print, television, and social media about plastics waste issues and bans on plastic items are on the rise everywhere in the world. Most serious is the issue relating to plastics persistence and microplastics contamination of the environment. Against this backdrop, my thesis presents work on recycling polyethylene terephthalate (PET) and Nylon 6 polymers to its individual monomer constituents by melt depolymerization using phase transfer catalyst. PET and Nylons are industrial polymers used in the manufacture of bottles, carpets, textile, fabrics, and many other products.
Melt depolymerisation of polyethylene terephthalate (PET) and Nylon 6 waste was studied using a 2-L high pressure autoclave reactor under autogenous pressure in excess water for various time intervals. Polyethylene Glycol (PEG 400) was used as a novel phase transfer catalyst for the depolymerization. An engineering model based on solid (polymer)-liquid(melt)-liquid (water) phase transfer catalysis (PTC) for hydrolytic depolymerization was developed and validated. It was found that the PEG phase transfer catalyst was found to be more efficient and effective than the standard metal catalysed depolymerization.
The PEG 400 phase transfer catalyst system was applied to the hydrolytic depolymerization of Nylon 6 (polyamide). Caprolactam monomer was obtained in 90-95% yield in 60 min using a temperature range of 200-250 0 C. The PTC catalyzed nylon 6 hydrolysis model was developed and validated. The second part of the thesis work involved synthesis of biobased and biodegradablecompostable polyesters for packaging associated with food, and paper-based products. Current carbon-carbon backbone polymers used in these applications cannot be recovered from the food/organic waste stream for recycling. They are not biodegradable and become persistent contaminants during composting of the organic/food waste stream. Therefore, there is a need for new polymer packaging that preserves and protects the integrity of the product but at the end-oflife can be readily composted along with the food/paper/organic wastes.High molecular weight (60- 80 kg/mol) polybutylene adipate co-terephthalate (PBAT), polybutylene sebacate co-terephthalate (PBSeT), and polybutylene azelate co-terephthalate (PBAzT) were synthesized. . The polymers obtained was characterized by intrinsic viscosity, acid number, and molecular weight. The extent of reaction was determined by monitoring acid group in the reaction moisture. Also, reaction kinetics has been studied for transesterification and esterification steps.
The compostability of such polyester products along with food, & paper waste was studied in a commercial scale compost bioreactor. Process parameters to operate the compost bioreactor were established. The food waste, paper and compostable products were converted to a stable, brown organic product with 70% volume reduction in 8 days. The results demonstrate that compostable plastics packaging products in concert with food/organic waste can be responsibly managed by integrating with small scale compost bioreactors.
Persons with disabilities have the right to request and receive reasonable accommodation. Please call the Department of Chemical Engineering and Materials Science at 355-5135 at least one day prior to the seminar; requests received after this date will be met when possible.
Department:
Civil and Environmental Engineering & Mechanical Engineering
Name:
Aref Ghaderi
Date Time:
Tuesday, August 22, 2023 - 4:00pm
Location:
3540 Engineering Building
Announcement:
ABSTRACT
Advisor: Dr. Roozbeh Darganzany
Nowadays, cross-linked elastomers play a significant role in several industries such as aerospace, construction, transportation, marine, aeronautics, and automotive due to excellent flexibility, toughness, form-ability, and versatility. During their intended service-life, the material is supposed to sustain aggressive environmental damages induced by water infusion, temperature, and solar ultraviolet radiation (UV) during their operation, which affects their durability and properties.
A reliable design of rubber components to prevent early failure by environmental degradation requires digital simulations by means of high-fidelity thermo-mechanical constitutive models that can simulate the adverse effects of aging on mechanical, electrical, thermal, and failure properties of polymers. So far, most aging models are developed by coupling hyperelastic constitutive models with single-kinetic degradation models, to demonstrate the decay of materials during aging. However, a more detailed modeling approach can be achieved through modular continuum-based damage models that integrate the finite strain theory and thermo-mechanical degradation models.
Rubber elasticity theory is driven partly based on (i) statistical mechanics at micro-scale (ii) Phenomenological Modeling at Meso-scale for modeling of the network (iii) Continuum Mechanics at Macro-scale to model the material. So, hyperelastic models fall into three main categories: the phenomenological approach, the micro-mechanical approach, and the data-driven approach.
Recently, the emergence of machine-learned (ML) models has attracted much attention. The first generation of "black-box" ML models as another type of phenomenological model was proposed to model the mechanical behavior of rubbery media.
In solid mechanics, stress–strain tensors are only partially observable in lower dimensions. Thus, obtaining data to feed a black-box ML model is exceptionally challenging. Thus, these approaches soon become obsolete due to the high demand for data for training, and the lack of constraint on their output margins.
The issue can be resolved in a new generation of ML models which is inspired by physics-informed neural networks (PINN) which infuse physics-based knowledge into the black-box models. Here, we modify PINN models to develop hybrid frameworks that can address the limitations of both phenomenological and micro-mechanical models by obtaining micro-structural behavior from the macroscopic experimental data set.
The objective of this defense is to provide a new approach for reduced-order physics-based Data-driven modeling of multi-stressor damage in elastomers by infusing Knowledge into a neural network. The following are the major thrusts of our research in the proposed dissertation:
(i) To design a systematic approach to reduce order of the constitutive mapping and address the data volume problem for training.
(ii) To incorporate background knowledge from polymer physics, continuum mechanics, and thermodynamics into the neural networks and constraint the solution space.
(iii) To develop a neural network to predict various inelastic effects which is far less data-dependent, more interpretable than current PINN, and uses a knowledge-confined solution space.
(IV) To validate our proposed hybrid framework based on limited data to describe the relationship between elastomeric network mechanics and environmental degradation.
To go into further detail, the model has been successfully developed and validated in five different damage scenarios which describe the evolutionary process of developing the final platform. These steps are as follows, (I) Providing a model for polymers in non-extreme environments to capture the dependence of elastomer behavior on loading conditions such as strain rate and temperature, as well as compound morphology factors such as filler percentage and crosslink density, (II) developing a model for single mechanism aging, i.e. thermal aging, or hydrolytic aging, (III) developing a model to capture accumulation damages of fatigue and thermo-aging, (IV) introducing Physics informed neural networks (PINNs) to simulate multiple stiff, and semi-stiff ODEs that govern Pyrolysis and Ablation, and (V) developing a Bayesian surrogate constitutive model to estimate failure probability of elastomers.
The models used in the proposed platform are the first hybrid models developed and validated for polymer components and thus, bring great novelty and value to the industry. The model proposed in this work can significantly improve the design process of polymeric components by predicting the reliability, durability, and performance loss of materials based on the projected mechanical and environmental loading conditions. Such knowledge can significantly reduce the design cost, reduce the number of reliability tests needed, reduce the maintenance costs and overhauls, and most importantly prevent unexpected catastrophic failures.
Persons with disabilities have the right to request and receive reasonable accommodation. Please call the Department of Mechanical Engineering at 355-5131 at least one day prior to the seminar; requests received after this date will be met when possible.
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Department:
Mechanical Engineering
Name:
Jun Guo
Date Time:
Thursday, August 24, 2023 - 12:00pm
Location:
Zoom
Announcement:
ABSTRACT
Advisors: Dr. Daniel Segalman and Dr. Wolfgang Banzhaf
Constitutive modeling of engineering materials is a prerequisite to making predictions about systems of which those materials are components. Often the analyst is faced with a new material or a traditional material in a state (strain, strain rate, temperature, etc.) for which there is no accepted constitutive model. In such cases the analyst must construct a constitutive model suitable to the purpose in an ad hoc manner, a task often dependent on individual experience or serendipity.
Here, we firstly explore a naive genetic programming approach to constructing constitutive equations suitable for engineering analysis, but the results of its direct application are disappointing. Next, a number of approaches are employed to address the problem in its components resulting in significantly better equations with respect to criteria regularly applied to assessing the utility of constitutive models. The improved approach is applied to constructing constitutive models for a metal (a yield function), two bio-materials (ligament and aorta tissues), and a geo-material (frozen soil). The approach developed here shows more benefits over a direct application of genetic programming as the material behavior becomes more complex.
An additional approach is introduced to generate the basis functions, which makes it easier to formulate the nonlinear behavior for engineering materials. It considers the separate effects of each variable and their interactions to formulate the material behavior more precisely. By using the basis functions, we can generate hierarchical models with varying conformity to experimental data, complexity, and condition number.
It is conventional to try to find a vector of parameters in the process of model calibration that yields an adequate fit with the calibration data and to use that for model predictions. For various reasons, including how one defines “adequate fit” (or even “best fit”) is quite arbitrary, there can be a subspace of equally plausible parameter vectors. A measure of merit for constitutive models is that though there may not be a unique acceptable parameter vector all plausible parameter vectors will be very similar. If this condition is not satisfied there may be substantial variance for the models that can be fit equally well by a multitude of parameter vectors and uncertainty quantification becomes impossible. The contribution of non-uniqueness of calibrated parameter vectors to meaningful prediction is illustrated on two different problems. A mathematical formulation for this measure of merit involving condition number of a Hessian matrix is proposed so as to incorporate this parameterization issue in the production of candidate constitutive models.
Multi-objective optimization is employed to generate constitutive models with good fitness, complexity, and condition number. The evaluation of one of these, the condition number, is computationally prohibitive when incorporated into the problem in a conventional manner. We developed an approach to alleviate this issue and generate models at great efficiency.
Persons with disabilities have the right to request and receive reasonable accommodation. Please call the Department of Mechanical Engineering at 355-5131 at least one day prior to the seminar; requests received after this date will be met when possible.
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Department:
Electrical and Computer Engineering
Name:
Haojun Wang
Date Time:
Tuesday, August 22, 2023 - 11:00am
Location:
C-103 Engineering Research Complex and Zoom
Announcement:
ABSTRACT
Advisor: Dr. Hogan
This dissertation presents two innovative contributions in the realm of materials science and nanotechnology. The first part introduces a novel integrated photodetector design, combining the two-dimensional material MoS2 with plasmonic nanoantenna arrays (NAs). These gold NAs were fabricated by e-beam lithography and strategically positioned above and below a MoS2 semiconductor layer. The nanoarrays led to significant local electric field enhancement through the thickness of the MoS2 layer at the nanoantenna interface and a resulting optical detection enhancement factor of up to 25. The fabrication process of the photodetector is detailed in this dissertation, encompassing MoS2 nanosheet transfer, NAs patterning, and layered NAs alignment. Experimental and simulation-based characterizations affirm the potential of the proposed integrated photodetector for enhanced optical field absorption and detection, with applications in photodetection and nonlinear optical processes.
The dissertation then delves into the electrical characterization of MoS2-based photodetectors, concentrating on photosensitivity and optimization parameters. Notably, the incorporation of the NAs significantly enhances electron-hole pair generation and reduces resistance. Optimized conditions for high net photocurrent and minimal power consumption are identified. Moreover, the nonlinear absorption behavior of the NAs-integrated devices is investigated, revealing the exceptional nonlinear optical properties of the double-layered NA/MoS2/NA structure. This structure exhibits strong two-photon absorption and provides valuable insights into nonlinear absorption processes, promising applications in near-infrared detection, energy harvesting, and spectroscopy of organic materials.
In the second part of the dissertation, a groundbreaking technique called reactive pulsed laser deposition of SiC is introduced. This technique allows precise and controlled deposition of a large number of SiC particles. The process involves a pulsed laser generating a localized hot spot on a target source, resulting in the ejection of silicon (Si) and carbon (C) atoms that combine to form SiC nanoparticles on the substrate surface. The fabricated SiC particles display intriguing photoluminescent properties and enable the production of a diode with distinct current rectification behavior. The experimental results demonstrate the efficacy of the reactive pulsed laser deposition technique, showcasing its potential for advancing the localized fabrication of SiC-based electronic devices and structures.
This Ph.D. dissertation significantly contributes to the understanding of integrated photodetectors, nonlinear optical effects, and precise material deposition techniques. The insights gained pave the way for enhanced optical field and absorption in photodetection, nonlinear optical processes, SiC-based devices, and open up new avenues for diverse research fields.
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Department:
Mechanical Engineering
Name:
Tyler J. Bauder
Date Time:
Tuesday, August 15, 2023 - 11:00am
Location:
Zoom
Announcement:
ABSTRACT
Advisor: Dr. Patrick Kwon
Electron Beam Melting (EBM) is a relatively new Powder Bed Fusion (PBF) Additive Manufacturing (AM) process. Unlike a very similar laser PBF process, EBM process occurs an Ultra-High Vacuum (UHV) and high temperature (~700°C) chamber, reducing residual stress and providing superior protection against oxidation. This makes EMB ideal for processing oxygen sensitive materials like Ti-6Al-4V, whose high strength-to-weight ratio, corrosion resistance, and high temperature performance have drawn the interest of aerospace and other high-performance manufactures. Due to the nature of these industries, fatigue life is of particular interest. However, the relationship between EBM processing and fatigue life is not well studied and is thus the focus of this dissertation.
First, a L16 Taguchi Design of Experiments (DOE) was constructed to investigate the effects of Focus Offset, Line Offset, Speed Function, Hot Isostatic Pressing (HIP) treatment, and surface roughness on the Very High Cycle (VHC) fatigue life of Ti-6Al-4V. Two HIP treatments were 800°C and 200 MPa for 2 hours and 1100°C and 100 MPa for 2 hours with 2.5°C/min quench. Half of the samples were tested in the as-machined condition with an average roughness, Ra, of 0.2 μm and the other half were further polished using Magnetic Assisted Finishing (MAF) to Ra = 0.1 μm. An ultrasonic fatigue testing machine was used to test fatigue life at 500 and 550 MPa loads, with a load ratio of R = -1. Nearly 225 samples were tested with 7 repeats per load condition.
Fatigue results indicated that none of the machine parameters and surface roughness had a statistically significant correlation with fatigue life. However, a statistically significant correlation between HIP treatment and fatigue life was found. The 800°C samples performed as well as, if not superior, to conventional Ti64 with the average fatigue lives of 8.08E+07 and 3.28E+06 cycles for 500 and 550 MPa, respectively. While the 1100°C samples displaced significantly lower fatigue performance with the average fatigue lives of 7.21E+05 and 1.38E+05 cycles for 500 and 550 MPa, respectively. Microstructure and fractography investigations suggest that the poor performance of 1100°C samples can be attributed to coarsening of the prior beta (β) grains during the super-transus HIP treatment leading to the formation of large colonies of similarly orientated alpha (α) grains, allowing for easier dislocation movement across aligned preferential slip directions.
This study concluded that the most important factor controlling fatigue life of EBMed Ti-6Al-4V is post HIP/heat treatment and that fine-tuning of print settings beyond those required to prevent obvious porosity and swelling defects will not have significant effects on the fatigue life of HIPed Ti-6Al-4V.
Persons with disabilities have the right to request and receive reasonable accommodation. Please call the Department of Mechanical Engineering at 355-5131 at least one day prior to the seminar; requests received after this date will be met when possible.
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Department:
Mechanical Engineering
Name:
Nicole Arnold
Date Time:
Monday, August 14, 2023 - 2:00pm
Location:
3112 Engineering Building and Zoom
Announcement:
ABSTRACT
Advisor: Dr. Tamara Reid Bush
Osteoarthritis (OA) is a debilitating musculoskeletal disease that causes degeneration of the joint surfaces. One of the most common areas of OA is at the base of the thumb, or the carpometacarpal (CMC) joint. CMC OA has been cited as a common cause of joint pain and disability which affected range of motion and strength of the hand. Due to loss of hand function, individuals had trouble carrying out activities of daily living which has resulted in a decrease in independence. Furthermore, CMC OA disproportionally affected females more than males, especially over the age of 55.
When conservative treatment options failed, surgical intervention may be necessary. The most common surgical option, ligament reconstruction with tendon interposition, was used to restore function and reduce pain for those who have thumb CMC OA. The effectiveness of surgery was commonly determined via patient questionnaires and clinical measurement devices. Clinical measurement devices to document changes pre- and post-surgery insufficiently captured the three-dimensional (3D) movement of the thumb and lacked accurate representation of isolated thumb forces. Relying on these clinical metrics have led to gaps in research associated with the thumb for both healthy and arthritic individuals. For the best treatment options and rehabilitation, new data and methods associated with thumb function are needed.
The objectives of this work were to: 1) identify the most appropriate mathematical method (Euler or body-fixed floating axis joint coordinate system methods) to obtain 3D motion patterns of the thumb, 2) determine and compare the motion abilities of the thumb in healthy males and females split into two groups (older healthy (OH) and younger healthy (YH)) and of those with CMC OA at three time points (pre-surgery, 3-months and 6-months post-surgery), and 3) compare isolated thumb force generation in males and females (OH and YH) and of those with CMC OA at three time points (pre-surgery, 3-months and 6-months post-surgery).
Results showed OA individuals utilized compensatory mechanisms to complete certain motion tasks compared to the healthy groups. This is most likely a result of pre-surgery ligament laxity and functional changes post-surgery at the CMC joint. Examination of force data showed that generally, only 50% of CMC OA participants improved at 6-months post-surgery compared to pre-surgery in their force abilities. Comparisons between healthy and OA groups yielded no significant impact on the amount of force generated at the three self-selected locations. Thumb pull forces were statistically larger across all groups. OH males and females produced larger isolated thumb pull forces compared the YH males and females. Additionally, wrist position only significantly impacted OH female force generation.
Overall, this work presents a novel, detailed method for data collection and analysis of thumb motion and force generation. This research provides clinicians with in-depth evidence to encourage individuals to pursue conservative treatment sooner and hand surgeons with more comprehensive information to create specialized treatment plans for those with thumb CMC OA.
Persons with disabilities have the right to request and receive reasonable accommodation. Please call the Department of Mechanical Engineering at 355-5131 at least one day prior to the seminar; requests received after this date will be met when possible.
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Department:
Mechanical Engineering
Name:
Kian Kalan
Date Time:
Friday, August 11, 2023 - 1:00pm
Location:
Zoom
Announcement:
ABSTRACT
Advisors: Dr. Ahmed Naguib and Dr. Manoochehr Koochesfahani
Precision Airdrop Systems (PADS) face difficulties in controlling their landing accuracy when flow-induced vibrations of the suspension lines occur. Recent research has identified a previously unknown cause of these vibrations: galloping of the suspension cables. Galloping is a type of vibration that can occur in cylinders with non-circular cross-sections. The suspension cables in PADS have a cross-section that is approximately rectangular in shape with rounded corners, but with the added complexity of surface topology (due to braiding of the lines). Using load measurements, recent experiments have shown that the presence of surface topology can alter the stability of rectangular cylinders to galloping; an effect that is dependent on Reynolds numbers. Knowledge of the corresponding topology effect on the flow around the cylinders is presently lacking. Therefore, this study aims to investigate the impact of surface topology on the boundary layer and near-wake flow around a rectangular cylinder with a side-ratio of 2.5 and fully-rounded corners (half-circular leading and trailing edges). The Reynolds number based on the cylinder thickness (𝑑𝑑) is in the range 𝑅𝑅𝑒𝑒𝑑𝑑 = 800 − 2500. The surface topology is defined using spatial Fourier modes with an amplitude of 5% of 𝑑𝑑, applied along the perimeter only (2D geometry) and along both the perimeter and the span (3D geometry) of the cylinder. While not an exact replica, this surface topology represents the characteristics of the actual suspension cable reasonably well. The study also investigates the effects of different topology amplitudes by using cylinders with 2.5% and 10% of 𝑑𝑑. Single-component molecular tagging velocimetry is employed to measure the streamwise velocity and visualize the flow field at various locations above the surface and in the wake of the cylinder.
Mean and root-mean-square velocity profiles are analyzed to examine the development of the boundary layer and separated flow on the top and bottom surfaces of the cylinder. The mean separation bubble and the development of the shear layer unsteadiness over the surface of the cylinders are discussed at 𝛼𝛼 = 0° and at different Reynolds numbers. The results demonstrate the Reynolds number-dependent effect of the surface topology cross-sectional geometry and its variation along the span. An interpretation is provided of how these results could influence the galloping instability of the cylinder.
The wake flow is investigated to help better understand the relationship between wake structures, surface topology, and the characteristics of the boundary layer. To achieve this, wake mean and rms velocity profiles are interrogated and the effect of the geometry on the Strouhal number of the wake vortex shedding is analyzed. An examination is also conducted to investigate the unsteady flow physics of the boundary layer and its relationship to the wake flow. This examination uses quantitative measures and flow visualization, and focuses on the smooth-surface cylinder. The analysis identifies and compares different Reynolds number dependent boundary- layer flow regimes. The correlation between the wake vortex shedding structure and various boundary-layer regimes is examined and compared to established understanding in literature for a sharp-corner rectangular cylinder.
The results reveal that the details of the topology near the leading edge of the cylinder are most significant in affecting the behavior of the boundary layer flow. For the particular topology wavelength used in the present study, the biggest effect is found when a topology peak is present at the leading edge for the 2D (2Dp) geometry. In comparison to the smooth cylinder, the 2Dp topology substantially increases the separation zone thickness and the separated shear layer unsteadiness. The ensuing wake flow, exhibits an increased wake closure length, slower recovery of the mean centerline velocity, lower vortex shedding Strouhal number, and disrupted wake vortex organization.
Persons with disabilities have the right to request and receive reasonable accommodation. Please call the Department of Mechanical Engineering at 355-5131 at least one day prior to the seminar; requests received after this date will be met when possible.
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Department:
Electrical and Computer Engineering
Name:
Abu Farzan Mitul
Date Time:
Monday, August 7, 2023 - 3:00pm
Location:
Zoom
Announcement:
ABSTRACT
Advisor: Prof. Ming Han
Optical fiber sensors are employed to study and investigate several physical-related parameters e,g, pressure, stress, vibration, rotation, current, bending, displacement etc. Moreover, optical fiber sensors are also used for several kinds of chemical parameters i,e, composition, level, liquid flow, concentration, gases detection and monitoring it`s presence. Fiber optic sensor (FOS) technology is associated with optical related accessories e,g, light processing (filters), optical source (laser, LED), optical detector (spectrometer. photodiode), light guiding (lenses) etc. In addition to the use of FOS technology, laser Diode (LD) has great deal of importance due to its easy integration, small size and moderate price. Semiconductor lasers are complex nonlinear systems where relatively small optical feedback can have a profound impact on the spectral and temporal behavior of the laser output. Under appropriate conditions, optical feedback provides a straightforward and highly effective way for laser linewidth reduction. These conditions can be met in the so-called self-injection locking [1, 2] or filtered optical feedback [3, 5] configurations where part of output light, after passing through an optical resonator, is injected back to the laser to interfere coherently with the light inside the laser internal cavity. Due to the excellent noise performance and straightforward implementation, fiber-pigtailed lasers under self-injection locking have been studied as light sources for fiber-optic sensor systems whose performance is sensitive to laser frequency noises such as phase-sensitive optical time-domain reflectometry systems and fiber-optic gyroscopes [6, 7, 8].
We present a method to suppress the wavelength drift of a semiconductor laser with filtered optical feedback from a long fiber-optic loop. The laser wavelength is stabilized to the filter peak through actively controlling the phase delay of the feedback light. A detailed steady-state analysis of the laser wavelength is performed to illustrate the method. Experimentally, the wavelength drift was reduced by 75% compared to the case without phase delay control. The active phase delay control had negligible effect on the line narrowing performance of the filtered optical feedback to the limit of the measurement resolution.
The long optical feedback length makes the lasers prone to mode-hopping. There have been reported attempts at suppressing mode-hopping by light polarization control and using more compact resonator [6, 7] but no detailed characterization of mode-hopping and the associated laser instability has been reported. We studied the mode-hopping and laser instability of the self-injection locked laser and found that a mode hopping event causes an abrupt change in the laser intensity after the resonator inside the feedback loop. Experiment shows that the frequency of locked lasers could oscillate during unstable operations. The fundamental frequency is determined by the time delay of the feedback light.
We demonstrate the use of a self-injection locked distributed feedback (DFB) diode laser for high-sensitivity detection of acoustic emission (AE) using a fiber-coil Fabry-Perot interferometer (FPI) sensor. The FPI AE sensor is formed by two weak fiber Bragg gratings on the ends of a long span of coiled fiber, resulting in dense sinusoidal fringes in its reflection spectrum that allows the use of a modified phase-generated carrier demodulation method. The demodulation method does not require agile tuning capability of the laser, which makes the self-injection locked laser particularly attractive for the application. Little work has been reported on using self-injection locked lasers in fiber-optic AE or ultrasonic sensor systems due to the challenges induced by the lack of the agile wavelength tuning capability of a self-injection locked laser. Experimental results indicate that the self-injection locked laser increases the signal-to-noise ratio by ~33 dB compared with the free-running DFB laser.
Furthermore, we have developed a low-cost fiber-optic sensor system that can measure absolute strain at multiple positions along a fiber using fiber-bragg grating sensors. A challenge in absolute strain measurement from an optical interferometer is that the order of fringes that can be recorded is high and typically cannot be determined precisely. A small strain could lead to a spectral shift of multiple orders, resulting in ambiguity in determine the absolute strain. In this system, we form a “rf interferometer” with low order fringes to ensure that the strain-induced rf spectral shift does not exceed half of the fringe period, rendering the possibility of absolute strain measurement. The spectral shift is limited to be within half of the fringe period to eliminate phase ambiguity and obtain the absolute strain.
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Department:
Biomedical Engineering
Name:
Ethan Tu
Date Time:
Thursday, August 3, 2023 - 10:00am
Location:
1404 Interdisciplinary Science and Technology Building and Zoom
Announcement:
ABSTRACT
Advisor: Prof. Adam Alessio
Artificial intelligence (AI) has evolved immensely in recent years, with AI achieving human levels of performance on a wide variety of tasks. However, AI has had limited adoption in clinical settings despite its promising prediction, classification and pathology detection applications. For a machine learned (ML) model to train effectively, the observed data must be a diverse, accurate representation of the true distribution. Therefore, to properly estimate the true distribution, extremely large datasets become necessary. In healthcare scenarios, datasets of sufficient size may be rare or absent, thus hindering the training of ML models. One of the ways to mitigate this problem is through data augmentation, where we supplement our datasets with slightly modified copies of already existing data or newly created synthetic data. Recently, sophisticated data augmentation methods are based on a class of neural networks (NNs) called Generative Adversarial Networks (GANs), which generate new images of high perceptual quality. This dissertation describes the design and development of a new type of GAN, named near-pair patch cycleGAN (NPP-cycleGAN), which generates realistic pathology-present images. Here, we train and test this network using pediatric chest radiographs. We demonstrate that the proposed GAN can generate high quality fracture-present pediatric chest radiographs. With the addition of these synthetic images to an object detector’s training dataset, we are able to improve the fracture detection performance. These results suggest that our proposed method can be applied to other pathology detection tasks and could potentially enable improved object detector performance in multiple clinical scenarios.
Persons with disabilities have the right to request and receive reasonable accommodation. Please call the Department of Biomedical Engineering at 884-6976 at least one day prior to the seminar; requests received after this date will be met when possible.
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Department:
Chemical Engineering and Materials Science
Name:
Tyler Nathaniel Johnson
Date Time:
Thursday, July 27, 2023 - 1:30pm
Location:
3540 Engineering Building and Zoom
Announcement:
ABSTRACT
Advisor: Dr. Andre Lee
Surface engineering has gained significant importance in the search for composites with enhanced properties. Among the materials of interest, graphene nanoplatelets stand out due to their unique characteristics, such as exceptional mechanical, electrical, and thermal properties. However, incorporating graphene-like materials into composites poses challenges due to the chemically inert nature of the basal plane. Nano-scale surface engineering techniques are necessary to render graphene-like materials suitable for optimized composites. In addition to surface engineering at the nano-scale, this document explores surface engineering at the macro-scale for the development of innovative manufacturing methods for pouch cell battery packaging materials in next-generation electric vehicles.
Plasma processing offers a promising approach to modify the basal plane of graphene, bridging the gap between chemical and physical methods. This document investigates the effects of C4F8 and O2 plasma source gases on graphene nanoplatelets. Precise control allows low-temperature plasma treatment to modify the graphene nanoplatelet surface without altering its intrinsic structure. This provides new opportunities for surface engineering in advanced composites. Plasma treatment enables tailored immersion characteristics and the introduction of functional groups, creating desired bonding environments. Plasma treatment is a powerful and efficient method for developing graphene-based composite materials.
Currently, battery thermal management systems in pouch cell systems rely on the use of cold plates, which have limitations such as increased weight and reduced energy density. To overcome these challenges, the integration of cold plate designs into existing pouch cell materials is proposed, and a novel manufacturing method is developed.
The novel manufacturing process is based on roll-molding, which allows for easy adoption at the manufacturing level by leveraging existing roll-to-roll lamination processes. Proof-of-concept experiments are conducted using laboratory-scale equipment to demonstrate the feasibility of the proposed approach. Furthermore, the document presents insights into scaling up the manufacturing process and identifies semi-optimized rolling conditions for the production of state-of-the-art cold plate designs.
This research aims to enhance the thermal management capabilities of pouch cell battery packaging materials, improving electric vehicle battery system performance and efficiency. Lamination procedures were compared to benchmark processes, with testing conducted on the laminated samples to evaluate mechanical integrity and oxygen permeability. Additionally, advanced materials were developed as superior alternatives to current 3-layer laminates, offering enhanced properties and manufacturability. These materials have the potential to enhance battery pack performance and functionality beyond existing limitations. The findings presented in this document provide valuable insights for advancing battery packing technologies, paving the way for more efficient, reliable, and high-performance battery systems in various applications.
Persons with disabilities have the right to request and receive reasonable accommodation. Please call the Department of Chemical Engineering and Materials Science at 355-5135 at least one day prior to the seminar; requests received after this date will be met when possible.
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Department:
Chemical Engineering and Materials Science
Name:
Shaylynn Crum-Dacon
Date Time:
Thursday, July 27, 2023 - 11:00am
Location:
3540 Engineering Building and Zoom
Announcement:
ABSTRACT
Advisor: Dr. Robert C. Ferrier Jr.
Epoxides, polyether precursors, are favorable materials for many applications. They have a ring strain that promotes polymerization, diverse functionalities, and are relatively easy to synthesize through sustainable means. Although epoxide polymerization can be traced back a few decades, it wasn’t until 2017, when published work by Ferrier reported using mono(μ-alkoxo)bis(alkylaluminum) (MOB) to quickly and easily polymerize different epoxides. This polymerization platform will be used to explore polyether-based single-ion conductor electrolytes in the first work. Polymer electrolytes are said to be the future for lithium batteries. By replacing the anodic materials with solid lithium (making a lithium metal battery) and the organic solvent media with a polymer, the high functioning lithium metal’s properties will increase battery efficiency. This project will dive into utilizing poly(epichlorhydrin) (PECH) and poly(propylene oxide) (PPO) to synthesize a single-ion conducting electrolyte. This work reveals synthesis of the single-ion conductor (SIC) using bis(trifluoromethanesulfanamide) as the single-ion conducting moiety. The incorporation of PPO combats the crystallinity of PECH, which is shown by Tg analysis and ionic conductivity. The knowledge gained from this research will be valuable in moving forward for solid state electrolytes for lithium metal battery applications.
Polymer composites are currently the most popular way to advance electrolyte matrices within lithium batteries, as they can eliminate the adverse properties of polymers (i.e. crystallinity and low ionic conductivity) by employing filler (solvents, ceramics, carbon powders, etc.) materials within the matrix to improve properties for desired applications. With intentions to incorporate the previous project, this work focuses on utilizing polyether-grafted nanoparticles (NPs) incorporated in a polyether matrix to study ether composites for LMB applications. An initiator was grafted onto the surface of the NPs and ECH was polymerized from the site. We alleviate compatibility concerns by using a low molecular weight ether matrix with a high molecular weight ether filler as well as explore possible electrolyte applications and combinations with the ether SIC.
The final project is intended to further epoxide use and application by expanding possible polymerization methods inspired by MOB synthesis. This work utilizes primary and secondary amine compounds to synthesize polymerization platforms. This introductory study revealed simplistic synthesis of different amine initiators that could be used in tandem with the N-Al adduct to polymerize epoxides with different functionalities. These platforms will be utilized to explore different pathways for synthesizing polymer and composite electrolytes for lithium battery applications.
Persons with disabilities have the right to request and receive reasonable accommodation. Please call the Department of Chemical Engineering and Materials Science at 355-5135 at least one day prior to the seminar; requests received after this date will be met when possible.
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Department:
Electrical and Computer Engineering
Name:
Dong Chen
Date Time:
Tuesday, July 25, 2023 - 10:00am
Location:
Zoom
Announcement:
ABSTRACT
Advisor: Dr. Zhaojian Li
Recently, autonomous systems such as robots and autonomous vehicles are emerging as promising solutions to improve efficiency and overcome the global labor shortage. These systems are often able to operate independently and with high scalability. However, their control presents unique challenges due to the high dimensionality of their state spaces and the complexity of interactions between their various components. Conventional control methods often struggle to manage real-time control for large-scale autonomous systems due to the inherent complexity and unpredictability of these systems. Fortunately, reinforcement learning (RL) algorithms, especially multi-agent reinforcement learning (MARL), have emerged as effective solutions, addressing the complexities of autonomous system control through their adaptive online capabilities and their proficiency in solving intricate problems. In this thesis, three distinct deep MARL algorithms are explored for efficient and scalable autonomous system control with large-scale autonomous agents. To demonstrate the effectiveness of these approaches, we test these MARL algorithms on practical and real-world applications such as power grids and autonomous driving.
In the first algorithm, an efficient and scalable MARL framework is developed specifically for dynamic traffic scenarios, where the communication topology can be time-varying. This framework leverages parameter sharing and local rewards to encourage cooperation between agents, while still maintaining impressive scalability. To significantly reduce the collision rate and expedite the training process, a novel priority-based safety supervisor is incorporated into the framework. Furthermore, a gym-like simulation environment is developed and open-sourced with three different levels of traffic densities. Comprehensive experimental results show that the proposed MARL framework consistently outperforms several state-of-the-art benchmarks and shows its significant potential for use in the control of autonomous systems in dynamic environments.
In our second exploration, we propose a fully-decentralized MARL framework for Cooperative Adaptive Cruise Control (CACC). This approach differs substantially from the conventional centralized training and decentralized execution (CTDE) method. Here, each agent makes decisions based solely on its local observations and individual rewards without the need for a central controller. To address the non-stationarity issues inherent in systems with partial observability, we further introduce a quantization-based communication protocol to enhance communication efficiency by applying random quantization to the messages being communicated and ensuring that critical information is transmitted with minimized bandwidth usage. We evaluate this approach in two distinct CACC environments, showing that our proposed approach outperforms existing approaches in both control performance and communication efficiency.
In our third exploration, we propose an efficient MARL algorithm tailored specifically for cooperative control within power grids. Specifically, We focus on the decentralized inverter-based secondary voltage control problem inherent in distributed generators (DGs) and formulate it as a cooperative MARL problem. We then introduce a novel on-policy MARL algorithm, named PowerNet, where each agent (i.e., each DG) learns a control policy based on (sub-)global reward, as well as encoded communication messages from its neighbors. Furthermore, a novel spatial discount factor is introduced to mitigate the effect of remote agents, expedite the training process and improve scalability. Moreover, a differentiable, learning-based communication protocol is developed to strengthen collaboration among neighboring agents. In order to facilitate training and evaluation, we develop and open-source PGSim, a highly efficient, high-fidelity power grid simulation platform. Our experimental results in two microgrid setups demonstrate that PowerNet not only outperforms the conventional model-based control method but also surpasses several state-of-the-art MARL algorithms.
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Department:
Biomedical Engineering
Name:
Kylie Smith
Date Time:
Thursday, July 20, 2023 - 9:00am
Location:
1404 Interdisciplinary Science and Technology Building and Zoom
Announcement:
ABSTRACT
Advisor: Prof. Kurt Zinn
Molecular imaging is a critical tool for the management of neurodegenerative disease. In particular, positron emission tomography (PET) has provided new ways to identify distinct subtypes in Alzheimer’s Disease, inform disease management, and monitor treatment progress. However, the power of PET imaging is challenged by limitations to accessibility that hinder its adoption. Opportunities to reduce risk of failure and improve the efficiency of PET research are of high priority, given the high costs of conducting a PET study and the urgent need for improved imaging techniques and interventions. This dissertation describes the design, development, and implementation of custom research tools to improve efficiency for pre-clinical PET imaging. A modular multi-rodent imaging bed was designed and validated for high throughput PET/MR, then de-risked for commercialization. Commercialization activities included evaluation of candidate materials for interference in pre-clinical imaging modalities, a value-in-use study, and incorporation of desirable features identified through informational interviews with end users. Anatomically derived 3D-printed phantoms were used to develop methods to track nose-to-brain transfer by PET, which were then applied in nonhuman primates. Using this approach, we were able to sensitively quantify the distribution of F-18-FB-insulin throughout the brain of Cynomolgus Macaques following nose-to-brain delivery. Clinically relevant dosing tools were prioritized to facilitate rapid translation to humans for evaluation of nose-to-brain insulin as a therapeutic for Alzheimer’s Disease. Together, these methods are hoped to reduce barriers to participation in PET neuroimaging research and help scientists get started or get farther with the resources they have available.
Persons with disabilities have the right to request and receive reasonable accommodation. Please call the Department of Biomedical Engineering at 884-6976 at least one day prior to the seminar; requests received after this date will be met when possible.
Department:
Electrical and Computer Engineering
Name:
Shivam Bajaj
Date Time:
Wednesday, July 19, 2023 - 1:00pm
Location:
3112 Engineering Building
Announcement:
ABSTRACT
Advisor: Dr. Shaunak D Bopardikar
The advancement in technology, especially Unmanned Aerial Vehicles (UAVs) or drones, has helped mankind in many aspects of everyday life, such as environmental monitoring and in surveillance. However, an easy access to UAV technology has spurred its malicious use, leading to numerous attempts of flying UAVs into restricted areas or in public places. One possible way to counter against such adversarially intruding UAVs is to tag or disable them before they reach a specified location by using superior drones. But the problem of how to plan the motion of these drones, i.e., designing algorithms that have provable guarantees on the numbers of adversarial UAVs that can be disabled has remained an open problem.
This dissertation addresses design of control strategies and online algorithms, i.e., algorithms that do not have information about the intruders a priori, for drones to pursue and disable one or many intruders and is divided into two parts. The first part involves many, possibly infinite, intruders that move directly towards a region of interest. For this scenario, we design decentralized as well as cooperative online algorithms with provable worst-case guarantees for 1) a single drone defender, 2) a team of homogeneous defenders, and 3) a team of heterogeneous defenders. The aim of such defender drones is to capture as many intruders as possible that arrive in the environment. To quantify how well the algorithms perform in the worst-case, we adopt a competitive analysis technique. In particular, the algorithms designed in this dissertation exhibit a finite competitive ratio, meaning that the performance of an online algorithm is no worse than a finite value determined in this dissertation. We also determine fundamental limits on the existence of online algorithms with finite competitive ratios.
In terms of heterogeneity, the first part addresses drones of different capabilities as well as motion models, such as a drone and a turret operating in the same environment. The second part of this dissertation considers coupling between the motion. Specifically, this part considers a turret or a laser attached to a drone. The drone is modelled as a planar Dubins vehicle and the laser with a finite range, which is attached to the Dubins vehicle, can rotate clockwise or anti-clockwise. We design an optimal control strategy for both the Dubins vehicle and the laser such that a static target, located in the environment, is tagged in minimum time. By applying Pontryagins maximum principle, we establish cooperative properties between the laser and the Dubins vehicle. We further establish that the shortest path must lie in a family of 13 candidate paths and characterize the solutions to all of these types.
Department:
Chemical Engineering and Materials Science
Name:
Genzhi Hu
Date Time:
Tuesday, July 11, 2023 - 10:00am
Location:
3540 Engineering Building
Announcement:
ABSTRACT
Advisor: Dr. Jason D. Nicholas
Reliable dissimilar material bonding is crucial in various fields, and the silver-nickel brazing technique has emerged as a promising method for joining ceramics to stainless steel. This technique offers improved mechanical bonding strengths and enhanced longevity compared to the commonly used Ag-CuO reactive air brazes. Additionally, this Particle Interlayer Directed Wetting and Spreading (PIDWAS) technique can also be used to prepare silver circuits on a variety of substrates that cannot normally be wet by molten silver. However, there is a lack of understanding regarding the mechanical and electrical behavior of circuits or current collectors produced using this technique. Furthermore, its applicability to aluminum containing stainless steel and the feasibility of using alternative interlayer materials remain uncertain.
To address these gaps, this dissertation focuses on investigating the mechanical and electrical performance of Ag-Ni circuits created through the PIDWAS technique. The bonding strength between alumina substrates is examined and compared to commercially available silver pastes such as Heraeus C8710 and DAD-87. The sheet resistivity on alumina and contact resistivity on lanthanum strontium manganite are evaluated to assess the electrical properties of Ag-Ni current collectors. The findings demonstrate that PIDWAS-produced Ag-Ni layers exhibit better overall performance than conventional Ag contact pastes for circuit and current collector applications.
Furthermore, this research explores the feasibility of utilizing the Ag-Ni PIDWAS brazing technique for aluminum containing stainless steel and investigates the mechanical, electrical, and durability aspects of the resulting braze joints. The braze joints are comprehensively evaluated under various conditions, including as-produced, air annealed, reduction-oxidation (redox) cycled, and rapid thermal cycled states. The results indicate that Ag-Ni brazes effectively getter and stabilize unwanted aluminum from the substrate, highlighting its potential for applications involving aluminum containing stainless steel.
Additionally, a novel PIDWAS brazing technique using Ag-Pt is introduced in this work. The mechanical and electrical performance, as well as the microstructure changes of Ag-Pt brazes, are evaluated in as-produced, air annealed, redox cycled, and rapid thermal cycled conditions. The results demonstrate that Ag-Pt brazes outperform Ag-Ni brazes in oxidizing environments. The potential application of Ag-Pt brazes in other systems is also discussed. In summary, this work demonstrates that 1) different PIDWAS interlayer materials can be used to promote the wetting and spreading of molten silver, and 2) these interlayers can also be used to chemically getter undesirable surface-segregating substrate components.
Persons with disabilities have the right to request and receive reasonable accommodation. Please call the Department of Chemical Engineering and Materials Science at 355-5135 at least one day prior to the seminar; requests received after this date will be met when possible.
Email sandra@msu.edu for Zoom information
Department:
Computer Science and Engineering
Name:
Jamell Anthony Dacon
Date Time:
Monday, July 3, 2023 - 1:00pm
Location:
Zoom
Announcement:
ABSTRACT
Advisor: N/A
Natural language processing (NLP) is a subfield of artificial intelligence (AI) and has become increasingly prominent in our everyday lives. NLP systems are now ubiquitous as they are capable of identifying offensive and abusive conversational content and hate speech detection on social media platforms, voice and speech recognition and transcription, news recommendation, dialogue systems and digital assistants, language generation, etc. Yet, the benefits of these language technologies do not accrue evenly to all of its users leading to harmful social impacts as NLP systems reproduce stereotypes or fallacious results. Most AI systems and algorithms are data driven and require natural language data upon which to be trained. Thus, data is tightly associated to the functionality of these algorithms and systems. These systems generate complex social implications i.e., displaying human-like social biases (e.g. gender bias) that induce technological marginalization and increased feelings of disenfranchisement.
Throughout this thesis, I argue that how harms arise in NLP systems and who is harmed by these biases, can only be conceptualized and understood at the intersection of NLP, justice and equity (e.g., Data Science for Social Good), and the coupled relationships between language and both social and racial hierarchies. I propose to address three questions at this intersection: (1) How can we conceptualize and quantify such aforementioned harms?; (2) How can we introduce a set of measurements to understand "bias" in NLP systems}; and (3) How can we quantitatively and qualitatively ensure "fairness" in NLP systems?}.
To address these pertinent question, we attempt differentiate the two consequences of predictive bias in NLP: (1) outcome disparities (i.e., racial bias) and (2) error disparities (i.e., poor system performance) to explicate the importance of modeling social factors of language by exploiting NLP tools to examine predictive biases of both binary gender-specific (male and female) and LGBTQIA2S+ representations, and on an English language variety, i,e., African American English (AAE). Language reflects society, ideology, cultural identity, and customs of communicators, as well as their values. Therefore, natural language data, culture and systems are intertwined with social norms.
Nevertheless, social media and online services contain rich textual information on topics surrounding ethnicity, gender identity and sexual orientation--members of the LGBTQIA2S+ community and language (e.g., AAE). This facilitates the collection of large-scale corpora to study social biases in NLP systems in hopes of reducing stigmatization, marginalization, mischaracterization, or erasure of dialectal languages and its speakers, pushing back against potentially discriminatory practices (in many cases--- discriminatory through oversight more than malice). In this thesis, I propose several studies to minimize the gaps between gender, race and NLP systems' performance within the scope of the three aforementioned questions. In order to enable in-depth conversations about what kinds of system behaviors are harmful, in what ways, to whom, and why; I will allude to three case studies, (1) Gender and Sexual Identities, Orientations and Expressions, (2) Language, Race and Culture divided into folds, and conclude with the (3) Gender, Race, Language and Social Justice referencing five of my published works accepted to top-tier conferences that engage with social factors of language, affected communities and NLP systems.
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Department:
Mechanical Engineering
Name:
Guangchao Song
Date Time:
Friday, June 23, 2023 - 2:00pm
Location:
Zoom
Announcement:
ABSTRACT
Advisor: Dr. Patrick Kwon
Surface finishing is one of the most critical manufacturing processes as it influences the surface qualities, improving the corrosion and fatigue resistance of a product. Among many available surface finishing technologies, Magnetic-Field Assisted Finishing (MAF) is a promising finishing process that utilizes a slurry mixture made of ferromagnetic and abrasive particles in a liquid medium, also known as a brush. The brush attached to a magnetic tool directly interacts with the surface of a workpiece and removes surface imperfections and defects to achieve a desired surface finish. Due to the MAF’s recent inception, there is still a lack of understanding regarding the application of MAF on various metallic materials, large workpiece areas, and freeform geometries. In the presented study, optimal processing parameters were investigated and obtained on mold steels and sheet metal. The optimized parameter settings significantly improve the final surface roughness, from 434 nm to 26 nm for HP4M mold steel, from 1056 nm to 38 nm for chrome-coated sheet metal, and from 507 nm to 45 nm for AISI S7 steel, respectively. Subsequently, the identified parameters were implemented in the continuous setup that successfully finished the sheet metal samples with a larger area. This application of optimized parameters in the continuous setup enhances the effectiveness and efficiency of the overall finishing process. Finally, the study yielded the appropriate brush constituents to improve the efficiency of the MAF process, and simulations were conducted to explore the effects of the iron particle size on the brush constituents. The investigations demonstrated that the larger iron particles are subjected to a more powerful magnetic force. The current status of the MAF process is premature to be implemented in practical industrial applications. This work determined the optimal contents of the brush constituents which will contribute to making the MAF process more practical.
Persons with disabilities have the right to request and receive reasonable accommodation. Please call the Department of Mechanical Engineering at 355-5131 at least one day prior to the seminar; requests received after this date will be met when possible.
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Department:
Mechanical Engineering
Name:
Akshay Shailendra Pakhare
Date Time:
Wednesday, June 21, 2023 - 2:00pm
Location:
3540 Engineering Building and Zoom
Announcement:
ABSTRACT
Advisor: Dr. Siva Nadimpalli
The capacity and energy density of the current rechargeable batteries are not sufficient to meet the future energy storage demands. One of the strategies to solve this issue is to replace the existing electrode material with high performance materials. The commercial electrodes are composites which consist of active material particles (that are responsible for energy storage), a polymer binder with conductive additives which holds all the particles together and provides electrical network. Both the negative (i.e., anode) and positive (i.e., cathode) electrodes are composites. Graphite is the conventional active material in the anode, and the high performance materials such as Si, Sn, Ge are being considered as a replacement for graphite due to their energy density. For example, Si offers nearly 10 times more capacity compared to graphite (i.e., 3579 mAh/g compared to 372 mAh/g). However, these high-performance materials exhibit poor cyclic performance and undergo significant capacity fade, i.e., reduction of usable capacity with cycling. To address these issues, the dissertation has two broad goals: 1) to develop novel experimental methods for interface fracture characterization in batteries and 2) to develop a comprehensive multiphysics model for rechargeable batteries.
Capacity fade occurs in batteries mainly due to two different mechanisms: chemical and mechanical processes. The chemical process involves loss of active ions due to irreversible reaction resulting in the formation of a passivation layer called the solid electrolyte interphase (SEI). The mechanical process involves fracture of active material particles or failure of the interface between binder and active material particles. In this work, the focus is on the mechanical process and especially the failure of binder/active material interfaces in the negative electrode (i.e., anode). Although the binder/active material interfaces exist in both anode and cathode, large volume changes of anode materials make the interface failure a critical issue for anodes. For example, the most promising next generation anode material Si undergoes nearly 270% volume change during electrochemical cycling. This level of volume change causes interface failure and loss of electrical network in the electrode resulting in capacity fade. In spite of its importance, there is a lack of understanding on the interface failure in rechargeable batteries. In this study we developed a novel experimental method to characterize the interface failure behavior in lithium-ion battery system. Specifically, PVdF polymer was used as a binder and Si as active material in this model system. Samples for fracture characterization were prepared by depositing PVdF on Si substrate followed by a series of nanofabrication processes. The blister test samples fabricated in this process were tested in a novel electrochemical cell in conjunction with an in-house optical system based on Michelson interferometer principle. The samples were pressurized until the PVdF film delaminated from Si substrate. The mechanical response of the pressurized film was measured, and the PVdF/ Si interface fracture was characterized in terms of critical energy release rate Gc. The effect of thermal oxide (i.e., SiO2) on the interface failure behavior was investigated. Further, the same setup was used to determine the effect of galvanostatic electrochemical cycling of Si on the interface failure behavior.
The significant volume change behavior of the next generation high-performance materials during electrochemical cycling can generate stresses as high as 1 GPa. These high stresses in high-performance material undergoing large deformation affects the diffusion of ions in active material particle, affects the voltage of a battery, and also affects the electrochemical kinetics at the electrode/electrolyte interface. Theoretical models are necessary to develop high energy density and durable batteries for future energy storage demands. The current battery models account for the stress-potential coupling but assume steady state electrochemical kinetics. However, transient electrochemical kinetics are required to capture rate dependent electrochemical behavior usually observed in batteries during operation, i.e., when current is drawn at various rates during discharge process of a battery. Also, the existing models were developed based Li-ion batteries, and there is a need to extend the models to other battery systems (i.e., other chemistries such as Na-ion). Therefore, we have developed a theory for Li-ion and Na-ion electrode active materials. A diffusion-deformation model with transient electrochemical kinetics was developed and implemented in a finite element package.
By combining the experimental and modeling tasks outline above, this dissertation successfully characterized and simulated the failure behavior of binder/active material interface and attempted to predict the capacity fade behavior in rechargeable batteries.
Persons with disabilities have the right to request and receive reasonable accommodation. Please call the Department of Mechanical Engineering at 355-5131 at least one day prior to the seminar; requests received after this date will be met when possible.
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Department:
Mechanical Engineering
Name:
Ahmed Yousef
Date Time:
Monday, June 19, 2023 - 9:30am
Location:
Zoom
Announcement:
ABSTRACT
Advisors: Dr. Maryam Naghibolhosseini and Dr. Mohsen Zayernouri
Adductor laryngeal dystonia (AdLD) is a neurological voice disorder that disrupts laryngeal muscle control during running speech. Diagnosis of AdLD is challenging because of the limited scientific consensus on accurate diagnostic criteria as it can mimic voice features of other voice disorders. The use of laryngeal high-speed videoendoscopy (HSV) as a powerful tool to capture the detailed vocal fold (VF) vibrations has been almost nonexistent to study AdLD and limited to sustained phonation, not connected speech in which AdLD’s symptoms manifest. The present dissertation aims to address the previous literature gap using HSV and provide, for the first time, quantitative analysis for the impaired vocal function in AdLD during connected speech. To accomplish this, HSV recordings were collected from vocally normal adults and AdLD patients during connected speech. Five different studies were implemented in order to analyze and extract clinically relevant information from these recordings.
The first study investigated the differences between AdLD and normal controls based on evaluating running speech durations in HSV over which VFs were visually obstructed by excessive movements of laryngeal tissues. To facilitate these analyses, a deep learning tool was developed to automatically classify HSV frames in terms of detecting visual obstructions in the VF images. The second study provided a new image segmentation tool for detecting VF edges during running speech in HSV. This tool was developed using a unique combination of the active contour modeling method and a machine-learning based method (k-means clustering) to segment VF edges in HSV kymograms. The third study developed a quantitative representation of VF dynamics in AdLD in running speech using HSV. A deep learning technique was used based on the tool developed in study two to segment the glottal area/edges and extract the glottal area waveform from the HSV recordings for analysis. The fourth study analyzed the pathological vocal function of AdLD during phonation onset and offset in connected speech using HSV. An automated approach was developed and validated with manual analysis to measure and compare the glottal attack and offset times between AdLD group and normal controls. Study five presented a one-mass lumped model that can estimate glottal area waveform and biomechanical characteristics of VFs based on HSV data.
The results of study one showed the accurate detection of the visual obstructions of the VF frames – facilitating the study of laryngeal activities in AdLD. The findings revealed that AdLD group exhibited longer durations of obstructions – making this measure a potential candidate for AdLD assessment. Also, indicating parts of connected speech that provide an unobstructed view of VFs allows for developing optimal passages for precise HSV examination and disorder-specific clinical voice assessment protocols. Study two and three demonstrated promising performance of the proposed automated tools to detect VF edges and analyze glottal area waveforms. These accurate techniques overcame the challenges involved in HSV analysis including the poor image quality during running speech and the excessive laryngeal maneuvers of AdLD. Future research should benefit from these newly developed automated tools for HSV analysis of VF vibrations in running speech to explore diagnostically relevant information in both vocally normal adults and AdLD. The findings of the fourth study revealed the accurate measurements of the glottal attack and offset times using the developed automated technique. The measurements showed significant longer attack time in AdLD and more variability of the attack and offset times in AdLD due to the irregularity of the VF vibratory behavior in this disorder. Accordingly, glottal attack time might be a compelling measurement of the severity of AdLD, which can be further investigated in future using the developed tool with larger sample size and, even for different voice disorders. Obtaining such measures in running speech opens up new lines of research to explore the clinical significance of these measurements and address the diagnostic challenges in AdLD. In the last study on modeling, the results show the successful optimization of the developed one-mass model to closely capture the characteristics of VF vibrations observed in the HSV running speech sample. The study uncovered the potential of this simplified model to estimate biomechanical properties of VFs with minimal computational cost non-invasively– paving the path for future research to utilize this model for analyzing connected speech samples and study the impaired VF dynamics in AdLD.
Persons with disabilities have the right to request and receive reasonable accommodation. Please call the Department of Mechanical Engineering at 355-5131 at least one day prior to the seminar; requests received after this date will be met when possible.
Email sandra@msu.edu for Zoom information
Department:
Computer Science and Engineering
Name:
Tian Xie
Date Time:
Tuesday, June 6, 2023 - 9:00am
Location:
Zoom
Announcement:
ABSTRACT
Advisor: N/A
Nowadays, the world has been mobilized. By 2021, mobile networks have connected 23.4 billion mobile devices and provided 5.3 billion users with ubiquitous mobile services. People can use the cellular network for voice and text communication, accessing the Internet, conducting monetary transactions, etc. With the development of cellular network, lots of new services continue to be added and provided by the operators.
Considering such a great amount of devices and people connected, it is very important to secure mobile networks. However, it is challenging to secure mobile networks for the complicated networks, rapidly evolving technology, a wide range of devices, and distributed nature of the network. Any vulnerability in mobile networks can threaten the entire wireless ecosystem, which is the motivation of this dissertation to conduct the security study that identifies and addresses the security vulnerabilities in the mobile networks for making it secure and dependable. In this dissertation, three studies about the most essential cellular network services (i.e., IP Multimedia Subsystem services, wireless IoT services, Internet Application Services) are included as follows.
In the study of cellular network IP Multimedia Subsystem (IMS) security, we conduct the first security study on the operational VoWi-Fi (Voice over Wi-Fi) services in three major U.S. operators’ networks using commodity devices. We disclose that current VoWi-Fi security is not bullet-proof and uncover three vulnerabilities. Two proof-of-concept attacks are devised and both of them can bypass the existing security defenses. We propose the solutions to address all discovered vulnerabilities.
In the study of wireless IoT services, we conduct the security study on both cellular and Wi-Fi IoT services. However, this dissertation only introduces our empirical security study on cellular IoT service charging over the major U.S. carriers. We discover security vulnerabilities and analyze their root causes. To assess their real-world impact, proof-of-concept attacks are devised. In the end, we analyze the challenges in addressing these vulnerabilities and develop an anti-abuse solution to mitigate attack incentives. The solution is standard-compliant and can be used immediately in practice.
In the study of Internet Application Service (IAS), we propose a novel security framework, MPKIX, designated as Mobile-assisted PKIX (Public-Key Infrastructure X.509). MPKIX secures both IAS providers and users by leveraging the broadly used PKIX services and mobile networked systems. It provides a reliable and privacy protection user verification mechanism and largely mitigates the possibility of ID theft attacks and benefits other involved parties.
In conclusion, the security research on the cellular network services can help secure mobile ecosystem, facilitate the global deployment, and head toward the secure and dependable mobile networks.
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Department:
Mechanical Engineering
Name:
Hoi Ho Hawke Suen
Date Time:
Monday, June 5, 2023 - 1:00pm
Location:
Zoom
Announcement:
ABSTRACT
Advisor: Dr. Patrick Kwon
Binder Jetting has been a promising additive manufacturing (AM) technique since it was first patented 30 years ago. The nature of its densification process is similar to powder metallurgy. It provides unique benefits compared to other methods, such as powder bed fusion and direct energy deposition, such as minimal residual stress, cost-efficient scaling up, higher powder reusability, etc. However, for most metal materials, the final density obtainable from the binder jetting process is low compared to other AM methods, making the technique only suitable for a few materials or limited in applications such as prototyping. This study implemented liquid phase sintering and a linear packing model to achieve high-density electrical steel with a pure elemental and pre-alloyed powder approach. Boron and silicon were used as additives to form a eutectic composition with iron to achieve liquid phase sintering. The elemental powder approach investigated the effect of boron and silicon on mechanical and magnetic properties with the ANOVA technique. The alloyed powder approach with boron & silicon as additives achieved a final density of 7.39 g/cc (98.4% of the theoretical density 7.51 g/cc), 8489.75 in maximum permeability, 0.053 Ws/kg for hysteresis loss at 1.5T, and a total loss of 34.39 W/kg for the frequency at 400Hz, 0.5T. Compared to Cramer et al. [1] of 7.31 g/cc in density (97.3% of the theoretical density of 7.51 g/cc), 10500 in maximum permeability, and 62.85 W/kg at 400Hz, 0.5T. With the processing parameters implemented, a stator with internal cooling channels was made with the joining technique. It shows that binder jetting is also a promising technique for fabricating electrical steels without limiting the preferred orientation offered by sheet lamination and a higher density than soft magnetic composites.
[1] Cramer, C. L., Nandwana, P., Yan, J., Evans, S. F., Elliott, A. M., Chinnasamy, C., & Paranthaman, M. P. (2019). Binder jet additive manufacturing method to fabricate near net shape crack-free highly dense Fe-6.5 wt.% Si Soft magnets. Heliyon, 5(11). https://doi.org/10.1016/j.heliyon.2019.e02804
Persons with disabilities have the right to request and receive reasonable accommodation. Please call the Department of Mechanical Engineering at 355-5131 at least one day prior to the seminar; requests received after this date will be met when possible.
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Department:
Chemical Engineering and Materials Science
Name:
Gouree Kumbhar
Date Time:
Friday, May 26, 2023 - 1:00pm
Location:
3540 Engineering Building and Zoom
Announcement:
ABSTRACT
Advisor: Dr. Robert C. Ferrier, Jr.
Epoxides are a promising polymer materials platform because of their diverse functionality, ease of synthesis, availability, and ring strain favoring polymerization. Recently reported mono(μ-alkoxo)bis(alkylaluminum) (MOB) based polymerization technique provides controlled molecular weight polymers for wide variety of functional epoxides without chain transfer. We want to use this facile polymerization platform to create polymers with orthogonally addressable pendant groups to precisely tune polymer properties. Specifically, this work focuses on incorporation of charged moieties through post polymerization modification of functional pendent groups to investigate their transport and self-assembly properties.
We have demonstrated control over molecular weight, composition, and architecture via copolymerization of propargyl glycidyl ether (PGE) and epichlorohydrin (ECH), with functional alkyne and chloromethyl groups respectively. Molecular weights up to 100 kg/mol with narrow distributions were achieved. Copolymer composition was varied by incorporating increasing ratios of PGE (20-80%) in the polymerization feed. In situ 1H NMR kinetic study was performed using two different systems that is MOB and separate initiator-catalyst to determine reactivity ratios. With the use of Meyer-Lowry method reactivity ratios were calculated as rPGE = 0.69 and rECH = 1.43 for MOB system, and rPGE= 0.72 and rECH= 1.48 for separate initiator-catalyst system. So, in both cases rPGE×rECH ≈1 which confirms the statistical nature of the copolymer with preferred addition of ECH to growing chain end regardless of polymerization technique.
These precursor copolymers were further modified with various charged groups such as imidazole and sulfonate via orthogonal chemistry through the chloromethyl and alkyne moieties. This will be beneficial in achieving tuned compositional control of structure–property relationships in a polyether materials platform. These functional polyethers were then used to create economical crosslinked networks to prepare amphoteric ion exchange membranes (AIEMs). Nafion ion exchange membranes have been used in vanadium redox flow batteries (VRFB) applications owing to their good ionic conductivity and excellent chemical and mechanical stability. But nafion’s high cost, excessive swelling and low ion selectivity limits its use for commercialization. AIEMs have potential for preventing vanadium ion penetration thus increasing ion selectivity. Membranes were synthesized by grafting of novel ECH and PGE-based charged copolymer S-P(PGE-stat-ECH) to the PVDF-co-HFP membrane matrix. We studied the physicochemical, electrochemical, and surface properties of these membranes to investigate candidacy of this novel membrane for VRFB application.
Next, we used a homopolymer of allyl glycidyl ether (PAGE) as a unifying platform for polyelectrolyte design. With the use of click chemistry we created polyether based polyanions and polycations to study effect of charge and molecular weight on self-assembly. We studied effect of NaCl and LiCl salt as well on polyelectrolyte self-assembly with varying polyanions to polycation ratios. Coacervation formations was studied using absorbance measurements on UV-vis spectrophotometer. With the use of MOB polymerization platform, we can synthesize variety of polymers, and this will be useful in exploring effects of counter-ions, polymer architecture, charge densities in future. Our synthetic platform provides control over different governing parameters separately which will be impactful in giving insights on polyelectrolyte self-assembly from fundamental standpoint. We expect the broader impacts of this research to encompass innovation in polyelectrolyte design and application.
In conclusion, we demonstrated control over factors such as molecular weight, polymer architecture, charge density, monomer sequence, and counter-ions independently with the use of this platform. We have utilized these materials to further develop AIEMs for electrochemical application and to study charged polymer self-assembly.
Persons with disabilities have the right to request and receive reasonable accommodation. Please call the Department of Chemical Engineering and Materials Science at 355-5135 at least one day prior to the seminar; requests received after this date will be met when possible.
Department:
Electrical and Computer Engineering
Name:
Ibrahim M. Allafi
Date Time:
Wednesday, May 24, 2023 - 10:00am
Location:
2219 Engineering Building and Zoom
Announcement:
ABSTRACT
Advisor: Dr. Shanelle N. Foster
Permanent magnet synchronous machines (PMSMs) are widely used in various industries such as transportation, manufacturing and renewable energy. The simple structure of direct torque control (DTC) coupled with its encoderless operation and fast dynamics are of great interest for PMSMs. Nevertheless, the occurrence of faults, such as turn-to-turn short circuit, high resistance contact, static eccentricity and partial demagnetization, remains a concern. Faults can prevent smooth drive operation of DTC and potentially lead to catastrophic losses if not detected and mitigated in their early phases. Hence, fault diagnosis of DTC driven PMSMs is paramount to ensuring reliable drive operation.
An essential aspect of developing effective fault diagnosis is to understand the impact of faults on drive operation and its corresponding reaction. A comprehensive examination of the nonlinear behavior of flux and torque hysteresis comparators in DTC driven PMSMs provides insight. It is shown that DTC can tolerate low-severity faults within the controller bandwidth while continuing to operate normally. However, when flux and torque errors exceed the bandwidth, DTC counteracts by introducing negative sequence voltages and torque angle variations which impacts fault diagnosis and control under faulty conditions.
Many existing fault diagnosis methods are based on field oriented control (FOC); however, it is not well understood how these methods translate to DTC driven PMSMs. Machine Voltage Signature Analysis (MVSA) is the most commonly used approach for fault diagnosis in electric machines. However, the use of DTC introduces challenges for adoption MVSA due to its nature of compensation, structure and regulation principle. A novel fault diagnosis approach for DTC driven PMSMs is developed. This approach maintains the simple structure of DTC, removes the need for complex signal processing tools, and relies solely on the available signals in the drive. The occurrence of faults results in unique deviations in the direction and magnitude of the commanded voltages in the stator flux linkage (MT) frame enabling fault detection, classification, and severity assessment.
Ultimately, the fault diagnosis algorithm used for inverter driven PMSMs should be effective and applicable irrespective of the control type. A comprehensive fault diagnosis approach is developed based on active and reactive power signature analysis. This data driven algorithm uses spectral components of the power signals as fault indicators. It is shown that this developed algorithm is capable of fault diagnosis in both FOC and DTC driven PMSMs.
The reliability of inverter driven PMSMs depends on the ability to monitor its state of health during operation. It is necessary to detect that a fault occurred, identify fault type as well as estimate its severity. Classification algorithms are used to separate fault types and estimate fault severity. Here, the performance of three classification algorithms is evaluated for inverter driven PMSMs. The classification algorithms are linear discriminate analysis (LDA), k-nearest neighbor (k-NN), and support vector machines (SVM). The SVM classifier is shown to be a highly effective method for detecting and classifying faults in PMSMs controlled by either drive, even with limited training data and high noise levels.
Journal Publications:
1. I. M. Allafi and S. N. Foster, “Condition Monitoring Accuracy in Inverter-Driven Permanent Magnet Synchronous Machines Based on Motor Voltage Signature Analysis,” Energies, vol. 16, no. 3, p. 1477, Feb. 2023, doi: 10.3390/en16031477.
2. A. Aggarwal, I. M. Allafi, E. G. Strangas and J. S. Agapiou, "Off-Line Detection of Static Eccentricity of PMSM Robust to Machine Operating Temperature and Rotor Position Misalignment Using Incremental Inductance Approach," in IEEE Transactions on Transportation Electrification, vol. 7, no. 1, pp. 161-169, March 2021, doi: 10.1109/TTE.2020.3006016.
Journals under Review:
1. I. M. Allafi and S. N. Foster, “Power Signature Based Fault Diagnosis of Inverter-Driven Permanent Magnet Synchronous Machines,” in IEEE Transactions on Industry Applications, 2023
Conference Proceedings:
1. I. M. Allafi and S. N. Foster, "Condition Monitoring of Direct Torque Controlled Permanent Magnet Synchronous Machines," 2022 IEEE Energy Conversion Congress and Exposition (ECCE), Detroit, MI, USA, 2022, pp. 1-7, doi: 10.1109/ECCE50734.2022.9948136.
2. I. M. Allafi and S. N. Foster, "On the Accuracy of Frequency Based Fault Diagnosis for DTCdriven PMSM," 2022 International Conference on Electrical Machines (ICEM), Valencia, Spain, 2022, pp. 1628-1634, doi: 10.1109/ICEM51905.2022.9910619.
3. I. M. Allafi and S. N. Foster, "Fault Detection and Identification for Inverter-Driven Permanent Magnet Synchronous Machines," 2021 IEEE 13th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED), Dallas, TX, USA, 2021, pp. 358-364, doi: 10.1109/SDEMPED51010.2021.9605501.
Email Vincent Mattison or Advisor for Zoom information
Department:
Computer Science and Engineering
Name:
Xiao Zhang
Date Time:
Tuesday, May 23, 2023 - 12:00pm
Location:
Zoom
Announcement:
ABSTRACT
Advisor: Dr. Li Xiao
Optical Wireless Communication (OWC) techniques are the potential alternatives of the next generation wireless communication. These techniques, for example, VLC (visible light communication), OCC (optical camera communication), Li-Fi, FSOC (free space optical communication), and LiDAR, are increasingly deployed in our daily life. To provide fast and secure wireless services, numerous OWC approaches use LED lamps as transmitters and photo diodes or cameras to receive light signals. However, present OWC approaches are constrained by slow speeds and limited usage cases. The primary goal of this thesis is to investigate the potentials on both the transmitter and receiver sides with designed effective strategies for boosting the data rate of OWC and extending their use scenarios from indoor to outdoor, terrestrial to non-terrestrial. In this paper, we study the possibilities of various spatial-temporal dimensions from 1D to 2D to 3D to 4D for optical wireless communication and enabled optical wireless sensing. We briefly introduce them below.
1D Spatial-Temporal Optical Wireless Communication. We found that compensation symbols, which are commonly used for fine-grained dimming, are not used for data transmission in OOK-based LiFi for indoor lighting and communication. We intend to demonstrate the LiFOD framework, which is installed on commercial off-the-shelf (COTS) LiFi systems, to increase the data rate of existing Li-Fi systems. We utilize compensation symbols, which were previously only used for dimming, to carry data bits (bit patterns) for enhanced throughput.
2D Spatial-Temporal Optical Wireless Communication. In our study about camera-based OWC (i.e., optical camera communication), we first investigate 2D rolling blocks spatial diversity in the camera imaging process rather than 1D rolling strips spatial diversity for optical symbol modulation. Our proposed RainbowRow overcomes the limitation of restricted frequency responses in traditional optical camera communication. We implement a low-cost RainbowRow prototype. We handle flickering and optical signal overlapping at the transmitter, as well as the robust decoding at the commercial camera in a variety of settings.
3D Spatial-Temporal Optical Wireless Communication. When compared to existing acoustic and RF-based approaches, underwater optical wireless communication appears promising due to its broad bandwidth and extended communication range. Existing optical tags (bar/QR codes) embed data in the plane with limited symbol distance and scanning angles. U-Star first exploits passive 3D optical identification tags for underwater navigation. We model 3D spatial diversity and utilize it to increase distance of data elements in our proposed UOID tags for simple and robust underwater navigation. To adapt to harsh underwater circumstances, we develop underwater denoising algorithms with CycleGAN, CNN based relative positioning, and real-time data parsing.
3D Spatial-Temporal Optical Wireless Sensing. The fourth project considers about the optical wireless enabled hand gesture reconstructing. The vision approaches compatible with time-consuming image processing adopt low 60 Hz location sampling rate (frame rate) for real-time hand gesture recognition. In this project, we propose RoFin, which first exploits 6 temporal-spatial 2D rolling fingertips for real-time 3D reconstructing of 20-joint hand pose. RoFin designs active optical labeling for finger identification and enhances inside-frame 3D location tracking via high rolling shutter rate (5-8 KHz). These features enable great potentials for enhanced multi-user HCI, virtual writing for Parkinson suffers, etc. We implement RoFin prototypes with wearable gloves attached with low-power single-colored LED nodes and commercial cameras.
4D Spatial-Temporal Optical Wireless Integrated Communication and Sensing. In the fifth project, we explore the integrated optical wireless communication and sensing/localization in the drone network. The existing centralized radio frequency control from a base station faces mutual interference and high latency, which will cause the localization error and lacks of on-site drone-to-drone interactions. Because of its high spatial multiplexing capability, LoS secure feature, broader bandwidth, and intuitive vision manner, optical camera communication (OCC) is considered as a potential alternative for sensing and communication in drone clusters. We propose PoseFly, a 4-in-1 AI assisted optical camera communication with drone identification, on-site localization, quick-link communication, and lighting for swarming drones.
These beneficial explorations of spatial-temporal in multi-dimensions around various applications demonstrate that the optical wireless communication can be the promising option as the next generation wireless network techniques.
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Department:
Computational Mathematics, Science and Engineering
Name:
He Lyu
Date Time:
Friday, May 19, 2023 - 10:00am
Location:
Zoom
Announcement:
ABSTRACT
Advisor: Dr. Rongrong Wang
In the fields of statistical and machine learning, one frequently encounters the task of analyzing high-dimensional data. Since high dimensionality poses a great challenge to traditional methods, new methods that are specifically designed for high-dimensional data have been developed. A promising approach to tackle the curse of dimensionality is to make prior assumptions on the data. In this dissertation, we focus on the low-intrinsic-dimensionality prior of the data, which assumes that the high-dimensional data lies around a low-dimensional manifold. In the special case when the manifold is a linear subspace, this prior reduces to the standard low-rank prior. The low-rank assumption underlies many popular statistical and machine learning algorithms, such as Principal Component Analysis and Singular Value Hard Thresholding.
The defense presentation will consist of two parts. The first part will explore the robustness of reconstruction under the low-rank prior for various applications. In particular, we analyze the fundamental perturbation problem of Singular Value Decomposition (SVD). Due to the significant importance of SVD in data science and its sensitivity to noise, studying its stability is crucial for the reliability of many machine learning algorithms that involve SVD. We establish a useful set of formulae for the sinΘ distance between the original and the perturbed singular subspaces. Following this, we further derive a collection of new results on SVD perturbation related problems.
In the second part, we employ the low-rank prior for manifold denoising problems. Specifically, we generalize the Robust PCA (RPCA) method to manifold setting and propose an optimization framework that separates the sparse component from the manifold under noisy data. It is worth noting that in this work, we generalize the low-rank prior to a more general form to accommodate data with a more complex structure, instead of assuming the data itself lies in a low-dimensional subspace as in RPCA, we assume the clean data is distributed around a low-dimensional manifold. Therefore, if we consider a local neighborhood, the sub-matrix will be approximately low rank. Theoretical error bounds are provided when the tangent spaces of the manifold satisfy certain incoherence conditions. And the efficacy of our method is demonstrated on both synthetic and real datasets.
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Department:
Electrical and Computer Engineering
Name:
Cristian Javier Herrera-Rodriguez
Date Time:
Wednesday, May 10, 2023 - 12:00pm
Location:
C103 Engineering Research Complex and Zoom
Announcement:
ABSTRACT
Advisor: Timothy Grotjohn
Diamond is one of the most promising semiconductor materials for high power and highfrequency electronic device applications because of its exceptional mechanical, electronic and thermal properties that include wide band-gap, high breakdown electric field, high carrier mobility and high thermal conductivity. All diamond Schottky diodes and field effect transistors, as well as diamond/gallium oxide heterojunction pn diodes, were designed with Sentaurus TCAD simulations and then fabricated and tested.
Schottky Barrier Diodes (SBDs) are unipolar devices formed with a potential barrier at a metal0semiconductor interface. SBDs are good for fast switching and they have a low voltage drop in the forward biased regime. Diamond based SBDs were fabricated on layered highly/lightly boron doped (p+/p- respectively) epilayers on diamond substrates. Tested diodes showed good behavior with some non-ideal characteristics. Simulations were done in Sentaurus with a nonideal metal-insulator-semiconductor interface for the Schottky contact to get agreement of the modeled and measured diode characteristics.
Diamond pn devices are promising for ultra-high voltage applications (>10kV), however diamond pn junctions have limitations due to (1) a high turn-on voltage (~5V) giving a significant on-state voltage drop and (2) n-type diamond having higher resistivity and poor ohmic contacts. An alternative n-type ultra-wide bandgap (UWBG) semiconductor with shallow donor dopants is β-Gallium Oxide (β-Ga2O3). Gallium oxide has gained significant attention due to its attractive properties like its wide bandgap (4.85eV) and high breakdown electric field in the range of 8 MV/cm. Diamond’s outstanding thermal properties can serve as a heat spreader for high power operation, which can compensate for the poor thermal conductivity of β-Ga2O3. The combination of p-type diamond and n-type Ga2O3 give the advantages of high thermal conductivity, good diamond p-type conduction, and good Ga2O3 n-type conduction. A pn junction model was developed in Sentaurus that included trap-assisted current flow at the heterojunction interface. Fabricated and tested p-type diamond and n-type Ga2O3 diodes are compared to simulations to understand the current flow mechanisms.
Diamond field effect transistors (FETs) can be built in various configurations including lateral metal-semiconductor FETs (MESFETs) and vertical junction FETs (JFETs), which are designed/simulated, fabricated and tested in this work. The MESFET was tested over a wide temperature range from 300 K to 700 K with the drain current almost constant from 425-700 K. Diamond material models of carrier ionization and mobility versus temperature were used in the Sentaurus simulations. A vertical JFET was designed/simulated and the fabrication processes were developed. The JFET showed gate control of the drain current, however the device leakage currents were high due to unwanted current conduction in selective area diamond growth regions.
Department:
Electrical and Computer Engineering
Name:
Luke Baumann
Date Time:
Wednesday, May 10, 2023 - 12:00pm
Location:
Zoom
Announcement:
ABSTRACT
Advisor: Dr. Shanker Balasubramaniam
Integral equations in Computational Electromagnetics (CEM) are one branch of diverse field. There are many methods to solve for electromagnetic scattering and transmission with boundary integral equations being one of the most efficient. This is due to only needing to discretize the surface of the object and leads to smaller, dense systems as opposed to the larger, sparse systems encountered in Finite Element Method (FEM). There are additional methods that combine boundary integral methods with FEM, namely Finite Element Boundary Integral (FEBI), which have the flexibility of using the more appropriate method as needed for a given region.
Within the subfield of boundary integral equations, there are many parts including the formulations, representation, testing, singularity treatment, numerical methods such as acceleration techniques, iterative and direct solvers, preconditioning, etc. In this thesis, I will present several new and existing formulations using the same formulation framework, demon strate how to perform the integrals for analytic and piecewise basis and testing functions, show how to modify acceleration techniques for a wide range of integral equations, and show results of analysis throughout as needed.
The new formulations are well-conditioned, free from traditional breakdowns, and comparable to existing state-of-the-art formulations. They share the majority of their implementation with the formulations they are compared against to limit any unintended comparisons.
Department:
Electrical and Computer Engineering
Name:
Jacob Hawkins
Date Time:
Thursday, May 11, 2023 - 12:00pm
Location:
3105 Engineering Building and Zoom
Announcement:
ABSTRACT
Advisor: Dr. Shanker Balasubramaniam
Integral equations are used to analyze scattering from electromagnetic fields incident upon a perfect electrically conducting (PEC) object. Some common formulations are the electric field integral equation (EFIE), magnetic field integral equation (MFIE), and combined field integral equation (CFIE). Each of these formulations has challenges. The operator in the EFIE is ill-conditioned, and the formulation is non-unique. The operator in the MFIE is well-conditioned, but the formulation is also non-unique. The CFIE (a weighted sum of the EFIE and MFIE) is also ill-conditioned, but the formulation is unique. Due to provable uniqueness, the CFIE is often used in scattering analysis for closed, PEC objects.
One approach to improve conditioning for the CFIE is to use well-known Calderón identities and precondition the EFIE with the EFIE. These identities prove the EFIE operator acting on the EFIE operator is equal to a sequence of second-kind MFIE type operators. The Calderón preconditioner is often constructed with a lossy wavenumber to preserve the uniqueness of the CFIE formulation. The EFIE acting on the EFIE is analytically well-behaved but fraught with difficulties once the equations are discretized using the Method-of-Moments technique. The crux of the problem is the EFIE operator maps a div-conforming function to a curl-conforming function. Quasi-curl-conforming-divergence-conforming basis sets such as Buffa-Christiansen functions are needed to properly discretize the formulation, and these functions require significant, additional computation compared to the divergence-conforming RWG functions often used to discretize the CFIE.
This thesis takes a different starting point to solve the scattering problem for PEC objects. Instead of the CFIE, the decoupled field integral equation (DFIE) and decoupled potential integral equation (DPIE) are used to avoid low-frequency and dense-mesh breakdown, topology breakdown, and resonances (all of which contribute to ill-conditioning) for PECs. Also, the operators in the DPIE and DFIE map curl-conforming functions to curl-conforming functions and divergence-conforming functions to divergence-conforming functions. However, these formulations are not generally well-conditioned at high frequencies.
The primary contribution of this thesis is a new set of Calderón identities which may be used to construct O(N) preconditioners for a unique and wideband well-conditioned formulation of the DPIE or DFIE constrained to PEC objects. The new formulations are accelerable with fast methods like the multi-level fast multipole method (MLFMM) and open the door to quick and accurate computation of scattered fields from multi-scale and electrically large PEC objects using only RWG functions.
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Department:
Mechanical Engineering
Name:
Farzaneh Tatari
Date Time:
Wednesday, May 10, 2023 - 9:00am
Location:
2555D Engineering Building and Zoom
Announcement:
ABSTRACT
Advisor: Dr. Hamidreza Modares
Identifying a high-fidelity model of nonlinear dynamic systems is a prerequisite for achieving desired specifications in any model-based control design technique. This is because most control design methods rely on the availability of an accurate model of the system dynamics. Coarse dynamics models without generalization guarantees typically induce controllers that are either overly conservative with poor performance or violate spatiotemporal constraints imposed on the system when applied to the true system.
This dissertation investigates the finite-time identification of deterministic and stochastic systems. First, a novel finite-time distributed identification method is introduced for nonlinear interconnected systems. A distributed concurrent learning (CL) based discontinuous gradient descent (GD) update law is presented to learn uncertain interconnected subsystems' dynamics by minimizing the identification error for a batch of previously recorded data collected from each subsystem as well as its neighboring subsystems. The state information of neighboring interconnected subsystems is acquired through direct communication. Finite-time Lyapunov stability analysis is performed and easy-to-check rank conditions on the distributed memories data of subsystems are obtained, under which finite-time stability of the distributed identifier is guaranteed. These rank conditions replace the restrictive persistence of excitation (PE) conditions which are hard and even impossible to achieve and verify. Next, a fixed-time system identifier for continuous-time nonlinear systems is presented. A novel adaptive update law with discontinuous gradient flows of the identification errors is presented that leverages CL to guarantee the learning of uncertain dynamics in a fixed time. Fixed-time Lyapunov stability analysis certifies fixed-time convergence to the stable equilibria of the GD flow of the system identification error under easyto-verify rank conditions.
Moreover, an online data-regularized CL-based stochastic GD is also presented for discrete-time (DT) function approximation with noisy data. A fixed-size memory of past experiences is repeatedly used in the update law along with the current streaming data to provide probabilistic convergence guarantees with much-improved convergence rates (i.e., linear instead of sublinear) and less restrictive data-richness requirements. This approach allows us to leverage the Lyapunov theory to provide probabilistic guarantees that assure convergence of the parameters to a probabilistic ultimate bound exponentially fast, provided that a rank condition on the stored data is satisfied. This analysis shows how the quality of the memory data affects the ultimate bound and can reduce the effects of the noise variance on the error bounds. We also presented deterministic and stochastic fixed-time stability of autonomous nonlinear DT systems. Lyapunov conditions are first presented under which the fixed-time stability of deterministic DT systems is certified. Extensions to systems under deterministic perturbations as well as stochastic noise are then considered. For the former, the sensitivity to perturbations for fixed-time stable DT systems is analyzed, and it is shown that fixed-time attractiveness is resulted from the presented Lyapunov conditions. For the latter, sufficient Lyapunov conditions for fixed-time stability in probability of nonlinear stochastic DT systems are presented. The fixed upper bound of the settling-time function is derived for both fixed-time stable and fixed-time attractive systems, and the stochastic settlingtime function fixed upper bound is derived for stochastic DT systems. Finally, a fixed-time identifier for modeling unknown DT nonlinear systems without requiring the PE condition is developed. A data-driven update law based on a modified GD update law is presented to learn the system parameters, which relies on CL. Fixed-time convergence guarantees are provided for the modified GD update law under a rank condition on the recorded data. To guarantee fixed-time convergence, fixed-time Lyapunov analysis is leveraged.
Persons with disabilities have the right to request and receive reasonable accommodation. Please call the Department of Mechanical Engineering at 355-5131 at least one day prior to the seminar; requests received after this date will be met when possible.
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Department:
Electrical and Computer Engineering
Name:
Elliot Xin Lu
Date Time:
Tuesday, May 9, 2023 - 2:00pm
Location:
1400 Biomedical and Physical Sciences Building and Zoom
Announcement:
ABSTRACT
Advisor: Carlo Piermarocchi
The study of quantum optics is principally concerned with investigating light-matter interactions. Within the discipline, computational simulation is a burgeoning field that can lend new insights into optical phenomena previously uncovered by theory or experiment. Collective emission effects such as superradiance serve as one prominent example. In contrast to ordinary emissions, superradiance involves dipolar coupling within optical ensembles and produces a coherent burst of radiation whose intensity scales with the square of the number of emitters. Whereas theoretical results involving superradiance are often shoehorned into small, ideal systems, numerical simulations permit the examination of much larger realistic systems, and can further aid in verifying experimental results. Studies of other phenomena, such as polarization enhancement, inhomogenenous broadening, and subradiance, benefit similarly.
To design new systems that exploit quantum optical effects, we devise in this thesis a new numerical approach that can faithfully simulate dynamics of optical active media. Such material are characterized by their ability to modify and re-emit radiation. Nanoscale semiconductor particles known as quantum dots serve as a prime example. Their larger dipole moments– compared to atoms– enable them to experience strong interactions with radiation fields, and permit the observation of a variety of optical phenomena, including superradiance. Despite this merit, numerical simulation of large ensembles of quantum dots–and for long time periods–is challenging. In contrast to previous counterparts, our computational model, which involves the solution to the Maxwell-Bloch equations via integral operator electric fields, is massively scalable in both time and space. This is facilitated by the Adaptive Integral Method (AIM), which effects FFT-based convolutions to evaluate the field. This allows us to perform large scale simulations that reproduce optical effects such as superradiance.
To demonstrate the fidelity of our approach, we evaluate the rate of photon emission from our ensemble and show that it reproduces the quadratic scaling of superradiance. In simulations of medium-sized (𝑁 = 50 − 300) ensembles of quantum dots in a Gaussian cloud, we confirm this quadratic scaling by subtracting independent emissions from total emissions. We also observe anisotropy of emission–another hallmark of superradiance–in the field radiated by the Gaussian cloud. Subradiance is revealed in steady state plots of the population excitation, which display diminished emissions. This effect is amplified by inhomogeneous broadening, which induces greater disorder and thus interference within the ensemble, but diminished by the presence of collective Lamb shifts.
Additionally, we compare the results of this calculation to those using another formalism, the Master equation. By applying zero-averaging random initial conditions to the polarization, we achieve strong numerical agreement between the two approaches. We observe both superradiant scaling, and destructive interference among dots separated by half-wavelengths. We remark, however, that the Maxwell-Bloch model is superior to the Master equation in resolving time delays and capturing propagation and memory effects. Hence, simulations involving ensembles of emitters separated far apart in space should opt for the Maxwell-Bloch approach to accurately account for delay effects.
List of publications
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Department:
Electrical and Computer Engineering
Name:
Omkar H. Ramachandran
Date Time:
Monday, May 8, 2023 - 10:30am
Location:
3405 Engineering Building and Zoom
Announcement:
ABSTRACT
Advisor: Dr. Shanker Balasubramaniam
The simulation of systems involving charged particles moving in the presence of electromagnetic fields is of great interest in a number of domains in physics, with applications including the characterization of pulsed power devices and accelerators, design of high precision etching and sterilization implements. As a result, several methods have been proposed to accurately simulate such systems. One such method is the particle-in-cell (PIC) technique, which characterizes the distribution of aplasma in phase space through a collection of statistically significant macroparticles. While contemporary implementations of electromagnetic PIC (EM-PIC) have typically relied on a finite-difference time-domain (FDTD) stencil to evaluate the fields, there has been a push for the adoption finite element methods that allow for the use of better geometry representations and more robust function spaces. In particular recent developments in the field have focused on developing implicit, unconditionally stable finite element field solvers that are free of mesh dependent stability constraints while natively conserving fundamental quantities such as charge and energy within a PIC scheme.
The goals of this dissertation are to develop efficient, charge-conserving, unconditionally stable finite element particle-in-cell (EM-FEMPIC) methods. First, (i) we construct a formulation of PIC built around exponential predictor-corrector particle integrators. We demonstrate that this approach has significantly better error convergence than equivalent polynomial methods, thus allowing for accurate evaluation of particle trajectories even at the large step-sizes afforded by implicit EM solvers. Next, (ii) for devices of a narrowband tendency, we construct a novel EMFEMPIC method based on envelope tracking. This allows us to accurately simulate the EM response of such a device while sampling at the narrow bandwidth, rather than at the highest absolute frequency of interest. Furthermore, we explore the consequences on charge-conservation for such a method and propose a rubric to ensure exact satisfaction of Gauss' laws. We then consider (iii) the matter of energy conservation in an EM-FEMPIC scheme and propose a set of guidelines that ensure the conservation of average energy over the course of a simulation. Finally, (iv) we reformulate a parameter extraction method originally proposed for efficient device-agnostic simulation of EM systems attached to lumped nonlinear devices to make it applicable to a system of moving particles. We couple this approach with a domain-decomposition framework to construct an efficient, 'particle-agnostic' extraction framework. Taken together these contributions address several open problems in the field and extend the applicability of EM-FEMPIC methods to larger, more relevant problems.
Journal Papers
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Department:
Mechanical Engineering
Name:
Archana Lamsal
Date Time:
Wednesday, May 3, 2023 - 1:00pm
Location:
2555D Engineering Building and Zoom
Announcement:
ABSTRACT
Advisor: Dr. Tamara Reid Bush
Sitting for long periods of time has health implications; two populations affected by long durations in the seated position include office workers and wheelchair users. Office workers suffer from cardio-vascular diseases and musculoskeletal disorders as a result of poor posture during prolonged sitting. Wheelchair users are also prone to various health issues including pressure injuries (PIs), for which shear loading and associated frictional forces are known risk factors. To address these issues, there is a need for developing an alternative working position which provides an opportunity for postural change in office workers and to study how the choice of fabrics used for the seat pan cover and pants worn by wheelchair users affect the frictional properties and shear forces at the seat interface.
The objectives of this work were: 1) to evaluate changes in body position, body loading, and blood perfusion while in a seated, standing, and new office seating position, termed the in-between position. 2) determine the coefficients of friction of seven commonly worn pant fabrics and two seat cover fabrics using a mechanical device and a tilting seat pan 3) to determine the shear force and coefficients of friction between five commonly worn pant fabrics and two seat cover fabrics through the development and utilization of a novel in-vivo experimental set up that permitted sliding of the human buttocks on the seat pan.
Various kinematic and kinetic analyses conducted in three different office working positions indicated that the in-between position provided a hip and lumbar position closer to standing than the seated position. Analysis of coefficient of friction using a mechanical device indicated that the office fabric seat cover produced smaller coefficient of friction than the vinyl seat cover with all pant fabrics. The in-vivo experiments also supported this result indicating that wheelchair users could benefit from an alternative seat cover material. Overall, this body of work provide a knowledge basis that will be useful in design of better office workspace and develop strategies that can reduce the risk of PI formation in wheelchair users.
Persons with disabilities have the right to request and receive reasonable accommodation. Please call the Department of Mechanical Engineering at 355-5131 at least one day prior to the seminar; requests received after this date will be met when possible.
Department:
Chemical Engineering and Materials Science
Name:
Jiawei Lu
Date Time:
Friday, May 5, 2023 - 10:00am
Location:
3405 Engineering Building
Announcement:
ABSTRACT
Advisor: Dr. Thomas R. Bieler
The Ti-6Al-4V (Ti64) alloy has been widely used as a light-weight structural material due to its excellent corrosion resistance and high strength even at elevated temperatures. However, the poor machinability of Ti64, leading to higher costs, has severely limited its application. The formation of segmented chips rather than smooth continuous chips, caused by the intrinsic low thermal conductivity of Ti64, is of great interest and significance for investigation.
Ti64 bars with various microstructures, namely mill-annealed (MIL), elongated (ELO), solution treated and aged (STA), and lamellar (LAM), were machined at 61, 91, and 122 m/min. The chips were collected, and their microstructures were characterized by scanning electron microscopy (SEM) and electron backscattered diffraction (EBSD). The morphology of these chips was also measured, and observations of the smooth and segmented sides were also made and compared.
For STA chips, nano-indentation and EBSD were used to investigate local shear strain phenomena. An existing continuum model based upon material constants and mechanical properties was used for shear band width prediction at various cutting speeds and the predicted values were compared with the measured values and discussed. In addition, a model based on the morphology of the segmented chips was adopted to calculate the homogeneous shear strains in the segments and catastrophic shear strains within the shear bands. Representative examples of chips were characterized by EBSD and analyzed using the stress tensor obtained from finite element numerical simulation. Finally, the chips were annealed at 500, 600 and 650 ℃ to investigate their response to annealing, revealing effects of the chip deformation history. For LAM, a few EBSD scans were also carried out to show the correlation between chip morphology and local orientations.
Overall, the work presented in this study demonstrates an approach to investigating the formation of segmented chips and the severe deformation during turning. It can be further applied to the chips obtained from other machining methods and to identify effects of higher cutting speeds.
Persons with disabilities have the right to request and receive reasonable accommodation. Please call the Department of Chemical Engineering and Materials Science at 355-5135 at least one day prior to the seminar; requests received after this date will be met when possible.
Department:
Chemical Engineering and Materials Science
Name:
Yan Xie
Date Time:
Wednesday, May 3, 2023 - 1:00pm
Location:
2250 Engineering Building
Announcement:
ABSTRACT
Advisor: Dr. Scott Calabrese Barton
Cascade reactions have attracted great attention in the fields of chemical synthesis, biofuel cells, and biosensors due to multiple benefits, including reduced waste generation and minimal purification requirements. They involve a sequence of chemical transformations that take place within a single reactor. In such reactions, the product of an individual reaction step, described as an intermediate, becomes the product of the following reaction step. The transport of these intermediates between neighboring active sites often faces the challenge of desorption into the bulk solvent, as well as competition with the side reactions. The efficiency of cascade reactions is therefore often limited by intermediate transport.
Nature has evolved several substrate channeling strategies to enable the direct transfer of intermediates between adjacent active sites, such as molecular tunneling, chemical swing arms, spatial organization, and electrostatic channeling. All of these mechanisms guide the transport of intermediates in sequential cascade reaction steps from the generation site to the consumption site. In this work, molecular dynamics simulations were performed to computationally understand the mechanisms of electrostatic channeling and molecular tunneling. Firstly, we studied the electrostatic channeling of glucose-6-phosphate (G6P) on a poly-arginine peptide connecting the sequential enzymes of hexokinase (HK) and glucose-6-phosphate dehydrogenase (G6PDH). The incorporation of a positive peptide bridge guides the direct transfer of negative G6P from HK to G6PDH via electrostatic interaction and prevents wasteful desorption. Metadynamics is used in conjunction with molecular dynamics simulation to quantify the hopping rate of G6P on the bridge. According to lag time calculations observed via a kinetic Monte Carlo model, a poly-arginine bridge is more efficient at channeling G6P compared to the previously studied poly-lysine bridge.
A more complex model of electrostatic channeling was then considered, namely the malate dehydrogenase–citrate synthase complex of the citric acid cycle. The negatively charged intermediate oxalacetate (OAA) travels along a positive surface on the enzyme complex. A Markov state model (MSM) identified the dominant pathway and four bottleneck residues. The residues formed the highest energy area, trapping the movement of OAA. By conducting a hub score analysis and measuring channeling probabilities, we verified that replacing the experimentally determined positive key residue Arg65 with the neutral residue Ala65 led to a 50% reduction in channeling probability, as observed experimentally. This occurred because the mutation caused a disruption of the continuous positive surface pathway of OAA.
The mechanism of molecular tunneling was studied for an ammonia tunnel in the asparagine synthetase system. Combining molecular dynamics with umbrella sampling, energy profiles were constructed for both the original structure and the mutant structure with an alanine → leucine replacement. The largest energy barrier was identified at the narrowest area of the tunnel formed by several bottleneck residues. Due to its larger side chain, leucine caused a narrowing of the tunnel when it replaced alanine in the mutant structure, resulting in the blockage of NH3, and thus an increase in the local energy profile. We also identified the possible desorption paths of NH3, which would allow the escape of NH3 after the mutation. The increased desorption probability along these paths is consistent with decreased enzyme activity as observed in experiments, due to inefficient NH3 transfer after mutation.
Finally, the enzymatic interaction between hexokinase (HK) and glucose-6-phosphate dehydrogenase(G6PDH) was studied with coarse-grained molecular dynamics (CG MD). CG MD simplified the complex system of HK-bridge-G6PDH by grouping several neighboring atoms into a coarse-grained bead and enabled the simulation timescales up to microseconds. Long simulation scales allowed the observation of enzymatic configuration change. The relative rotation of G6PDH shows an electrostatic interaction between the enzymes, which is dependent on ionic strength.
Overall, this work computationally examines the mechanisms of substrate channeling at an atomic level and acts as a guide to design efficient artificial cascades with substrate channeling.
Persons with disabilities have the right to request and receive reasonable accommodation. Please call the Department of Chemical Engineering and Materials Science at 355-5135 at least one day prior to the seminar; requests received after this date will be met when possible.
Department:
Civil and Environmental Engineering
Name:
Mahdi Ghazavi
Date Time:
Wednesday, May 3, 2023 - 10:00am
Location:
3540 Engineering Building
Announcement:
ABSTRACT
Advisor: Dr. Muhammed Emin Kutay
Long-life pavements are designed and built to last for over 50 years without needing major structural rehabilitation or reconstruction. Reported benefits of such pavements include low lifecycle cost, less frequent repair and/or rehabilitation, lower user-delay costs and lower environmental impact. Several approaches exist to design long-life pavements, all of which are based on mechanistic-empirical principles. While designing long-life pavements, deep structural distresses (e.g., bottom-up cracking) are designed to never develop, by limiting the maximum critical stresses and strains. Only surficial distresses (e.g., top-down cracking, rutting etc.) are allowed to occur, but they are managed via periodic maintenances (e.g., mill and overlay). Several states in the US have built long-life pavements by enhancing structural design methods, using better materials, improving specifications and construction practices. In Michigan, four pilot long-life pavement sections were constructed between 2017 and 2019; two rigid and two flexible pavements. Each pilot project included a long-life and an accompanying standard (control) section constructed on the same highway. Modifications to standard designs and materials were made to extend their service life. The focus of this dissertation is on the two flexible projects. The scope of the study included as-built evaluation of these pilot long-life projects to determine their potential for meeting the intended design and service lives. MDOT performed numerous field tests and collected material samples from these projects. Extensive analysis of the field data and numerous laboratory tests were conducted to characterize the material properties. As-constructed material properties were used in different mechanistic-empirical (ME) design software to estimate the expected performance of all the pilot projects. Based on the detailed laboratory and field testing and the mechanistic-empirical performance predictions, recommendations were made in structural design, material selection, construction, and quality control and quality assurance procedures. The main objective of this study is to perform a thorough analysis of the pilot flexible long-life projects which were designed based on state-of-the-practice methods and enhance the mechanistic-empirical design of these pavements and propose alternative design approach for long life pavements to potentially reduce life cycle cost and improve their performance.
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Department:
Biomedical Engineering
Name:
Manoj Madhavan
Date Time:
Tuesday, May 2, 2023 - 11:00am
Location:
1404 ISTB Building and Zoom
Announcement:
ABSTRACT
Advisor: Prof. Ripla Arora
During morphogenesis 2D epithelial tissue undergo architectural changes to form 3D structures called folds. Folding is a key phenomenon during embryogenesis and organogenesis and, is essential for several physiological functions. For example, folds in the stomach (rugae) and intestine (crypts) increase surface area for nutrient absorption and in the brain (gyri) increase cortical surface area for neural processing. The uterine luminal epithelium in mammals including humans, horses, and rodents, undergoes structural changes to form folds. Although improper uterine folding in horses results in pregnancy failure, the precise role of folds in embryo implantation remains unknown. Using 3D imaging and 3D reconstruction of the mouse uterus, we uncover dynamic changes in the luminal folding pattern. We show that in a healthy pregnancy, the uterus forms transverse folds prior to embryo implantation. Using models of aberrant uterine folding, we show that longitudinal folds lead to embryo-uterine axes misalignment and abnormal chamber formation. Further, we show that increased estrogen signaling and reduced progesterone signaling lead to aberrant longitudinal folds. Finally, we extend our findings to examine the effects of excess estrogen signaling on folding during hyperstimulation – a clinical procedure performed during In Vitro Fertilization (IVF) to increase egg numbers for higher success rate of implantation and pregnancy. In women, pregnancies following hyperstimulation often lead to preterm birth, placental abnormalities, and other complications. Our findings suggest that hyperstimulation in mice leads to pregnancy loss due to aberrant folding. Our research can be potentially used to improve pregnancy outcomes following IVF and fresh embryo transfer. In addition to fueling future research on endometrial folds in humans, our research will open up new avenues for the treatment of infertility and provide new targets for diagnosis based on uterine 3D structure.
Persons with disabilities have the right to request and receive reasonable accommodation. Please call the Department of Biomedical Engineering at 884-6976 at least one day prior to the seminar; requests received after this date will be met when possible.
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Department:
Electrical and Computer Engineering
Name:
Taha Yasin Posos
Date Time:
Monday, May 1, 2023 - 11:00am
Location:
2219 Engineering Building
Announcement:
ABSTRACT
Advisor: Sergey Baryshev
Large-area field emission cathodes made from carbon nanotube (CNT) fiber have long been promising as the next generation electron sources for high-power radio frequency (rf) or microwave vacuum electronic devices (VEDs). CNTs have excellent field emission properties such as low turn-on voltage and high output current at electric fields as low as ~10 MV/m, as compared to the legacy metal emitter technology. Therefore, CNT technology has the potential to decrease the operating voltage and simplify VED systems. However, in addition to high beam charge, beam-driven radiation sources require electron beams with low emittance (i.e. high brightness), which must be provided in a stable continuous fashion. Although there have been many studies on CNT fibers' emission current performance, there is not sufficient research on their emission uniformity, emittance, brightness, and overall upper performance limitations specific to the CNT material itself. The lack of these important characterization metrics led to the work presented in this thesis. Not only were the conventional current-voltage (I-V) relations measured and evaluated, but also the electron beams carrying the currents were monitored in situ in real-time by projecting the beam onto a scintillator screen in a custom field emission microscope. These enabled the measurement and evaluation of emittance and brightness. The existing bottlenecks limiting the fiber's performance were uncovered for the first time and new advanced CNT fiber cathode designs were proposed and engineered accordingly.
In Chapter 2, various standard (previously attempted) designs of CNT fiber cathodes were tested in the field emission microscope. The results showed that all cathodes had high emittance, low brightness, a large beam spread, non-uniform emission, current saturation, and instability. Hot spots and microbreakdowns were observed during emission. Analysis of the data revealed that all these problems were due to the formation of stray emitters on the cathode surface during emission. It was concluded that the tested fibers failed to provide any reasonable beam quality regardless of the cathode geometry.
Exceptionally non-uniform current emission observed in the experiments raised the question about the mechanism of current saturation when the output charge failed to keep up with the increasing electric field. In Chapter 3, a computational method was developed to extract the emission area from the emission micrographs and then calculate the emission current density. It was found that the current density saturated quickly and stopped obeying the Fowler-Nordheim law. It was demonstrated that the saturation effect occurred because the local current density reached a maximum level limited by the number of carriers and their finite transit time inside the bulk material's depletion region. It was concluded that overcoming the saturation issue is only possible if uniform emission can be achieved.
In Chapter 4, a brand new and unique cathode design was developed that successfully solved all the problems caused by stray emitters. It was demonstrated that the new design provided a uniform and stable electron beam with a small divergence angle, resulting in a beam with low emittance and high brightness. This result is a significant advancement that outlines a feasible path toward utilizing CNT fiber electron sources for practical VED applications. More specifically, it was observed that the entire cathode surface of a radius of approximately 75 μm emitted uniformly (with no hot spots) in the direction of the applied electric field. From this, the normalized dc current brightness was estimated as BN = 3.7×1010 A/m2 rad2 using the estimated emittance of 52 nm rad. From this, the brightness in the pulsed mode, the preferable mode in most VED HPM applications, was predicted to attain a notable value of BN = 4.4×1015 A/m2 rad2 .
Journal Publications:
1. T.Y. Posos, O. Chubenko, and S.V. Baryshev, “Confirmation of Transit Time-Limited Field Emission in Advanced Carbon Materials with a Fast Pattern Recognition Algorithm”, ACS Applied Electronic Materials 3.11, 4990 (2021), doi:10.1021/acsaelm.1c00789
2. T.Y. Posos, S.B. Fairchild, J. Park, and S.V. Baryshev, “Field emission microscopy of carbon nanotube fibers: evaluating and interpreting spatial emission”, Journal of Vacuum Science & Technology B 38.2, 024006 (2020), doi:10.1116/1.5140602
3. M.E. Schneider, H. Andrews, S.V. Baryshev, E. Jevarjian, D. Kim, K. Nichols, T.Y. Posos, M. Pettes, J. Power, J. Shao, and E.I. Simakov, “Evaluating Effects of Geometry and Material Composition on Production of Transversely Shaped Beams from Diamond Field Emission Array Cathodes”, Appl. Phys. Lett. 122.5, 054103 (2023), doi:10.1063/5.0128148
4. M.E. Schneider, B. Sims, E. Jevarjian, R. Shinohara, T. Nikhar, T.Y. Posos, W. Liu, J. Power, J. Shao and S.V. Baryshev, “Ampere-class bright field emission cathode operated at 100 MV / m,” Phys. Rev. Accel. Beams 24.12, 123401 (2021), doi:10.1103/PhysRevAccelBeams.24.123401
Journals under Review:
1. T.Y. Posos, Jack Cook and S.V. Baryshev, “Bright Spatially Coherent Beam from Carbon Nanotube Fiber Field Emission Cathode”, arXiv 2301.06529 (2023), doi:10.48550/arXiv.2301.06529
Conference Proceedings:
1. Z. Li, S.V. Baryshev, T.Y. Posos, M.E. Schneider, and S.G. Tantawi, “RF Design of an X-Band TM02 Mode Cavity for Field Emitter Testing”, Proc. 12th International Particle Accelerator Conference (IPAC’21), 2961, JACoW Publishing (2021), doi:10.18429/JACoW-IPAC2021- WEPAB148
Conference Presentations:
1. T.Y. Posos, Jack Cook, S.V. Baryshev, “Enabling Bright Carbon Nanotube Fiber Field Emission Cathode”, 13th Annual Graduate Symposium (MIPSE 2022), Michigan Institute for Plasma Science and Engineering
2. T.Y. Posos, “High Brightness Carbon Nanotube Fiber Field Emission Cathode”, 34th International Vacuum Nanoelectronics Conference (IVNC 2021), IEEE
3. T.Y.Posos, O. Chubenko, and S.V. Baryshev, “Field Emission Microscopy of Diamond and Nanotube Materials”, 9th International Workshop on Mechanism of Vacuum Arcs (MeVArc 2021)
4. T.Y. Posos, S.B. Fairchild, J. Park, and S.V. Baryshev, “Field Emission Microscopy of CNTs Fiber”, 32nd International Vacuum Nanoelectronics Conference (IVNC 2019), IEEE
5. T.Y. Posos, S.B. Fairchild, J. Park, and S.V. Baryshev, “Field Emission Microscopy of Looped CNT Fiber”, 2019 Engineering Graduate Research Symposium (EGRS 2019), Michigan State University College of Engineering
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Department:
Mechanical Engineering
Name:
Syed Fahad Hassan
Date Time:
Thursday, April 27, 2023 - 9:00am
Location:
Composite Vehicle Research Center Conference Room and Zoom
Announcement:
ABSTRACT
Advisor: Dr. Mahmoodul Haq
Thermoplastic polymers have seen rapid increase in automotive applications. Advances in nanofillers technology has seen these polymers compete with thermosets with respect to mechanical properties, light-weighting, emission control, precise manufacturing and high-volume processing. Unlike metals and thermosets, thermoplastics are relatively soft and their material response at intermediate strain rates (1 - 100s-1), commonly experienced in automotive crashes, is not well-documented. The tendency of thermoplastics to undergo large deformations before yield and failure, places a limitation on the type of apparatus which can be used to characterize their tensile response at these strain rates. This complex polymeric material behavior has led to an apparent lack of experimental techniques required to generate reliable tensile stress–strain data and a resultant absence of robust constitutive equations based ‘digital twins’.
To address this challenge, a three-pronged approach was implemented. First, a novel, symmetric, double-acting drop weight impact apparatus that allows for pure-tensile testing at desired strain rates was designed and developed ‘in-house’ at the composite vehicle research center (CVRC). Equipped with an accurate data acquisition system, this fixture allows for application of equal displacement on both ends of the test sample, which results in efficient stress transfer throughout its gage length and a smoother transition to dynamic equilibrium. Two in-line load cells were used on both ends of the sample to record load data and ensure symmetric load application. Digital image correlation along with high-speed camera was used for obtaining strain information. The data acquisition system was automated with an optical trigger to ensure repeatability of response and facilitate data processing.
Second, the test fixture was validated with Aluminum 6061-T6 data reported in the literature corresponding to two unique strain rates. The experimentally validated fixture was then used for the third part of the work that focused on intermediate strain rate characterization of five commonly used automotive thermoplastics. The thermoplastics were divided into three classes based on their stiffness and ductility. Further, the effect of nanoparticle inclusions on resulting tensile response of one select polymer (Acrylonitrile Butadiene Styrene - ABS) was investigated. Three nanoparticles, two graphene platelets and one carbon nanotube, were used at 1% wt. The baseline for the rate dependent response of all thermoplastics was established by initially testing them at different strain rates within the quasi-static regime. Next, all thermoplastics were tested at three strain rates corresponding to fixed drop heights of 10 in., 20 in. and 25 in.
Results show a homogenous strain field in the gage length of all samples tested, indicating a stable impact velocity and load rising rate. Further, the load recorded on both load cells was similar indicating symmetric loading. Importantly, little to no ringing was observed in the output load response eliminating the need for further signal processing.
In general, results indicate that with increasing strain rates, the tensile strengths increased whereas the failure strains (ductility) reduced. The material specific variations in strength and ductility for each polymer were different due to differences in microstructure and morphology. For example, at a strain rate of 27s-1 , the tensile strengths of ABS increased by 84% while failure strains reduced by 48%, compared to its quasi-static response. ABS nanocomposites exhibited improved strengths at higher strain rates relative to their quasi-static response. Nevertheless, it was lower than the pristine ABS response at similar strain-rate levels. This can be attributed to the improper dispersion of the nanoparticles as they were incorporated by mechanical mixing and no chemical compatibilization with host polymer was performed. Overall, the results showed that the new apparatus is reliable and repeatable for characterizing the tensile response of thermoplastic polymers at intermediate strain rates.
Persons with disabilities have the right to request and receive reasonable accommodation. Please call the Department of Mechanical Engineering at 355-5131 at least one day prior to the seminar; requests received after this date will be met when possible.
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Department:
Computer Science and Engineering
Name:
Emily Ribando-Gros
Date Time:
Tuesday, April 25, 2023 - 11:00am
Location:
Zoom
Announcement:
ABSTRACT
Advisor: N/A
The growing emphasis on data collection and machine learning has renewed the contributions of the ubiquitous Laplace operator in shape and data analysis. Variants and simplifications of the differential geometry de Rham-Hodge Laplacian have emerged as fast and concise topological and geometric shape descriptors for complex data sets. However, choosing the appropriate type of Laplace operator depends on the application and discretization scheme, especially in the context of volumes with 2-manifold boundary where treatment of boundary conditions is crucial.
In this dissertation, we present the Boundary-Induced Graph (BIG) Laplacian, introduced using tools from Discrete Exterior Calculus (DEC), to bring the graph Laplacian and Hodge Laplacian on an equal footing for manifolds with boundary. BIG Laplacians are defined on discrete domains, accounting for appropriate normal or tangential boundary conditions. We examine the similarities and differences of the graph Laplacian, BIG Laplacian, and Hodge Laplacian through an in-depth comparison.
Furthermore, we demonstrate experimentally the conditions for convergence of BIG Laplacian eigenvalues to those of the Hodge Laplacian for elementary shapes using an Eulerian representation of 3D domains as level-set functions on regular grids. Additionally, we show that similar schemes for defining Laplacians can be used as the kinetic energy component for the Hamiltonian operator of the density of small biological molecules. The spectra of such Hamiltonians serve as useful features for machine learning tasks in drug design and density function theory advancements, offering potential implications for practical applications.
Department:
Civil and Environmental Engineering
Name:
Mumtahin Hasnat
Date Time:
Monday, April 24, 2023 - 12:00pm
Location:
3540 Engineering Building
Announcement:
ABSTRACT
Advisor: Dr. Muhammed Emin Kutay
The Michigan Department of Transportation (MDOT) has been using the Distress Index (DI) since the inception of its pavement management system (PMS) in the early 1990s. DI was developed to help MDOT engineers in their decision-making process, budget allocation, and prioritization for future maintenance or reconstruction activities. However, the raw data requirements for the DI are complicated (and somewhat unique compared to the rest of the nation) and MDOT has been having difficulty in finding vendors to collect PMS data. Over the last three decades, the pavement industry has seen many advances in data collection, distress identification, performance modelling, and other processes fundamental to PMSs. Consequently, there is a need to revisit the DI used by MDOT and revise it according to modern pavement data collection standards and calculation methodology. The objective of this study was to develop an enhanced pavement condition score and associated PMS data collection methodology for use by MDOT. To meet this objective, 2081 flexible and 741 rigid pavement sections were selected from MDOT's performance database. Then five different condition indices used by other state agencies were computed using MDOT's PMS data and compared them against MDOT's Distress Index (DI). The results were presented through statistical analysis and scatter plots. Maintenace records were used to compare the magnitudes of different indices right before maintenance activities were performed. The new pavement condition parameter was selected to follow the current state of the practice it its rating scale and consider major distresses. The developed new condition parameter is backward compatible using MDOT's historical pavement management data. Moreover, while developing the new pavement condition index, important criteria such as policy sensitivity, ease of understanding, usefulness in decision-making were considered. Furthermore, various performance models were used to predict the new condition index and International Roughness Index (IRI) data and pavement fix lives were estimated for both asphalt and rigid pavements.
Department:
Computer Science and Engineering
Name:
Jose Guadalupe Hernandez
Date Time:
Friday, April 21, 2023 - 11:00am
Location:
3540 Engineering Building
Announcement:
ABSTRACT
Advisor: N/A
Evolutionary algorithms provide an effective set of tools for solving complex optimization problems found in the real world. When a new evolutionary algorithm is proposed, it is typically evaluated against hand-picked test problems or a benchmark suite to demonstrate its abilities. Indeed, multiple benchmark suites exist to shine a light on the types of problems an evolutionary algorithm is effective against. Such suites, however, are limited in their ability to help us understand why an evolutionary algorithm performs the way it does. In particular, problems with complex fitness landscape topologies do not allow for an intuitive understanding of how an algorithm traverses the search space.
Here, I propose a set of low-level diagnostic tools as an alternative to benchmark suites to more precisely and intuitively measure the strengths and weaknesses of an evolutionary algorithm; each diagnostic generates a handcrafted search space topology with targeted problem characteristics (i.e., modality, deception, dimensionality, etc.). More specifically, I focus on how the set of diagnostics can be used to develop a deeper understanding of a critical component found across many evolutionary algorithms -- the selection scheme. Indeed, we find key differences among commonly used selection schemes, where these differences help identify the kinds of problems each scheme is best suited for.
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Department:
Mechanical Engineering
Name:
Sakib Iqbal
Date Time:
Wednesday, April 19, 2023 - 10:00am
Location:
Zoom
Announcement:
ABSTRACT
Advisor: Dr. Xinran Xiao
Sheet Molding Compound (SMC) is a type of ready to mold composites material. The most common SMC consists of glass fiber bundles about one inch long distributed randomly in a B-stage polyester resin. SMC possesses good mechanical properties and manufacturing flexibility in forming complex shaped parts and is relatively low cost, making it one of the attractive choices to replace metallic parts in automotive industry. Nevertheless, SMC composites have not been utilized in critical automobile components owing to the lack of a satisfactory predictive model, especially for crashworthiness simulations. The main challenges in analysis of SMC structures are the large scatter in mechanical properties and the large difference in strengths under different stress distribution or loading conditions. For example, SMC demonstrates 1.5-2 times higher strength under 3-point (3-pt) bending in comparison to uniaxial tension strength. This phenomenon is known as the size effect on strength and can be explained by Weibull’s statistical strength theory. For materials with large size effect such as SMC, simulations carried out with the mean mechanical properties (i.e., tension, compression, and shear data) would result in a significant underprediction of flexural responses of the structure. To improve the predictions, the statistical distributions of the mechanical properties need to be considered and the size effect should be examined. Although statistical analysis has long been considered in composite designs, probabilistic finite element (PFE) analysis based on statistical strength models has also been employed to consider uncertainties and design reliability at every scale in composites, little work has been done to examine the size effect of strength in FE simulations.
This work aims to incorporate the size effect in probabilistic simulations of SMC composite structures. First, we extended the unimodal Weibull strength model into multimodal one by combining the tensile and flexural Weibull strength models. This approach was examined with a glass fiber SMC composite. A randomization algorithm was developed to incorporate the strength distribution in PFE models. The strength distribution model was discretized into a limited number of segments and the values of the average strength for each segment and their probabilities were determined. The strength values were then randomly assigned across the integration points in the PFE model according to their probabilities. This approach successfully reproduced the tensile and flexural responses with the mean peak load, post peak behavior, and energy absorption similar to experimental results within ten iterations. Next, in addition to the tensile strength, the statistical distributions of the elastic modulus and compressive strength were also considered. The tensile strength and compressive strength were modeled by bimodal Weibull strength distributions corresponding to the uniaxial and 3-pt bending experiments. To determine the mixture weight fraction of the bimodal models and some difficult to measure parameters in the damage mechanics based composite material model, model optimization was explored using two techniques: (1) Artificial Neural Network (ANN)-based machine learning (ML) and (2) Random Search. It was observed that although computationally inexpensive, ANN-ML was rather complicated for a general-purpose regression. On the other hand, RS is easy to implement. Its high computational cost is acceptable as the optimization has to be done only once for any specific material model. The PFE models optimized with RS were examined with four verification cases including tensile, compression, 3-pt and 4-pt bending, and three validation cases including open hole tension, disk bending with a fixed boundary and with a simply supported boundary conditions. The PFE predictions agreed well with the experimental results across these load cases.
Persons with disabilities have the right to request and receive reasonable accommodation. Please call the Department of Mechanical Engineering at 355-5131 at least one day prior to the seminar; requests received after this date will be met when possible.
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Department:
Mechanical Engineering
Name:
Tejas Patel
Date Time:
Tuesday, April 18, 2023 - 9:00am
Location:
Zoom
Announcement:
ABSTRACT
Advisors: Dr. Lik Chuan Lee and Dr. Tong Gao
The human heart is a highly complex organ, and its primary function is to pump blood through the arteries, veins and to perfuse all other body tissues and organs, including itself. In the last decade, cardiac simulations have become increasingly crucial to gain clinical insight into cardiac function, treatment, and testing. Nowadays, multi-physics cardiovascular simulations applied to patient-specific modeling can help in the diagnosis of cardiovascular diseases and in studying relevant clinical treatments. Hence, our central objective here is to develop a generalized multi-physics finite element (FE) framework that includes thermal-fluid structure interaction coupling to study cardiac function and treatments.
First, we developed a stabilized FE based flow solver with heat transfer to study hemodynamics. A python based open-source FE library (FEniCS) is used from ground- up to custom-build the solver. We benchmark and validate the solver and study convergence for classical test cases at intermediate Reynolds and Peclet number.
Second, we utilize the solver to investigate cryoballoon ablation (CBA), which is a minimally invasive surgery that uses freezing or cryoenergy to treat atrial fibrillation (AF). To begin with, we use a patient-specific left atrium (LA) geometry and realistic pulmonary vein (PV) blood flow boundary conditions to validate hemodynamics of the LA chamber. Next, we position a cryoballoon (CB) at the pulmonary vein ostium to simulate incomplete occlusion during cryotherapy and investigate the factors affecting lesion formation. We observe that lesion size is highly sensitive to the CB position and balloon tissue contact area. The threshold gap for lesion formation is 2.4 mm. We also note that as the balloon tissue contact area increases, the surgery is more effective, and the power absorbed across the CB reduces.
Third, we extend our development to a fully coupled fluid-structure interaction (FSI) solver with heat transfer using FEniCS. The FSI solver (named vanDANA) that uses the immersed boundary (IB) method is based on the Distributed Lagrange Multiplier based Fictitious Domain method and the interpolation of variables is conducted using the smeared delta-functions. Additionally, the structure can be set as incompressible or compressible. We benchmark our solver and analyze the scalability on HPC. This builds a solid foundation for the future use of this solver.
Persons with disabilities have the right to request and receive reasonable accommodation. Please call the Department of Mechanical Engineering at 355-5131 at least one day prior to the seminar; requests received after this date will be met when possible.
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Department:
Computer Science and Engineering
Name:
Ritam Ganguly
Date Time:
Tuesday, April 18, 2023 - 12:30am
Location:
3405 Engineering Building and Zoom
Announcement:
ABSTRACT
Advisor: N/A
Given the broad scale of distribution and complexity of today's system, an exhaustive model-checking algorithm is computationally costly and testing is not exhaustive enough. Runtime Verification on the other hand analyzes a developing execution, be it online or offline, of the system in order to check for the health of the system with respect to some specification. Runtime verification of distributed systems with respect to temporal specification is both critical as well as a challenging task. It is critical because it ensures the reliability of the system by detecting violations of system requirements. To guarantee the lack of violations one has to analyze every possible ordering of system events which makes it computationally expensive and hence challenging. In this dissertation, we focus on a partially synchronous distributed system, where the various components of the distributed system do not share a common global clock and a clock synchronization algorithm limits the maximum clock skew among processes to a constant. Following listed are the main contributions of this dissertation,
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Department:
Chemical Engineering and Materials Science
Name:
Aditya Patil
Date Time:
Friday, April 14, 2023 - 3:00pm
Location:
3405A/B Engineering Building and Zoom
Announcement:
ABSTRACT
Advisor: Dr. Andre Lee
Functionalizing the incompletely condensed octaphenyl double-decker silsesquioxane tetrasilanol, Ph8-DDSQ(OH)4, with reactive dichlorosilanes forms condensed, hybrid molecules with reactive organic groups on the opposite edge of the inorganic SiO1.5 core, surrounded by phenyl moiety. This unique phenyl moiety surrounded SQ core provides additional thermo-oxidative stability for high temperatures, organic thermoplastics, and thermosetting polymers. Unlike corner-capped functional POSS, condensing DDSQ with dichlorosilane enables different chemical moieties on the opposite side of the SQ core, allowing SQ to act as the "bridging" chemical needed for bonding two different classes of materials. In addition, another benefit that is unique when condensing DDSQ-tetrol is the formation of isomers. Ph8DDSQ(OH)4, when fully condensed with R1R2SiCl2, is the formation of conformational isomers or regioisomers. (isomers about the SQ core) The conformational isomer mixture often exhibited lower liquidus temperature than pure isomers, which benefits when mixed with organic resins at lower temperatures. Structural isomerism is the most radical type wherein the two compounds have the same number of atoms, but their chemical and physical properties are entirely different since they have logically distinct bonds. This work examines an asymmetrically capped DDSQ system synthesized as a coupling agent between graphene oxide (GO) nanofiller and styrene vinyl ester (VE) resin. The DDSQ-modified GO is dispersed into VE resin with only a simple mechanical stirring at room temperature. This work studies the different isomers of the DDSQ system. Firstly, meta and para isomeric moieties of phenyl ethynyl phenyl (PEP) dichlorosilane were obtained via the Sonogashira reaction and by subsequent reaction with trichlorosilane. The synthesized dichlorosilanes were reacted with DDPh8T8(OH)4, and the relevant reaction conditions and yield were presented. The reaction led to the formation of cis and trans isomers. These isomers form a eutectic mixture with a sharp melting point upon varying the ratio of cis and trans products. Upon using a mixture of meta and para dichlorosilanes as capping reagents, the reaction yielded a 6-isomer mixture of compounds. This isomeric mixture didn't exhibit sharp melting characteristics as the individual isolated compounds exhibited. The sharpness of the solid-liquid transition character can also be dampened when long-chain chlorosilanes are used as capping agents for tetrol.
This work also investigated the effect of constitutional isomerism in cage-like silsesquioxanes. Precisely, edge-open octaphenyl silsesquioxane diol condensed with tetramethyl dichlorosiloxane and double-decker-shaped silsesquioxane tetraol condensed with dimethyl dichlorosilanes form structural isomers. The interactions between the organic group bonded to the D-Si and the adjacent phenyl group connected to the T-Si of DDSQ molecules have a defining effect on the internal configuration of the DDSQ cage. This change affects phase transformation between liquid and solid states, forming a glassy state in a pure isolated compound.
Persons with disabilities have the right to request and receive reasonable accommodation. Please call the Department of Chemical Engineering and Materials Science at 355-5135 at least one day prior to the seminar; requests received after this date will be met when possible.
Department:
Computer Science and Engineering
Name:
Mohammad Hosein Khalifeh
Date Time:
Friday, April 14, 2023 - 1:30pm
Location:
3105 Engineering Building
Announcement:
ABSTRACT
Advisor: N/A
Most networks constantly change, and predicting links or recovering from link failures is crucial for maintaining a network. Distance-based graph invariants are important criteria for network maintenance. A graph mutation is a change in the edge set of a graph, and a graph gradient is the change in a graph invariant after a mutation. We present the general concepts of discrete integral and derivative for vertex and edge weighted graphs as a tool. Using the concepts, some related problems are solved more efficiently, flexibly, and simpler than the existing solutions.
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Department:
Electrical and Computer Engineering
Name:
Yan Gong
Date Time:
Tuesday, April 11, 2023 - 1:00pm
Location:
Zoom
Announcement:
ABSTRACT
Advisor: Wen Li
To date, a wide variety of neural tissue implants have been developed for neurophysiology recording from living tissues, and neural interfaces provide a direct communication pathway between nervous systems and machines. This direct communication pathway offers a new potential method to research neuron working mechanism, and to manipulate neuron activity. Simultaneously, many challenges, that raised up with rapid development of biomedical implants, need to be overcome. First, an ideal neural implant should ensure its own safety, which means minimizing the damage to the tissue and performing reliably and accurately for long periods of time. On the basis of safe implantation, better recording capabilities, flexible and configurable are required by future tools. For decades, many artificial neural interfaces evoke sensation in central and peripheral nervous systems (CNS and PNS respectively) by electrical signals. However, electrical stimulation has many limitations and difficulties, hardly considered the best solution for many cases, neural stimulation needs improved technology. Optogenetic, a rising role in field of neural interfaces, has proven its capabilities by direct optical stimulation of genetically modified target neuron population and achieving dramatical advantages comparing with traditional methods in spatial and temporal resolution.
This written report provides a development process towards an origami implantable recording array integrated with multiple micro-LEDs, and conduct systematic research on the challenges mentioned above, including but no limited to packaging technique, packaging material, and evaluation of encapsulation in reactive environments.
In order to systematically study package material and package technique, different materials properties are discussed for the chronic implantation of devices in the complex environment of the body, including biocompatibility, and moisture and gas hermeticity. This report summarizes common solid and soft packaging used in a variety of neural interface designs, as well as their packaging performances in term of electrical properties, mechanical properties, stability, biodegradability, biocompatibility, and optical properties.
For study reliable packaging for implantable neural prosthetic devices in body fluids. This report studied the stability of Parylene C (PA), SiO2, and Si3N4 packages and coating strategies on tungsten wires using accelerated, reactive aging tests in three solutions: pH 7.4 phosphate-buffered saline (PBS), PBS + 30 mM H2O2, and PBS + 150 mM H2O2 to simulate different inflammation situations. Different combinations of coating thicknesses and deposition methods to meet different design requirements were studied at various testing temperatures to accelerate the aging process.
Finally, these package techniques and material knowledge were used to fabricate origami neural implants. A 2D to 3D convertible, thin-film, opto-electro array with 4 addressable microscale light-emitting diodes (LEDs) for surface illumination and 9 penetrating electrodes for simultaneous recordings has been developed. The fabrication methods have been discussed with the electrical, optical, and thermal characteristics of the opto-electro array being quantified.
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Department:
Mechanical Engineering
Name:
Mahyar Abedi
Date Time:
Friday, April 7, 2023 - 11:30am
Location:
Zoom
Announcement:
ABSTRACT
Advisor: Dr. Andre Benard
As inexpensive and environmentally friendly technology, humidification-dehumidification (HDH) is an ideal candidate for water desalination due to its simple design and low energy requirements. With the ability to treat various types of compromised waters, the addition of a packed-bed medium enhances the desalination efficiency and system compactness, making direct-contact packed-bed HDH desalination systems a perfect fit for geographically distributed water desalination units and building integration.
The first part of this thesis focuses on modeling the behavior of a desalination unit and its integration with solar thermal systems, with a one-dimensional mathematical model developed and validated experimentally. Machine learning regression techniques are used to develop a data-driven surrogate model, which accurately predicts desalination performance but requires a larger dataset for high fidelity. A comprehensive assessment is carried out for the integration of an HDH system with a solar chimney, resulting in solar desalination chimneys. The assessment suggests that the pressure drop is a critical factor in the system's performance. A direct-contact packed-bed condenser shows a prominent desalination capacity. Small-scale configurations are ideal for household freshwater needs, while the large-scale can be implemented as sporadic water treatment plants in rural areas. Solar air heater systems are also studied for potential integration with desalination units, with an experimental flat plate solar air heater built and validated with 3D computational and 1D mathematical models. The investigation suggests that although the integrated system is more efficient (both thermal and desalination) compared to that of the solar desalination chimney, the dependency of the system on energy sources for the circulation of water and air is a significant drawback. This dependency can limit the system's autonomy and increase its operational costs.
The second part of this thesis investigates the integration of desalination units with buildings, specifically greenhouses. The greenhouse is integrated with a transparent solar water heater as a roof that absorbs the NIR waveband to increase the temperature of used or saline water and then passes the essential wavebands for plant growth. The hot water then flows through a water-treatment unit to produce potable water. Experimental pilots of the solar water heater are built, and models are developed to predict the behavior of the solar water panel meticulously. To incorporate the impact of spectral variation on lettuce as the case study, a dynamic growth model is developed that quantifies light spectrum variations. Changes in the light spectrum are accounted for via a new light-use efficiency parameter in the plant growth model. Then, several models are coupled to predict the behavior of an integrated greenhouse with a transparent solar water heater as a roof, a water treatment unit, and a spectral-incorporated plant growth model for lettuce in Phoenix, AZ. The models suggest that the transparent solar water heater on the roof reduces greenhouse ventilation load by about 30%, and the water treatment unit produces 35-40 kg of potable water daily, sufficient for single-row cultivation of lettuce. The integrated greenhouse has the potential to produce an average of 300 kg of fresh lettuce each month during the growth period, according to the plant growth model.
Persons with disabilities have the right to request and receive reasonable accommodation. Please call the Department of Mechanical Engineering at 355-5131 at least one day prior to the seminar; requests received after this date will be met when possible.
Department:
Mechanical Engineering
Name:
Royal Ihuaenyi
Date Time:
Thursday, April 6, 2023 - 10:00am
Location:
2555D Engineering Building
Announcement:
ABSTRACT
Advisor: Dr. Xinran Xiao
One key challenge in the deployment of future e-mobility systems is to ensure the safe operating condition of high-energy density batteries. Therefore, understanding battery failure mechanisms and reducing safety risks are critical in the design of electrified systems. Although the response of battery materials and systems under various conditions has been extensively explored in recent years, there are still a lot of challenges with developing models for predicting failures. One such challenge is the development of accurate thermomechanical models to predict battery failure caused by combined thermal and mechanical loadings. Such thermomechanical models aim to identify the thermomechanical failure condition of batteries through battery materials such as the separator. The structural integrity of battery separators plays a critical role in battery safety. This is because the deformation and failure of the separator can lead to an internal short circuit which can cause thermal runaway. In thermal runaway scenarios, the separator first expands and then shrinks before reaching its melting temperature. Furthermore, this shrinkage induces tensile stresses in the separator. Hence, developing a thermomechanical model that can predict the response of separators in their entire range of deformation is necessary.
Commonly used battery separators are dry-processed polymeric membranes with anisotropic microstructures and deformation modes that involve various physical processes that are difficult to quantify. These complexities introduce challenges in their characterization and modeling as their properties and structural integrity depend on multiple factors such as strain rate, loading direction, temperature, and the presence of an electrolyte. To predict the structural integrity of polymeric separators in abuse scenarios an understanding of the thermal and mechanical behavior of the separator is needed. Due to the multiple factors influencing the structural integrity of polymeric battery separators, developing models for the prediction of their thermomechanical response has always been challenging. Furthermore, computational models in form of user-defined material models are needed to account for these factors since existing material models in commercial software do not have that capability.
In this study, thermomechanical models are developed to predict the response of polymeric battery separators in thermal ramp scenarios. The time-dependent response of polymeric battery separators is taken into account and the material is modeled as viscoelastic in the deformation region before yielding and as viscoplastic under large deformations post-yield. As a first step, a linear thermoviscoelastic model, developed on an orthotropic framework was extended to account for the temperature effect and the plasticization effect of electrolyte solutions to predict the thermomechanical response of separators within the linear range of its deformation. In the developed linear orthotropic thermo-mechanical model, the temperature effect was introduced through the time-temperature superposition principle (TTSP). To account for the plasticization effect of electrolyte solutions on the thermo-mechanical response of the separator, a time-temperature-solvent superposition method (TTSSM) was developed to model the behavior of the separator in electrolyte solutions based on the viscoelastic framework established in air. Furthermore, an orthotropic nonlinear thermoviscoelastic was developed to predict the material response under large deformations before the onset of yielding. The model was developed based on the Schapery nonlinear viscoelastic model and a discretization algorithm was employed to evaluate the nonlinear viscoelastic hereditary integral with a kernel of Prony series based on a generalized Maxwell model with nonlinear springs and dashpots. Temperature dependence was introduced into the model through the TTSP. Subsequently, the developed nonlinear viscoelastic model was coupled with a viscoplastic model developed on the basis of a rheological framework that considers the mechanisms involved in the initial yielding, change in viscosity, strain softening and strain hardening in the stress-strain response of polymeric battery separators. The coupled viscoelastic – viscoplastic model was developed to predict the thermomechanical response of separators in their entire range of deformation before the onset of failure.
The material investigated in this work is Celgard®2400, a porous polypropylene (PP) separator. Experimental procedures were carried out under different loading and environmental conditions, using a dynamic mechanical analyzer (DMA), to characterize the material response, calibrate and validate the developed models. The developed thermomechanical models were implemented as user-defined subroutines in LS-DYNA® finite element (FE) package, which enables simulations with the thermal expansion/shrinkage behavior. Furthermore, analytical solutions were developed to verify the model implementation and predictions. The results from this study show that the model predictions of the material anisotropy, rate dependence, temperature dependence, and plasticization effect of electrolyte solutions agree reasonably well with the experimental data. The results also demonstrate that the non-isothermal simulations without considering the thermal expansion/shrinkage behavior of the separator resulted in large errors.
Persons with disabilities have the right to request and receive reasonable accommodation. Please call the Department of Mechanical Engineering at 355-5131 at least one day prior to the seminar; requests received after this date will be met when possible.
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Department:
Biomedical Engineering
Name:
Harvey Lee
Date Time:
Wednesday, April 5, 2023 - 2:00pm
Location:
1404 Interdisciplinary Science and Technology Building & Zoom
Announcement:
ABSTRACT
Advisor: Prof. Assaf Gilad
The use of synthetic biology to carry out functions achieved through conventional means are often met with higher performance and cheaper production costs, such as the modern development and production of recombinant human insulin in E. coli. This dissertation focuses on utilizing synthetic biology to access the versatility of proteins and unique features of Rare Earth Elements (REEs) for molecular imaging and biomedical engineering. REEs are an essential resource for modern technology – anything with a screen, lens, glass, lights, magnets, steel alloys, or batteries require the use of REEs. In addition to their properties in magnetism, chemical reactivity, and temperature durability, REEs are also heavily utilized for their unique spectroscopic properties, making them crucial for almost every sector of industry, as well as molecular imaging assisted diagnostics. Current methods for mining REEs involve the extensive use of harsh chemicals and intense labor, not to mention low yields and excessive byproducts. Moreover, not only do REEs exist on Earth in a finite amount, but their mining and distribution is alarmingly reliant on very few sources, leaving the availability of such resources vulnerable to unforeseen circumstances. It would therefore be beneficial for nations to develop REE recycling technology with higher yields, lower costs, and environmentally friendlier methods. Herein, motifs found in nature were integrated into newly designed synthetic proteins to enable REE binding, leading to applications in bioremediation and theranostics.
Persons with disabilities have the right to request and receive reasonable accommodation. Please call the Department of Biomedical Engineering at 884-6976 at least one day prior to the seminar; requests received after this date will be met when possible.
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Department:
Mechanical Engineering
Name:
Alessandro Bo
Date Time:
Wednesday, April 5, 2023 - 10:00am
Location:
Zoom
Announcement:
ABSTRACT
Advisor: Dr. Wei Lai and Dr. Andre Benard
This PhD thesis presents an in-depth characterization of the magnesium manganese oxide redox system for energy storage applications. The study is divided into three main parts. Each one of them explores the features of the energy storage material at increasing length scales: starting from the pellet-scale (on the order of millimeters), then moving to the packed-bed scale (on the order of centimeters), and finally reaching the reactor-scale (on the order of meters), in which the energy storage concept is demonstrated in an experimental reactor.
The first part of the study deals with the experimental characterization and modeling of the magnesium manganese oxide redox system thermodynamics. The test sample is an individual cylindrical pellet in the 1:1 magnesium-to-manganese molar ratio composition. Its extent of oxidation is measured via a series of thermogravimetric experiments conducted at temperatures between 1000 and 1500 °C and oxygen partial pressures between 0.01 and 0.9 bar(a). The experimental results are used to develop two thermodynamic models that accurately predict the behavior of the redox system within these temperature and oxygen partial pressure ranges. Furthermore, these models allow to improve the material characterization by providing estimates on the average enthalpy and entropy of reaction. This study provides the minimum theoretical knowledge needed to develop computational models to predict and optimize the operation of such energy storage material when integrated in a large-scale reactor.
The second part of the study deals with the measurement of the effective electrical conductivity of a packed bed of magnesium manganese oxide pellets. During this experimental campaign, different pellet form factors (cylindrical and spherical) and compositions (1:1 and 3:2 magnesium-to-manganese molar ratios) have been examined. These measurements are performed using a four-wire technique at temperatures ranging between 1000 and 1500 °C under atmospheric pressure. This study demonstrates that, under such conditions, the energy storage material is electrically conductive. This result plays a crucial role in the development of fast charging strategies for energy storage systems based on the magnesium manganese oxide redox system. In fact, given its electrical properties, the packed bed can be thermally charged by directly passing an electrical current through it (Joule heating) instead of relying on external heating elements. This study provides valuable insights into the design and operation of such energy storage systems, and the findings have important implications for the development of more efficient and cost-effective energy storage products.
The third part of the study deals with the modeling and experimental validation of a thermochemical energy storage reactor based on the magnesium manganese oxide redox system. The model combines transient lumped (0D) species and energy governing equations for both the solid and gas phases within the packed bed, with 1D axial and radial transient heat conduction equations within the reactor insulating layers. The model is validated using the experimental data collected during a redox cycling campaign of a nominally 1 kW/5 kWhth reactor based on the magnesium manganese oxide redox system. The redox material chemical kinetics is modeled using an equilibrium kinetics approach. Experimental correlations are also used to validate the pressure drop measured across the packed bed upon system discharge. This model provides a starting point for the design and optimization of commercial-scale energy storage systems based on the magnesium manganese oxide redox system.
Overall, this PhD thesis provides a foundational understanding of the magnesium manganese oxide redox system behavior at different length scales, starting from the pellet-scale, moving to the packed bed scale, and finally reaching the reactor-scale. The results of this study have significant implications for the development of efficient and scalable thermochemical energy storage systems.
Persons with disabilities have the right to request and receive reasonable accommodation. Please call the Department of Mechanical Engineering at 355-5131 at least one day prior to the seminar; requests received after this date will be met when possible.
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Department:
Electrical and Computer Engineering
Name:
Abdullah Karaaslanli
Date Time:
Tuesday, April 4, 2023 - 2:30pm
Location:
2555D Engineering Building and Zoom
Announcement:
ABSTRACT
Advisor: Dr. Selin Aviyente
Community detection and graph learning are two important problems in graph analysis. The former problem deals with topological analysis of graphs to identify their mesoscale organization; while graph learning aims to infer the interactions between nodes of a graph from data when the graph topology is not known a priori. Existing community detection and graph learning methods are mostly limited to single-layer graphs, where nodes are assumed to be connected with a single static edge. However, this assumption ignores the fact that many realworld relational data have multiple dimensions, which can be better represented with multilayer graphs. In this thesis, we propose various community detection and graph learning methods for different types of multilayergraphs.
In Chapter 2, we tackle the community detection problem in dynamic networks. Specifically, we focus on evolutionary spectral clustering, which extends spectral clustering to dynamic networks to learn a community structure that changes smoothly over time. We show the equivalence of evolutionary spectral clustering to a variant of dynamic stochastic blockmodel. For this purpose, we first introduce a novel dynamic SBM where the evolution of communities over time is modeled with pairwise Markov random fields. We then show that the log-posterior of the proposed model is equivalent to the quality function of evolutionary spectral clustering. This equivalence is used to determine the forgetting factor in evolutionary spectral clustering and to develop two new algorithms for dynamic community detection. The proposed algorithms are applied to both simulated and real-world dynamic networks and their performances are compared to state-of-the-art dynamic community detection methods.
Chapter 3 introduces a multilayer community detection method, which is especially tailored to handle multilayer brain networks constructed from electroencephalogram(EEG) data. In particular, we first construct functional multilayer networks from EEG data, where layers correspond to different frequency bands and interlayer edges are allowed between all brain regions. Next, a new multilayer modularity metric is defined based on a multilayer null model that preserves the layer-wise node degrees while randomizing the remaining characteristic cs of the network. The proposed modularity is parameterized with resolution parameter to handle the resolution limit of modularity, and interlayer scale parameter to control the importance of interlayer edges in community formation. Third, a group community detection method is proposed to find the common community structure for a set of subjects. The proposed multilayer community detection method is employed to identify the group level differences between the two response types during Flanker task, i.e. error and correct.
In Chapter 4, we present an algorithm to learn signed graphs, which we represent as a two-layer multiplex network where one layer corresponds to positive edges while the other to negative edges. The algorithm is based on graph learning approaches developed using graph signal processing. Existing graph learning methods rely on smoothness of graph signals over the graph; however, they are only capable of learning unsigned graphs. To this end, we propose a signed graph learning approach, that learns signed graphs based on the assumption of smoothness and non-smoothness of graph signals over positive and negative edges, respectively. The proposed method is further extended using kernels to take the nonlinear relations between nodes into account. From GSP perspective, this extension corresponds to assuming smoothness/non-smoothness of graph signals in a higher dimensional space defined by the kernel. The proposed approach is applied to the problem of gene regulatory network inference from single cell gene expression data. Experiments on simulated and real single cell datasets show that the method compares favorably with other single cell gene regulatory network reconstruction algorithms.
Chapter 5 addresses the problem of how to learn multiple signed graphs simultaneously. Existing GSP based GL approaches for this problem are limited to unsigned graph topologies. Therefore, we extend the algorithm developed in Chapter 4 to learn multiple signed graphs. In particular, given multiple datasets each of which includes graph signals associated with a signed graph, we assume smoothness and non-smoothness of graph signals as in Chapter 4. Furthermore, we assume that the signed graphs are similar to each other, which is ensured by regularizing the learned signed graphs through a learned signed consensus graph. The proposed method is employed for the joint inference of multiple gene regulatory networks from single cell gene expression data. Experiments on simulated and real single cell datasets show that the method performs better than methods that can learn a single graph at a time and previous joint gene regulatory network reconstruction algorithms.
In Chapter 6, we tackle the problem of learning multiple unsigned graphs from a heterogeneous dataset, which requires clustering graph signals while learning a graph for each cluster. Namely, we present an optimization problem for joint graph signal clustering and graph topology inference. The approach extends graph cut based clustering by partitioning the graph signals not only based on their pairwise similarities but also their smoothness with respect to the graphs associated with the clusters. The proposed method also learns the representative graph for each cluster using the smoothness of the graph signals with respect to the graph topology. Results on simulated and real data indicate the effectiveness of the proposed method.
Persons with disabilities have the right to request and receive reasonable accommodation. Please call the Electrical and Computer Engineering Department at 355-5066 at least one day prior to the seminar; requests received after this date will be met when possible.
Department:
Computer Science
Name:
Vincent Ragusa
Date Time:
Tuesday, April 4, 2023 - 1:00pm
Location:
1455A BPS
Announcement:
ABSTRACT
Advisor: N/A
Evolutionary computation is a powerful optimization tool, and an invaluable test bed for population genetics. Evolutionary algorithms can become stuck on local optima, but can escape these traps by temporarily losing fitness in order to discover even higher fitness in a process called valley-crossing. Valley-crossing is fundamentally linked to the balance between the forces of selection and variation, and as such, controlling this balance is important for optimizing the efficiency of evolutionary algorithms. Nature, in contrast, is not actively optimized for performance, and yet nature seems to overcome many challenges that evolutionary algorithms do not. It is possible that nature benefits from a highly dynamic balance between selection and variation, and this constant flux helps natural populations avoid stagnation and overcome obstacles in the fitness landscape. Working with this hypothesis in mind, I investigate the nature of selection and how natural phenomena strengthen or weaken it.
I find that selection strength can be thought of as the degree to which an evolving system is dissimilar to neutral drift. This perspective opens the door to accept all phenomena that affect the strength of selection as part of a unified theory of selection that treats selection strength as an emergent property. I present a new evolutionary dynamic -- the free-for-all effect -- that is the reduction of selection strength on organisms with higher-than-average fitness. Free-for-all can result in rapid evolutionary adaption that would otherwise seem impossible, and provides an elegant explanation for punctuated equilibrium. The discovery of free-for-all highlights the importance of spatial structure in evolving populations, and has led to the design of a new evolutionary search method called super explorers. Super explorers mimic the free-for-all effect, and improve evolutionary search, while placing full control into the hands of the algorithm designer.
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Department:
Mechanical Engineering
Name:
Javad Hosseinpour
Date Time:
Tuesday, April 4, 2023 - 10:00am
Location:
Zoom
Announcement:
ABSTRACT
Advisor: Dr. Abraham Engeda
Since the 1960s the idea of using supercritical carbon dioxide (s-CO2) as the working fluid in a Brayton power cycle has been entertained. But due to technical limitations of the time, the idea did not progress forward much. Presently, due to the availability of more knowledge, better technological platform, and advanced analysis tools, many believe it is time to revisit the idea of using carbon dioxide as the working fluid for power generation. Theoretically, the concept of a closed-loop s-CO2 Brayton cycle is highly attractive and promising; however, there is yet a major hurdle to be passed, namely the designing, developing, and testing of a reasonable size (10 MWe or higher) prototype of an s-CO2 Brayton-cycle-based power gas turbine. Specifically, designing a stable s-CO2 compressor is one of the main challenges that need to be addressed.
In this dissertation, a supercritical CO2 Brayton cycle design tool in Microsoft Excel coupled with CoolProp real gas NIST database was developed to optimize and analyze the power cycles as well as obtain the best operating conditions for an s-CO2 compressor working in a 10 MWe power cycle. Then, three s-CO2 Brayton cycles, namely simple recuperated, recompression, and dual turbine cycles were reconfigured to produce 11.11 MW (10 MWe) output net power. The results were compared to the conventional Brayton cycle as the basic s-CO2 layout. It was shown that the recompression cycle had the highest efficiency, but the highest back-work ratio and the lowest specific work.
Furthermore, the reconfigured simple recuperated cycle had a thermal efficiency of 43.2% with a specific work of 125.13 kJ/kg, which is in a moderate range between the dual turbine and recompression cycles. The lower capital cost of the simple cycle suggests it could be a viable option for commercialization. Furthermore, a new compressor design procedure was introduced and developed for s-CO2 centrifugal compressors with a pinched diffuser under ondesign and off-design conditions in MATLAB. The developed codes aimed to obtain a stable supercritical CO2 compressor design and to predict the performance of s-CO2 compressors by considering Span-Wagner real gas equation of state, condensation limit, as well as internal and external losses. The procedure was validated with experimental results for an air compressor and Sandia's s-CO2 compressor to examine the validity of the meanline code. The efficiency and pressure ratio obtained from the 1-D code were compared to CFD results and showed reasonable agreement with experimental data. It was found that there was an overprediction due to not considering the volute in the design at higher mass flow rates. By comparing the total-to-static efficiency of Sandia’s compressor with 1-D code and CFD, it was found that while the CFD results match the experimental data, the code could not calculate the total-to-static efficiency of Sandia’s compressor for the mass flow rates below 2.5 kg/s.
Besides, a new impeller with a vaneless pinched diffuser was proposed, which achieved a compressor efficiency of 90.45% with an excellent operating range of 47.8%. The results matched well with simulations for different mass flow rates at the design speedline of 20,000 RPM. Additionally, the internal behavior of s-CO2 was studied at the choke condition and a new analogy between the compressor passage and a converging-diverging nozzle was made for the high limit of the performance map. Besides, a loss analysis in the proposed s-CO2 compressor was performed, revealing that 75.8% of the total enthalpy loss was due to internal losses. Finally, the condensation contours were studied and the results highlighted that condensation is unavoidable in an s-CO2 centrifugal compressor; however, the condensation does not cause damage or affect the compressor's performance.
Persons with disabilities have the right to request and receive reasonable accommodation. Please call the Department of Mechanical Engineering at 355-5131 at least one day prior to the seminar; requests received after this date will be met when possible.
Department:
Civil and Environmental Engineering & Computer Science and Engineering
Name:
Hamed Bolandi
Date Time:
Wednesday, March 29, 2023 - 1:00pm
Location:
3546D Engineering Building and Zoom
Announcement:
ABSTRACT
Advisor: Dr. Vishnu Boddeti
This Multidisciplinary research proposes deep neural networks to bypass the Finite Element Analysis (FEA) and predict high-resolution stress distributions on loaded steel plates with variable loading, geometries, and boundary conditions. FEA for structures has been broadly used to conduct stress analysis of various civil and mechanical engineering structures. Conventional methods, such as FEA, provide high-fidelity solutions but require solving large linear systems that can be computationally intensive.
The existing workflow for FEM applications includes: (i) modeling the geometry and its components, (ii) specifying material properties, boundary conditions, and loading, (iii) Applying mesh strategy, and (iv) stress analysis which may be time-consuming based on the complexity of the model. Instead, Deep learning (DL) techniques can generate solutions significantly faster than conventional run-time analysis. This can prove extremely valuable in real-time structural assessment applications. In this work, The Convolutional Neural network (CNN) was designed and trained to use the geometry, boundary conditions, and static load as input to predict the stress contours in intact steel plates. Furthermore, we predict high-resolution stress distributions on damaged steel plates using CNNs augmented with custom loss functions that use physics rules to bypass the need for Finite Element Analysis.
We embedded physics constraints into the loss function to enforce the model training, precisely capturing stress concentrations around the tips of various structural damage configurations. The proposed technique’s performance was compared to Finite-Element simulations using partial differential equation (PDE) solver. There is also an emerging need for the prediction of dynamic stress distribution since Catastrophic failure of structural components is often caused by lateral loads, such as earthquakes and winds. Thus, accurate predictions of dynamic stress distribution are useful during highly disruptive events to guide corrective actions. Neuro-DynaStress is proposed to predict the entire sequence of stress distribution based on Finite Element simulations using a partial differential equation (PDE) solver.
More specifically, CNN, along with the multi-head attention transformer and feature alignment, is used to extract features and capture the data’s temporal dependence. The model was designed and trained to use the geometry, boundary conditions, and sequence of loads as input and predict the sequences of high-resolution von Mises stress contours. Moreover, to increase the accuracy of dynamic stress prediction, we propose Physics Informed Neural Network (PINN). The PINN-Stress model can predict the entire sequence of stress distribution based on Finite Element simulations using a PDE solver. Using automatic differentiation, we embed a partial differential equation into a deep neural network’s loss function to incorporate information from measurements and PDEs. In order to force our model to learn the physical constraints, we minimize the violation of the equation of motion and also minimize the boundary condition violation to fully enforce the underlying PDE. The PINN-Stress model can predict the sequence of normal and shear stress distribution in almost real-time and can generalize better than the model without PINN. Our model is also able to predict von Mises stress using the von Mises equation.
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Department:
Chemical Engineering and Materials Science
Name:
Jin Dai
Date Time:
Wednesday, March 22, 2023 - 1:00pm
Location:
3540 Engineering Building and Zoom
Announcement:
ABSTRACT
Advisor: Dr. Wei Lai
Lithium-ion batteries, based on the pioneering work of three Nobel Laureates, are everywhere in our lives from portable electronics, electric vehicles, to grid storage. However, they currently employ liquid electrolytes containing flammable organic solvents that could lead to a fire if batteries are overheated. Solid electrolytes, also called fast-ion conductors or superionic conductors, are alternatives with the uttermost safety. Among various solid electrolytes, lithium garnet oxides are a promising family of materials due to their high ionic conductivity and electrochemical stability. This work discusses the study of diffusion and conduction in LixLa3Zrx-5Ta7-xO12 garnet oxides using computational methods. We developed two new generations of interatomic potentials, induced dipole, and machine learning, for this composition series. We compared them with existing interatomic potentials in terms of force/virial error against density-functional theory, prediction of phase transition, self- diffusivity, and ionic conductivity, and found machine learning interatomic potentials have the best accuracy. We then applied machine learning interatomic potentials to investigate the temperature and composition dependence of diffusion and conduction in bulk materials and the influence of grain boundary structure on ionic conductivity. We believe that the atomic insight obtained from this work could be worthwhile in understanding the bottleneck of materials performance and could provide guidance on further improvements.
Persons with disabilities have the right to request and receive reasonable accommodation. Please call the Department of Chemical Engineering and Materials Science at 355-5135 at least one day prior to the seminar; requests received after this date will be met when possible.
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Department:
Biomedical Engineering
Name:
Alexander Bricco
Date Time:
Wednesday, March 22, 2023 - 10:00am
Location:
1404 Interdisciplinary Science and Technology Building and Zoom
Announcement:
ABSTRACT
Advisor: Prof. Assaf Gilad
Reporter genes are important tools for researchers studying molecular and cellular biology as they give location and measurable values to the expression level of a given gene. Reporter genes for MRI, allow these functions to be done at arbitrary tissue depth and noninvasively. Chemical Exchange Saturation Transfer (CEST) based reporter genes have shown promise in acting as reliable reporters in MRI but the relatively low sensitivity to the method has decreased its utility in research situations. Initial attempts to optimize existing CEST reporter genes proved difficult due to a series of technical challenges lead to developing a process where iterative machine learning and experimentation were used to develop CEST reporter genes that produce nearly a fourfold increase in contrast over prior art. Additionally POET is used to generate a reporter gene that produces significant contrast at a farther downfield frequency than prior CEST reporter genes.
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Department:
Computer Science and Engineering
Name:
Hayam Abdelrahman
Date Time:
Thursday, March 16, 2023 - 1:00pm
Location:
Zoom
Announcement:
ABSTRACT
Advisor: N/A
Locating neck-like features, or locally narrow parts, of a surface is crucial in various applications such as segmentation, shape analysis, path planning, and robotics. Topological methods are often utilized to find the set of shortest loops around handles and tunnels. However, there are abundant neck-like features on genus-0 shapes without any handles. While 3D geometry-aware topological approaches exist to find neck loops, their construction can be cumbersome and may even lead to unintuitive loops. Here we present two methods for efficiently computing a complete set of surface loops that are not limited to the topologically nontrivial independent loops.
In the first approach, we propose an efficient “topology-aware geometric approach” to compute the tightest loops around neck features on surfaces, including genus-0 surfaces. We use the critical points of a processed distance function as a Morse function to find both the location and evaluate the significance of possible neck-like features. Critical points of a Morse function defined on a volume provide rich topological and geometric information about the structure of the shape. Our algorithm starts with a volumetric representation of an input surface and then calculates the distance function of mesh points to the boundary surface as a Morse function. We directly create a cutting plane through each neck feature. Each resulting loop can then be tightened to form a closed geodesic representation of the neck feature.
It is known that reducing the dimension of a problem typically boosts efficiency drastically. Hence, we propose our second approach, which is a novel, efficient approach that uses the skeleton of the shape to compute such surface loops. Given a closed surface mesh, our algorithm produces a practically complete set of loops around narrow regions of the volume enclosed by or outside the surface. Moreover, as our approach accepts a 1D representation of the shape as input, it significantly simplifies and accelerates computations. In particular, the handle-type loops are found by examining a subset of the skeleton points as candidate loop centers; and tunnel-type loops are found by examining only high-valence skeleton points.
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Department:
Computer Science and Engineering
Name:
Nikolay "Nick" Ivanov
Date Time:
Monday, February 27, 2023 - 1:00pm
Location:
3540 Engineering Building and Zoom
Announcement:
ABSTRACT
Advisor: N/A
In recent decades, we have witnessed a convergence of multiple technologies into the integrated ever-evolving Smart World ecosystem. The ongoing evolution of the Smart World is shaped by cross-technological integration, as well as the adoption of new technologies into the ecosystem. Particularly, academia and industry envision blockchain technology as one of the major new additions to the Smart World. However, the adoption of blockchain technology is impeded by three major practical challenges: security, scalability, and usability. This thesis aims at addressing these three challenges by focusing on revealing new blockchain attacks, facilitating threat mitigation in smart contracts, and introducing new trust-free applications of blockchain technology. First, this thesis addresses some security challenges of blockchain largely overlooked in existing research. We discovered six zero-day social engineering attacks in Ethereum smart contracts and propose measures to address them. Furthermore, we introduce a new attack against hardware crypto wallets, confirmed by the manufacturers of the wallets, which evades security verification by user. Second, the thesis elaborates on defending smart contracts against attacks. We design a comprehensive five-dimensional classification taxonomy of smart contract defense tools and classify 133 existing threat mitigation solutions using our taxonomy. Next, we introduce a new smart contract security testing approach called transaction encapsulation, and implement a transaction testing tool, which reveals the actual outcomes (either benign or malicious) of Ethereum transactions. Third, the thesis introduces novel practical blockchain applications that exhibit increased security, privacy, and user control compared to other distributed solutions. We propose a framework that uses a single Ethereum smart contract for enabling high-performance scalable smart contracts on the cloud. Finally, the thesis introduces a solution that uses Ethereum smart contracts for leveraging decentralized networks of WiFi hotspots with cross-domain authentication and automated QoS enforcement. We implemented and thoroughly evaluated all the proposed attacks, defenses, and frameworks thereby confirming the real-world applicability of our work. The thesis concludes with an outlook of our ongoing and future efforts to further address the practical challenges associated with the integration of blockchain into the Smart World ecosystem.
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Department:
Biomedical Engineering
Name:
Victoria Avery Toomajian
Date Time:
Friday, February 24, 2023 - 10:00am
Location:
1404 ISTB Building and Zoom
Announcement:
ABSTRACT
Advisor: Prof. Chris Contag
Delivery tools such as viral vectors, lipids, liposomes, polymers, polymeric micelles, inorganic nanoparticles, and extracellular vesicles have been studied for targeted therapeutic delivery. A number of these have been approved by the Food and Drug Administration for treatment of disease and many are currently being investigated in clinical trials. Extracellular vesicles (EVs) are an emerging therapeutic delivery tool based on their ability to be naturally taken up by cells, low immunogenicity, and potential for inherent targeting ability. EVs are small membrane bound particles released by cells and are considered to be a naturally occurring method of cell-to-cell communication. The targeting ability of EVs has been demonstrated using tumor cell-derived EVs that show increased uptake in tumors and tumor cells. In addition, EVs from immune cells have been used to target areas of inflammation, and one potential benefit of using EVs is that tracking studies have shown that EVs cross tissue barriers in vivo. EVs have been tracked by common imaging modalities, all of which rely on labeling the EV with a modality-specific tracer, such as inorganic nanoparticles, fluorescent dyes, bioluminescent or fluorescent proteins, or radioactive tags. One of the emerging imaging methods for tracking EVs in vivo is magnetic particle imaging (MPI), which uses superparamagnetic iron oxide nanoparticles (SPIOs) as the tracer. Once labeled with SPIOs, EVs can be tracked in vivo with MPI, which offers the significant advantages of being sensitive and directly quantitative. Development of EVs as a therapeutic delivery tool can be enhanced through imaging, and here I evaluate this for primary cancer and metastasis as well as cardiovascular disease.
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Department:
Computer Science and Engineering
Name:
Pedram Kheirkhah Sangdeh
Date Time:
Friday, February 10, 2023 - 2:00pm
Location:
Zoom
Announcement:
ABSTRACT
Advisor: N/A
The ever-increasing demands for data-hungry wireless services and rapid proliferation of wireless devices in sub-6 GHz band have pushed current wireless technologies to a breaking point, necessitating efficient and intelligent strategies to utilize scarce communication resources. This thesis aims at leveraging novel communication frameworks, artificial intelligence techniques, and synergies between them in bringing efficiency and intelligence to the next generation of wireless networks. We first propose new spectrum sharing and non-orthogonal multiple access schemes to enhance spectral efficiency, connectivity, and throughput of cellular and Wireless Local Area Networks (WLAN). We then take advantage of recent advances in artificial intelligence to reduce communication overhead of channel sounding mechanism and accelerate resource allocation in WLANs. Our learningbased solutions efficiently utilize available communication and computation resources to facilitate multi-user MIMO and OFDMA in WLANs. We finally design a communication framework for accelerating federated learning in future intelligent transportation systems, where heterogeneous capabilities and mobility of users along with limited available bandwidth for communications are huge obstacles toward making the network intelligent in a distributed manner. With the aid of a deadline-driven scheduler and asynchronous uplink multi-user MIMO, our proposed solution reduces data loss at vehicles in a dynamic vehicular environment, making a concrete step toward the practical adoption of federated learning in future transportation systems.
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Department:
Electrical and Computer Engineering
Name:
Adamantia Chletsou
Date Time:
Tuesday, January 31, 2023 - 9:00am
Location:
2219 Engineering Building and Zoom
Announcement:
ABSTRACT
Advisor: Dr. John Papapolymerou
This dissertation demonstrates the implementation methods and performance of antennas on different substrates using the traditional lithography method and Additive Manufacturing (AM) techniques. The developed devices are used for biomedical applications and vehicular communications. The effectiveness of using photonic curing and reactive silver ink to develop 3D printed antennas on thermo‐sensitive substrates is investigated. Intense Pulsed Light (IPL) is used to cure silver nano‐particle ink on the automotive Acrylonitrile Butadiene Styrene (ABS) and the vero‐white polymer. Different curing profiles of IPL are tested on the ABS and the vero‐white to identify the optimal one. Development of antennas using lithography, Aerosol Jet Printer (AJP) combined with thermal curing, AJP combined with photonic curing, and AJP combined with reactive ink is investigated and their overall performance is compared.
The first step of this dissertation is to explore the antenna design that is optimal for biomedical, Radio Frequency Identification (RFID) applications, operating inside human muscle and in free space. The next step is the development of a dual‐band, planar antenna for automotive applications using lithography on a flexible, lightweight substrate and AM techniques on ABS. The antenna performance is tested on a real vehicle and the effects of the ground on the antenna radiation pattern are identified. Co‐Planar Waveguide (CPW) lines are developed using the same procedure to identify the losses due to silver conductivity. Thereafter, an Electrically Small Antenna (ESA) is developed on a 3D printed hemisphere for vehicular communications. Prototypes of this antenna are tested on a real vehicle and a ground plane inside a near field system. The effect of the vehicle body on the antenna performance is evaluated.
Persons with disabilities have the right to request and receive reasonable accommodation. Please call the Department of Electrical and Computer Engineering at 355‐5066 at least one day prior to the seminar; requests received after this date will be met when possible.
Email sandra@msu.edu for Zoom information
Department:
Computer Science and Engineering
Name:
Hossein Pirayesh
Date Time:
Monday, January 30, 2023 - 4:00pm
Location:
Zoom
Announcement:
ABSTRACT
Advisor: N/A
While interest in Internet of Things (IoT) applications has surged in recent years, the broad diversity in their constraints, such as power consumption, channel bandwidth, link robustness, and packet latency, still challenges state-of-the-art technologies to enable efficient and ubiquitous wireless connectivity for IoT devices in many practical scenarios. In this thesis, we study three sets of primary constraints in developing IoT networks; energy efficiency, spectral efficiency, and physical-layer security. First, this thesis introduces EE-IoT, an energy-efficient wireless communication scheme for IoT networks. EE-IoT allows low-complex non-multi-carrier IoT devices to communicate with an orthogonal frequency division multiplexing (OFDM)-based wireless localarea network (WLAN) access point (AP) at a very low sampling rate, thereby leading to a significant reduction of IoT devices’ hardware complexity and power consumption. This thesis further enables a transparent coexistence of IoT devices and legacy Wi-Fi devices. Second, to improve spectral efficiency of dense IoT networks, this thesis introduces UD-MIMO, a practical uplink distributed multiple-input multiple-output (MIMO) for WLANs, and MaLoRaGW, a first-of-itskind multi-antenna long-range (LoRa) gateway that enables multi-user MIMO (MU-MIMO) LoRa communications in both uplink and downlink. The key enablers of the proposed schemes are new co-channel interference management techniques that allow Wi-Fi APs and LoRa gateways to concurrently serve multiple users in the absence of fine-grained inter-node synchronization. Third, this thesis introduces two jamming-resilient receiver architectures to secure vehicular ad hoc networks (VANETs) and ZigBee communications against high-power, in-band constant jamming attacks. The proposed schemes leverage multi-antenna technology and new signal detection methods to suppress jamming signals and decode desired signals. This thesis provides detailed information regarding the implementation of the proposed schemes on real-world wireless testbeds and evaluates their performance in practice.