By Claire Tremont, Manager of Communications and Digital Strategy
Operated through the University of Connecticut School of Engineering, the Digital Design Research, Analysis, and Manufacturing (D2REAM) Center – an academic-government-industry partnership that will develop groundbreaking modeling and simulation capabilities for the next generation of Army ground vehicle systems – aims to support advanced structural digital design and manufacturing, and discovery of novel metamaterials.
By using the strong research ecosystems at UConn, the center, which launched in July, looks to build a stronger partnership between academia, government, and industry. The center is supported by a $4 million round of funding in its first year, and by an additional $5 million in the second year.
“Our objective is to formulate and develop novel digital engineering models that will help the Army make better predictions, which in turn will further reduce the need to build physical prototypes,” says Mechanical Engineering Professor and Department Head Horea Ilies, who leads the center along with Castleman Professor of Engineering Innovation Associate Professor Julián Norato. “We have at UConn one of the strongest computational design and manufacturing groups in the nation.”
UConn’s team of Mechanical Engineering Graduate students achieved a remarkable victory, securing first place at the national hackathon event hosted by The Computer & Information in Engineering (CIE) Division of the American Society of Mechanical Engineers (ASME). This annual competition featured 34 participants from 18 institutions and took place from August 20 to 23, 2023, at the Boston Park Plaza in Boston, MA.
From left: PhD students Leidong Xu, Zihan Wang, and Prof. Hongyi Xu
The dynamic duo of Leidong Xu and Zihan Wang, both PhD students affiliated with Prof. Hongyi Xu’s Computation Design for Manufacturing Laboratory, earned the grand prize of $1,400 for their outstanding performance. The second-place team received $700, while the third-place team received $350.
The ASME-hosted hackathon presented an invaluable opportunity for participants to immerse themselves in the practical applications of data science and machine learning techniques to solve real-world engineering challenges. The primary objective of this competition was to develop realistic textures for solid objects created using computer-aided design (CAD) software. These textures were expected to mimic the behavior of real-world materials like metals and alloys across various scales.
UConn’s triumph at this national event is a testament to the exceptional talent and dedication of its Mechanical Engineering students, showcasing their ability to harness cutting-edge technology to address complex engineering problems. The students and Prof. Xu delved deeper into their journey leading up to and during the hackathon below.
What were the key challenges you and your team encountered during the hackathon, and how did you overcome them?
The hackathon event has a tight timeframe, and it is a huge challenge for us to develop a complete and polish project. To overcome it, we allocate time wisely and finally get all results done in one week.
Could you provide insights into the innovative solution you developed for the hackathon challenge?
Zihan and Leidong enhanced an existing system that utilized 2D microstructure images to recreate 3D microstructures that are statistically equivalent. Our advanced framework employs a Transfer Learning model to capture essential features from the granular microstructures of alloys. Notably, we’ve augmented computational efficiency through
parallel computing, which also allows our generated microstructures to be incorporated into intricate 3D volumes like tubes, helical gears, and turbo blades. Our methodology integrates transfer learning via VGG-19, style transfer techniques for texture synthesis, and a multi-GPU parallel approach. Beyond its technical prowess, our framework addresses a crucial design hurdle, bridging the gap between microstructures and designers’ vision seamlessly.
What lessons or takeaways do you think other aspiring participants can learn from your experience?
With the rapid evolution of machine learning methodologies in recent times, it’s imperative for researchers to first understand the inherent characteristics of their data before selecting an approach. From there, adapting and tweaking existing frameworks or strategies can be pivotal in optimizing results.
How did your preparation and training beforehand impact your performance during the hackathon?
We are very familiar with the programming and visualization tools we used during the hackathon. Additionally, we possess sufficient expertise in pre-trained deep learning models, image-processing methods, and style transfer techniques. This proficiency greatly expedited our problem-solving process throughout the hackathon.
Were there any unexpected twists or turns during the competition that forced you to adapt your approach?
With the limited time at hand, we realized that we needed to capitalize on the advantage of using pre-trained deep learning models to tackle the challenge effectively. Initially, we had planned to build our solution from the ground up, training our own models and optimizing them for the specific problem we were addressing. However, given the tight timeframe, this approach would have consumed a substantial portion of our available time. Upon evaluating our situation, we recognized that leveraging pre-trained models could provide us with a significant head start. These models were already trained on vast amounts of data and had learned complex patterns, making them well-suited for our problem as well. This shift in strategy allowed us to save precious time on training and focus more on adapting the model to our specific needs.
Looking ahead, what are your aspirations or goals in the field of technology and innovation after your victory at the ASME 2023 CIE Hackathon?
Our victory at the ASME 2023 CIE Hackathon has reinforced our drive to further refine and innovate our current framework. We see a multitude of avenues for enhancement. Specifically, we’re eager to develop a fully automated system for image analysis and labeling, which would drastically streamline the process. Another focus is to fine-tune our parallel algorithm to produce microstructure images with even greater resolution. Moreover, in a bid to consolidate our findings and methods, we’re excited about our upcoming collaboration with Sandia National Laboratories. Our joint effort aims to encapsulate our hackathon project into a comprehensive journal paper, sharing our innovations with the broader scientific community.
How do you envision leveraging the skills and experiences gained from the hackathon in your future projects and endeavors?
IDETC/CIE hackathon is an opportunity to engage with real-world engineering problems, moving beyond academic theory. This setting will allow me to apply my technical knowledge in a practical context, enhancing my understanding of the mission and challenges of national labs and leading industry companies. Participating in the hackathon in a team will serve as an excellent opportunity for honing my teamwork and cooperative abilities. The exchange of ideas, innovation, and sense of camaraderie within such events play a critical role in my future career.
Can you provide insights into your background in coding? How long have you been coding, and what initially sparked your interest in this field?
We started on our computational research journey during our undergraduate years. For us, coding transcends mere functionality; it is an art form. We firmly believe that the elegance and precision of the code play a pivotal role in determining the quality of the final research output. Consequently, we always strive to craft our code with extra care and refinement, ensuring that it not only fulfills its intended purpose but also stands as a testament to our dedication and passion.
Were there any specific coding languages or technologies that played a crucial role in your solution for the hackathon challenge?
We heavily relied on the PyTorch package, which is based on the Python programming language, to implement our innovative idea. Beyond that, deep learning methods and image analysis techniques are both very important to contribute to our success.
How do you plan to continue developing your coding skills and staying updated with the latest advancements in technology?
To ensure sustained progress in our coding capabilities and awareness of cutting-edge developments, we’ve mapped out a multi-faceted approach. This includes actively participating in tech competitions, which challenges our problem-solving abilities and exposes us to diverse perspectives. Furthermore, attending conferences allows us to gain firsthand insights from industry leaders and pioneers. Finally, keeping abreast of the latest
Abstract: Idaho National Laboratory (INL) is at the forefront of the nation’s advanced reactor R&D effort. Advanced reactor are a promising form of baseload carbon free energy generation and several studies are expecting them to play a critical part in US decarbonization plans. The seminar presentation will be divided in two parts. The first will provide an overview of R&D activities at the lab in support of advanced reactor development efforts. The timeline for forthcoming reactor demonstrations efforts at INL and elsewhere will also be presented. The second part of the seminar will discuss the new ‘multiphysics modeling & simulation’ capabilities being developed at INL as part of the ‘MOOSE’ ecosystem. These tools are being developed primarily to model the complex physics in advanced reactors, but can also be employed to wider engineering applications. They represent a paradigm shit in multi-disciplinary engineering analysis, but enabling the tight coupling of various physical phenomena in nuclear reactors
Biographical Sketch: Dr. Abdalla Abou-Jaoude is an R&D Staff Scientist in the Advanced Reactor Technology Department of Idaho National Laboratory (INL). He is leading efforts in three main areas at INL: advanced modeling & simulation, molten salt irradiation, and nuclear technoeconomics. As the work package manager for the National Reactor Innovation Center’s (NRIC) Virtual Test Bed (VTB), and the Nuclear Energy Advanced Modeling and Simulation (NEAMS) campaign point of contact to the Nuclear Regulatory Commission (NRC), Dr. Abou-Jaoude engages with stakeholders and coordinates different efforts in support of advanced reactor multiphysics simulations capabilities. He is also leading work packages for the NEAMS and Molten Salt Reactor (MSR) campaign on developing multiphysics simulation capabilities for molten salt reactors.
Dr. Abou-Jaoude was awarded an internal lab project to demonstrate molten salt irradiation capability at INL. The project intends to conduct the first fueled chloride salt irradiation in history at the NRAD reactor. Abdalla also serves as Activity Lead for the Systems Analysis & Integration (SA&I) campaign on developing technoeconomic assessment of advanced reactors, notably microreactors. As part of this effort, he developed an “economics-by-design” framework to improve competitiveness of novel concepts and better align them with market needs.
Previously at INL, Abdalla has been involved in various aspect of advanced reactor designs, notably for molten salt reactors, sodium fast reactors (namely the Versatile Test Reactor), nuclear thermal propulsion, and heat-pipe based microreactors. He also previously supported a private-public partnership with a U.S. utility to evaluate hydrogen-cogeneration options at nuclear power plants. He graduated with a doctorate in Nuclear Engineering from Georgia Tech in 2017 and was the INL Deboisblanc Distinguished Postdoctoral Associate during 2018. He obtained a MEng in Mechanical with Nuclear Engineering from Imperial College London in 2013.
Abstract: Lithium-ion batteries are one of the critical momentums of our current mobile society. With the further development and application of increasingly high energy density batteries and large capacity battery packs in electric vehicles, cellphones, laptops, and large-scale energy storage systems, the consequences of battery safety issues now become significant threats. Internal short circuits (ISCs) and thermal runaways (TRs) are typical battery safety issues where mechanics, electrochemistry, and thermal are strongly coupled. Interdisciplinary endeavors are in pressing need to address these safety issues. In this talk, multiphysics modeling and characterization at both cell level (~102 mm) and active particle level (~1 μm) will be highlighted to provide a mechanistic understanding of the nature of triggering and evolution of ISCs as well as the responsible mechanical instabilities of the solid-solid interfaces. In the meantime, a machine-learning combined with physics-based modeling will be introduced to achieve faster computation with higher accuracy. Results provide new insights into multiphysics behaviors in battery safety issues and offer engineering-ready modeling methodologies for the next-generation battery design, evaluation, and monitoring.
Biographical Sketch: Dr. Jun Xu joined the Department of Mechanical Engineering at the University of Delaware as an Associate Professor in 2023 Fall. Dr. Xu served as the inaugural Director of NC Battery Complexity, Autonomous Vehicle and Electrification Research Center when he was an Associate Professor at the University of North Carolina at Charlotte. Dr. Xu’s research mainly focuses on multiphysics modeling and characterization of batteries, and impact dynamics. Dr. Xu now serves as an executive committee member of the Advanced Energy System Division, ASME. He is Associate Editor of ASME Journal of Electrochemical Energy Conversion and Storage, Scientific Reports and Batteries. Dr. Xu has published more than 130 peer-reviewed journal papers with citations of 5,400+, H-index 43. Dr. Xu was included in World’s Top 2% Scientist List (Stanford University, 2022) and awarded the prestigious James H. Woodward Faculty Research Award (2021, Chancellor’s Award) and Early-Career Faculty Awards for Excellence in Research (2022) at UNC Charlotte. Dr. Xu earned his Ph.D. degree from Columbia University in 2014.
Abstract: Weather Forecast and Climate Models, often referred to as General Circulation Models (GCMs), play pivotal roles in modern society, impacting various sectors, from everyday planning to aviation and national defense. This presentation explores the multifaceted significance of GCMs, both scientifically and economically.
Economically, accurate weather and climate predictions yield an annual economic benefit exceeding $160 billion. Moreover, recent economic assessments conducted across various countries consistently reveal robust cost-benefit ratios for investments in weather and climate services, typically ranging from 1:4 to 1:36. This remarkable potential 2,500% Return On Investment underscores their fundamental societal importance.
This presentation will provide an overview of the historical development of GCMs and shed light on the profound significance of weather and climate predictions in today’s world. It will also delve into the intricacies of GCMs, with an emphasis on their subgrid-scale processes and the methods used to account for them (i.e., parameterizations). Additionally, the application of Computational Fluid Dynamics (CFD) tools, such as large eddy simulations, to help develop and refine parameterizations will be explored.
Biographical Sketch: Dr. Maria Chinita is a researcher at the University of California Los Angeles and Jet Propulsion Laboratory. She earned her PhD in Meteorology from the University of Lisbon, Portugal, in 2018. Her research primarily focuses on atmospheric boundary layers, involving several modeling techniques and observational data to gain a better understanding of small-scale processes. She applies these insights to develop unified parameterizations for atmospheric convection.
Abstract: Metal additive manufacturing (AM), e.g., laser-based powder bed fusion (L-PBF), offers an enabling opportunity for making complex metal parts or customized alloys with design freedom. The unique thermal cycle of rapid heating, fast solidification, and melt-back during metal AM may cause very complex metal pool dynamics, such as steep temperature gradient and high cooling rate, intense Marangoni flow, and intrinsic cyclic heat treatment. The complex very complex kinetic process and thermal history may lead to various quality issues of the printed parts. Therefore, the understanding and prognosis of metal pool dynamics remain the central intractable problem for printing high-quality metal parts or new alloys. Computational fluid dynamics (CFD) models may help to understand the complex thermo-mechanical process physics, but require the calibration of model parameters and are computationally expensive for real-time prognosis. On the other hand, machine learning has the potential to handle high-dimensional and massive process data for efficient surrogate modeling and decision-making. However, pure data-driven machine learning models suffer from black-box or explainability, are inherently computation-intensive and storage-intensive, and need a large amount of high-quality labeled training data to achieve a good performance. A deep knowledge gap exists between machine learning modeling and computational modeling in the prediction of melt pool dynamics. To take full advantage of ML methods while leveraging the physical laws underpinning melt pool dynamics, this talk presents a physics-informed machine learning (PIML) approach to integrate deep learning with the governing equations of the melt pool for forward prediction of the temperature and velocity fields in the melt pool. The PIML approach may also inverse learning of unknown model constants (e.g., Reynolds number and Peclet number) of the governing equations. The robust PIML algorithm also shows fast convergence by enforcing physics via soft penalty constraints.
Biographical Sketch: Dr. Yuebin Guo is Henry Rutgers Professor of Advanced Manufacturing and Leads the New Jersey Advanced Manufacturing Institute at Rutgers University-New Brunswick, USA. Prior to Rutgers, he served as the Assistant Director for Research Partnerships at the U.S. Advanced Manufacturing National Program Office (AMNPO). He was also an Alexander von Humboldt Fellow at RWTH Aachen and Fraunhofer IPT, Aachen, Germany. His research focuses on manufacturing processes, digital twins, physics-informed machine learning, and materials informatics. He is the author of more than 300 peer-refereed technical publications in these areas. He is a recipient of numerous awards, including the SME Sargent Progress Award, ASME Federal Government Swanson Fellow, Tau Beta Pi Outstanding Faculty, NSF CAREER, SAE Teetor Educational Award, and SME Outstanding Young Manufacturing Engineer. He is an elected fellow of ASME, SME, and CIRP.
Abstract: Thermoelectric coolers (TECs), also known as Peltier coolers, are solid-state cooling devices powered by a direct (dc) current. They offer high reliability, silent operation, and do not require the use of refrigerant chemicals that can be harmful to the environment. However, TEC performance is generally limited as compared to traditional vapor-compression refrigeration systems and this has limited their relevance and applicability, especially when other necessary system components, such as heat exchangers, are considered. RTX Technology Research Center (RTRC) has developed an advanced heat exchanger / heat sink technology on a DARPA program that provides >50% enhancement in heat removal at equivalent operating conditions. Such an improvement means that an air-cooling system can deliver heat-removal performance that approaches that of liquid cooling. When these DARPA heat exchangers are combined with TECs, the resulting cooling system can, for the first time, perform to the level required for galley refrigeration on commercial aircraft. The resulting system, known as SpaceChiller, comes at an opportune time and fills a technology need that is arising in the aerospace industry as airlines start to fly increasingly long distances using small, single-aisle aircraft.
Biographical Sketch: Dr. Pearson has been with RTX Technology Research Center (RTRC, previously known as UTRC) in January 2011. Since then, he has worked on a wide range of projects spanning most of United Technologies’ and RTX’s diverse business units including Pratt & Whitney, Collins Aerospace, Raytheon, and Carrier. Major research areas have included advanced heat exchangers, eco-friendly refrigeration systems, thermally engineered metamaterials, and thermoelectric power generation and cooling. He became a Team Leader of the Heat Transfer team in November 2020. Since October 2022, he has been leading Thermofluid Science Discipline, a team of 15 staff that conduct high-risk and low-TRL research across RTX’s businesses, focused on the company’s unique challenges in heat transfer, fluid dynamics, large-scale thermodynamic systems, and interfacial physics. Dr. Pearson holds a Ph.D., M.S., and B.S. degree in Mechanical & Aerospace Engineering from the Illinois Institute of Technology in Chicago, IL, where he worked on NASA-sponsored work in electrodynamics and was an NSF Graduate Research Fellowship recipient. He has over 23 granted and pending patents and 10 peer-reviewed journal publications.
Three ME faculty will present their research. Come and learn about their exciting research, ask questions, and learn about research opportunities.
Prof. Mihai “Mishu” Duduta obtained his B.S. degree from the Massachusetts Institute of Technology in Materials Science Engineering and received his M.S. and Ph.D. degrees in Engineering Sciences from Harvard University. While at MIT he co-invented semi-solid electrodes for batteries, then after graduating, became the first employee of 24M Technologies, a battery start-up spun out to commercialize the technology. In 2019 he was a Bakken Medical Devices Innovation Fellow at the University of Minnesota – Twin Cities, focusing on finding soft robotic technological solutions to unmet clinical needs, then joined the University of Toronto as an assistant professor in Mechanical and Industrial Engineering until last year. His interdisciplinary research group is focused on soft transducers as building blocks for the next generation of soft machines that can interact safely with humans and disrupt medicine, manufacturing, communications and beyond.
Prof. Wajid Chishty joined the Department of Mechanical Engineering in January 2023. He has a PhD in Mechanical Engineering from Virginia Polytechnic & State University (2005), an MSE in Aerospace Engineering from University of Michigan (1996) and an MBA in Finance from University of Karachi (1991).He has more than 30 years of experience in the areas of gas turbine maintenance, repair and overhaul, combustion research and teaching. He has authored many well-cited publications and is a member of ASME, ASEE and AIAA. His research interests include dynamics of droplets and bubbles, thermoacoustics, aircraft performance and engineering management. He has held senior management positions managing technology transfers and directing applied research in the fields of sustainable aviation, urban air mobility and renewable energy. Prof. Chang Liu obtained his Ph.D. degree in Mechanical Engineering from Johns Hopkins University in 2021 and then conducted postdoctoral research at the University of California, Berkeley before joining UConn. His research interest is the intersection among fluid dynamics, nonlinear dynamical systems, control theory, state estimation and optimization with a special focus on turbulence. He is interested in developing novel interdisciplinary approaches to obtain reduced-order models and better understandings of fluid dynamics. His current research topics include wall-bounded shear flows, flow control, and thermal convection.
Approximately 537 million people worldwide are affected by diabetes mellitus, a condition characterized by high blood sugar levels. By 2030, it is projected that this number will increase to 643 million. Among individuals with diabetes, almost half are older adults aged 65 or above who have type 2 diabetes. As the global population ages and the number of people with diabetes continues to rise rapidly, this age-related disease poses a significant challenge in the medical and socioeconomic realms.
In people with diabetes and cardiovascular disease, the leading cause of death and disability is a condition called atherosclerotic vascular disease. This disease involves the progressive narrowing and hardening of blood vessels due to complex processes such as calcification, glycation, and crosslinking. However, identifying the specific molecules responsible for this degradation process remains a persistent challenge in the field.
On the other hand, gaining a deeper understanding of the causes of this disease can help us develop strategies to prevent, diagnose, or even reverse the loss of elasticity in arterial tissues.
The research funded by this new NIH R56 grant and carried out in Prof. Anna Tarakanova’s group, aims to develop such a deeper understanding by investigating the mechanical deterioration of arterial elastic tissue at various levels, ranging from the sub-molecular to tissue scales. The research will develop a computational framework that simulates and unravels the molecular mechanisms behind the mechanical deterioration of arteries during aging and disease.
