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.

Abstract: The construction and operation of buildings contribute massively to global energy use and greenhouse gas emissions; therefore, buildings will play a central role in the path toward a sustainable, net zero, clean energy future. This presentation will give a high-level framing of buildings’ role in the 21^st century energy challenge, as well as associated opportunities and emerging research, development, demonstration, and deployment (RDD&D) that are developing in response. The talk will start out by quantifying buildings contributions to energy and emissions, and then highlight select ongoing programs and RDD&D efforts at the National Renewable Energy Laboratory (NREL).

Biographical Sketch: Dr. Wale Odukomaiya joined NREL’s Building Technologies and Science Center in 2018 as a Director’s Fellow. His research focuses on innovating heat transfer, energy storage, and functional materials in ways that improve building efficiencies and support low-carbon buildings. This research applies fundamental heat transfer, thermodynamics, and materials science to advanced energy technologies and building components, with an emphasis on thermal and electromechanical energy storage technologies; heating, ventilating, and air conditioning (HVAC); and advanced manufacturing of related components. Prior to joining NREL, Dr. Odukomaiya was a postdoctoral research fellow in the Building Technologies Research and Integration Center at Oak Ridge National Laboratory, where he worked on the development of energy storage and magnetocaloric refrigeration technologies. His research background includes developing advanced energy technologies and building components, energy policy and economics, and thermal and electro-mechanical energy storage.

Abstract: Metamodels, or models of models, map defined model inputs to defined model outputs. When metamodels are constructed to be computationally cheap, they are an invaluable tool for applications ranging from topology optimization, to uncertainty quantification, to real-time prediction, to multi-scale simulation. In particular, for heterogeneous materials, metamodels are useful for exploring the influence of the (potentially massive) heterogeneous material property parameter space. By nature, a given metamodel will be tailored to a specific dataset. However, the most pragmatic metamodel type and structure will often be general to larger classes of problems. At present, the most pragmatic metamodel selection for dealing with mechanical data — specifically simulations of heterogenous materials — has not been thoroughly explored. In this work, we draw inspiration from the benchmark datasets available to the computer vision research community. These benchmark datasets have both made it feasible to compare different methods for solving the same problem, and inspired new directions for method development. In response, we introduce benchmark datasets for engineering mechanics problems (for example, the Mechanical MNIST Collection https://open.bu.edu/handle/2144/39371 [1,2,3, 4]). Then, we show some example problems that we are exploring with these datasets such as our methodology for constructing metamodels for predicting full field quantities of interest (e.g., full field displacements, stress, strain, or damage variable), for leveraging information from multiple simulation fidelities, and for creating well calibrated models. Looking forward, we anticipate that disseminating both these benchmark datasets and our computational methods will enable the broader community of researchers to develop improved techniques for understanding the behavior of spatially heterogeneous materials. We also hope to inspire others to use our datasets for educational and research purposes, and to disseminate datasets and metamodels specific to their own areas of interest (https://elejeune11.github.io/).

Biographical Sketch: Emma Lejeune is an Assistant Professor in the Mechanical Engineering Department at Boston University. She received her PhD from Stanford University in September 2018, and was a Peter O’Donnell, Jr. postdoctoral research fellow at the Oden Institute at the University of Texas at Austin until 2020 when she joined the faculty at BU. At BU, Emma has received the David R. Dalton Career Development Professorship, a Computational Science and Engineering Junior Faculty Fellowship, the Haythornthwaite Research Initiation Grant from the ASME Applied Mechanics Division, and the American Heart Association Career Development Award. Current areas of research involve integrating data-driven and physics based computational models, and characterizing and predicting the mechanical behavior of heterogeneous materials and biological systems.

Abstract: Sickle cells are known for their significantly shortened lifespan (10-20 days), which is much shorter than the lifespan (~120 days) of the normal red blood cells (RBCs). Similar to normal RBCs, sickle cells are also challenged by repeated hypoxia cycles as well as mechanical fatigue. To examine the impact of these repeated challenges toward the progressive degradation process of RBCs, we have developed in vitro microfluidic assays for testing RBCs in health and disease under cyclic hypoxia loading or cyclic mechanical loading. Both types of fatigue loading are found to cause significant RBC degradation in a cumulative manner. More importantly, our results show that sickle cells on average degrade much faster than normal healthy RBCs. These results provide new insights into the possible mechanisms underlying the significantly shortened lifespan of sickle cells. The developed assays can be used for drug efficacy screening and potentially disease severity testing in a patient-specific manner.

Biographical Sketch: Ming Dao is the Principal Investigator and Director of MIT’s Nanomechanics Laboratory, and a Principal Research Scientist in the Department of Materials Science and Engineering at MIT. His research interests include nanomechanics of advanced materials, cell biomechanics/biophysics of human diseases, and machine learning for engineering and biomedical applications. He has published over 160 papers in peer-reviewed journals, including Science, Nature Materials, Science Advances, Nature Communications, PNAS, etc. He was ranked within the Top 2% Scientists list established by Ioannidis/Stanford University in all four updates published in June 2019 (single year), October 2020 (single year & career), October 2021 (single year & career), and November 2022 (single year & career). He is also ranked as a top 0.5% researcher in both citation and h-index by Exaly.com (March 2023).

He is a Fellow of the American Society of Mechanical Engineers (ASME) and named the 2012 Singapore Research Chair / Professor in Bioengineering and Infectious Disease by MIT. He was a visiting professor with the National Institute of Blood Transfusion, Paris, France (INTS, 2016-2017) and an adjunct professor with Xi’an Jiaotong University, Xi’an, China (2011-2020). Since 2018, he has been a visiting professor at Nanyang Technological University, Singapore. He has also chaired or co-chaired 18 international symposiums/workshops/webinar series.