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.

Abstract: Topology optimization has emerged as a promising and powerful approach to design engineered materials and components. Initially restricted to two-phase, solid-void design problems in linear elasticity, topology optimization approaches for multi-physics and multi-material problems have emerged. These problems are often dominated by interface phenomena, such as contact and delamination at material interfaces and boundary layer effects at fluid-solid interfaces. Accurately modeling these phenomena and, at the same time, allowing for topological changes in the optimization process pose interesting challenges on the formulation of the design optimization problem, the physics model, and the discretization method.

This talk will provide an overview of topology optimization approaches for problems, reviewing both density and level set topology optimization methods. This overview will show that level set methods combined with immersed finite element approaches provide a promising framework, especially for coupled multi-physics and multi-material topology optimization problems. The accuracy, robustness, and accuracy of the finite element analysis play a crucial role for such problems. This talk will present an isogeometric formulation of the eXtended Finite Element Method where the level set and state variables fields are discretized on adaptively refined meshes, using truncated hierarchical B-splines. Using approximate Lagrange extraction, this formulation can be integrated in standard finite element solvers.

The characteristics of this XFEM analysis and level set topology optimization framework will be illustrated with 2D and 3D problems in solid and fluid mechanics, including elastic, flow, and conjugate heat transfer problems.

Biographical Sketch: Dr. Maute is the Palmer Endowed Chair and a professor in the Ann and H.J. Smead Aerospace Engineering Sciences Department at the University of Colorado Boulder. Dr. Maute received a Bs/Ms. in Aerospace Engineering in 1992 and Ph.D. in Civil Engineering in 1998, both from the University of Stuttgart, Germany. After working as a postdoctoral research associate at the Center for Aerospace Structures, he started his faculty position at CUB in 2000. His research is concerned with computational mechanics and design optimization methods. He focuses on fundamental problems in solid and fluid mechanics and heat transfer with applications to aerospace, civil, mechanical engineering problems. For the past 30 years, Dr. Maute has worked on topology and shape optimization methods for a broad range of problems focusing on coupled multi-physics and multi-scale problems, such as fluid-structure interaction and chemo-mechanically coupling. Dr. Maute has published his work in over 200 journal articles, book chapters, and conference proceedings.

Abstract:

The emerging economic and societal needs such as advanced manufacturing, environmental treatment, and space exploration call for machines that can operate in harsh and complex environments. An attractive approach is to utilize a new paradigm of physical intelligence in material development: a rational material design will enable its on-board actuation, sensing, and analysis, without a need for central computing or complex control. This talk will present our recent progress in the fundamental research of embedding physical intelligence in soft active materials. A stretchable polymer network responds to an external stimulus such as light or heat, dramatically changes its shape or material property, and enables special functionality in its bulk or surface. The first part of the talk presents photoactive liquid crystal elastomers that can change their shape and generate work output under light illumination or temperature change. Emphasis is placed on the fundamental photo-thermo-mechanical coupling across many length scales, especially at the mesoscale where the polymer network and liquid crystal mesogens behave collectively, leading to multiple interesting phenomena and their consequences in the macroscopic actuation. The second part of the talk presents temperature-switchable adhesives with high adhesion strength, large switching ratio, fast switching speed, and good reversibility. A polymer network containing many free-end dangling chains is a strong adhesive at ambient environment due to the long chains, dense physical bonds, and large dissipation from the polymer matrix, and is completely non-adhering at an elevated temperature due to its thermo-responsive phase transition. This talk is hoped to help advance the fundamental knowledge of soft active materials, bring together communities of relevant research fields, and expand the potential large-scale applications.

Bio:

Ruobing Bai is an assistant professor in the Department of Mechanical and Industrial Engineering at Northeastern University. He received his BS in Theoretical and Applied Mechanics at Peking University in 2012, and PhD in Engineering Sciences at Harvard University in 2018. He was a postdoctoral fellow in the Department of Mechanical and Civil Engineering at California Institute of Technology from 2018 to 2020. He is the recipient of the Chun-Tsung Scholar in Peking University, the Haythornthwaite Research Initiation Award from the Applied Mechanics Division of American Society of Mechanical Engineers (ASME), and the Extreme Mechanics Letters (EML) Young Investigator Award. Research in the Bai group aims to combine theory and experiment in areas including solid mechanics, soft active materials, fracture and toughening of materials, adhesion, and sustainable materials, for applications such as soft robotics, advanced manufacturing, human-machine interfaces, and human health.

Abstract: The properties of materials are highly dependent on their structures, which include morphologies, grain boundaries, phases, atomic structures, Etc. In particular, the atomic structures determine the limit of their properties, which are the unique characteristics of each material. However, predicting physical properties and understanding their origins demand accurate electronic structure and atomic structure calculations, which require a lot of computational resources. Over the past few decades, computational power has been tremendously improved, enabling atomistic simulations on a reasonable length and time scale to answer such questions. Accordingly, high-throughput calculations, data mining, and machine-learning(ML) solutions have been becoming mainstream in materials research.

In this talk, we introduce our effort to understand the atomic structure and materials property relationship to practical application in the next-generation battery and semiconductor materials using atomistic simulation tools such as; ab initio Density Functional Theory, Classical Molecular Dynamics, and ML algorithms. First, we present newly developed active materials for next-generation rechargeable battery applications, which include novel cathode and electrolyte materials. And then, we exhibit the structure-property relationship to describe dielectric properties using the ML algorithm and intuition of fundamental physics. These examples will sufficiently show atomistic simulation’s practical application to materials research.

Biographical Sketch: Dr. Shin is a Sr. Staff Engineer and Project Leader in the Advanced Materials Lab at Samsung Semiconductor Inc (SSI). He studies theoretical and computational materials science through computational modeling, simulation, and Artificial-intelligence driven materials discoveries for energy harvesting, conversion, and storage materials.

Dr. Shin received his Ph.D. in Materials Science and Engineering at Boston University in 2012 and held a Chemistry Postdoc Fellow position in the Energy Storage and Distributed Resources Division at Lawrence Berkeley National Laboratory. He worked as Research Engineer at Samsung Research America before joining SSI.