Abstract: Multiscale modeling methods are typically envisioned as precise and predictive simulation tools to solve complex science and engineering problems. However, even conventional atomistic models often lack computational efficiency and accuracy, making them inadequate for providing reliable information for large-scale continuum models. In this seminar, I will discuss the developments aimed at overcoming these critical limitations. 

At the beginning of the talk, I will introduce how atomistic models can enhance our understanding of the experimental observations of crystal growth in 2D materials using empirical reactive forcefield (FF). Although the models offer valuable insights at the atomic scale, the development of reliable FFs is severely limited due to the fixed potential expressions. Recently, neural network potentials (NNPs) have emerged to surpass the longstanding limitations of empirical potentials.

While NNPs can provide higher accuracy than other empirical FFs and lower computational costs compared to quantum calculations, efficient sampling or data generation for training becomes increasingly critical. I will present recent advancements in an automated active learning (AL) framework for NNPs, focusing on accurately simulating bond-breaking in hexane chains through steered molecular dynamics sampling with trained NNPs and assessing model transferability to other alkane chains.

In the end, I will introduce one of the sampling approaches, the multiorder-multithermal (MOMT) ensemble, to capture a broad range of liquid- and solid-phase configurations in a metallic system, nickel. Data distillation through active learning can significantly reduce sampled data without losing much accuracy. The NNP, trained from the distilled data, can predict different energy-minimized closed-pack crystal structures even though those structures were not explicitly part of the initial data. Moreover, this data can be applied to other metallic systems (aluminum and niobium) without repeating the sampling and distillation processes.

The capabilities developed through the research will provide valuable tools for a fundamental understanding of the chemical process and mechanistic insights into the predictive design and interpretive simulations of materials processes and properties.

Biographical Sketch: GS Jung is a Research Staff at Oak Ridge National Laboratory. His research interests are in the multiscale modeling of materials to understand their fundamental properties from synthesis and growth to performance and failures. Before joining ORNL, he earned his Ph.D. in multiscale modeling for 2D materials from the Massachusetts Institute of Technology.

Abstract: Mathematical and computational modelling approaches can quantify the mechanics & mechanical environment of soft biological tissues under physiological and pathological loading. These tools can quantify ‘mechanical stimuli’ inputs to algorithms that control growth and remodelling (G&R) of the tissue to simulate adaptation to altered environmental conditions. In this talk, I will overview a rate-based constrained mixture G&R modelling approach which was developed to create the first computational model of aneurysm evolution [1]; I will then summarise its sophistications and applications over the past 20 years. In general, the modelling approach is as follows: the constitutive model of the tissue accounts for the individual natural reference configurations of cells and matrix components; a model of the organ/tissue is defined/calibrated to initially be in homeostasis in the physiological loaded configuration; subsequent tissue loss/damage or changes to the mechanical environment can change the distribution of mechanical stimuli from homeostatic setpoints and drive G&R processes that lead to the progression of disease or the adaptation to a new homeostatic state. Illustrative applications of the approach will include fluid-solid-growth frameworks for modelling intracranial aneurysm evolution [2], in vivo-in vitro-in silico modelling of bladder adaption to outlet obstruction [3] and a conceptual chemo-mechano-biological model of cartilage evolving in health, disease and treatment [4]. Outlook for future research and clinical translation of the models will be discussed.

References:

[1] Watton, Hill & Heil (2004) A Mathematical Model for the Growth of the Abdominal Aortic Aneurysm, BMMB, 3:98-113.

[2] Teixeira et al. (2020) Modeling intracranial aneurysm stability and growth: an integrative mechanobiological framework for clinical cases. BMMB, 19:2413–2431.

[3] Cheng et al. (2022) A constrained mixture-micturition-growth (CMMG) model of the urinary bladder: Application to partial bladder outlet obstruction (BOO), JMBBM, 134: 105337.

[4] Rahman et al. (2023) A chemo-mechano-biological modelling framework for cartilage evolving in health, disease, injury, and treatment, CMPB, 231: 107419.

 

Biographical Sketch: Paul Watton is Head of the Complex Systems Modelling Group and Professor of Computational and Theoretical Modelling at the Dept. of Computer Science & Insigneo Institute for in silico Medicine at the University of Sheffield, UK. He also holds an adjunct position with the Dept. of Mech. Eng. and Materials Science, University of Pittsburgh, US. He has a mathematical background (BSc Pure & Applied Mathematics, MSc Mathematical Logic, PhD Applied Mathematics) and his research focuses on modelling the biomechanics & mechanobiology of soft-biological tissues with application to disease progression and treatment.

Abstract: Soot is a major air pollutant produced by incomplete combustion of hydrocarbon fuels. The contribution of soot to global warming is currently estimated with large uncertainty (partly) due to the fractal-like agglomerate structure of its constituent nanoparticles. Here, the dynamics of soot nanoparticles are investigated to advance our current understanding of particle formation during combustion. Discrete element modeling (DEM) enables the detailed description of the particle morphology (doi.org/10.1016/j.proci.2016.08.078) and optical properties (doi.org/10. 1016/j.proci.2018. 08.025) in population balance models and computational fluid dynamics (doi.org/10.1016/j.combustflame.2021.01.010). Power laws relating the optical properties of soot to its filamentary structure are derived by DEM (doi.org/10.1016/j.carbon.2017.06.004) to facilitate the accurate monitoring of soot emissions by aerosol (doi.org/10.1016/j.proci.2020. 07.055), laser (doi.org/10.1016/j.combustflame.2022.112025) diagnostics and fire detectors (doi.org/10.1016/j.powtec.2019.02.003). Most importantly, these relations enable the estimation of the soot direct radiative forcing accounting for its realistic agglomerate structure (doi.org/10.1021/acs.est.2c00428).

Biographical Sketch: Dr. Georgios Kelesidis is an Assistant Professor at Rutgers School of Public Health and Deputy Director of the Nanoscience and Advanced Materials Center of the Environmental and Occupational Health Sciences Institute at Rutgers University. Prior to this appointment, he was a Lecturer and Research Associate at the Department of Mechanical and Process Engineering of ETH Zürich, Switzerland. He received a Diploma in Chemical Engineering from the University of Patras, Greece with honors (top 3%), along with the Limmat Stiftung Award of Academic Excellence (2013). His subsequent MSc studies in Process Engineering at ETH Zürich were supported by a Particle Technology Laboratory Fellowship (2013-2015), while his MSc thesis earned the IBM research prize (2017) for computer modelling and simulations in chemistry, biology and material science. His 2019 PhD thesis on the morphology and optical properties of flame-made nanoparticles received the 2020 PhD Award from GAeF (German Association for Aerosol Research) and the ETH medal for Outstanding Doctoral Thesis (top 8 %). He received also the 1st Graduate Student Award on Carbon Nanomaterials at the 2019 AIChE Annual Meeting (Orlando, FL, USA), as well as Best Poster Awards at the European Aerosol Conference (EAC) in 2016 (Tours, France) and 2020 (Aachen, Germany), the 2019 ETH Conference on Combustion Generated Nanoparticles (Zürich, Switzerland) and the 2019 Fall Meeting of the Material Research Society (MRS). The societal impact of his PhD research was also highlighted by the Forbes Magazine by including him in the 2020 Forbes 30 under 30 Europe list for Science & Healthcare. He has co-authored 21 peer-reviewed articles so far, being the first author in 16 of them. He has organized technical sessions at MRS (2016), EAC (2019-2021), the 2020-2022 Annual Meetings of the American Association for Aerosol Research, the 11th International Aerosol Conference (2022) and the 9th World Congress on Particle Technology (2022). He has supervised so far 10 MSc and 7 BSc students. He is currently co-supervising 1 PhD student at ETH Zürich.

Abstract: Recent decades have seen rapid development in all manufacturing technologies, including additive manufacturing (AM). This has raised the need for design methods to leverage the new, increasingly complex fabrication possibilities. Topology optimization has the potential to generate new high-performing design solutions since it is a free-form design method that does not require a preconceived notion of the final layout. It uses computational mechanics and optimization tools to generate improved designs. For operating designs to perform as predicted, the used model must capture the material behavior. Additionally, the planned manufacturing process might induce material characteristics and design limitations that should be considered as the design is generated. This talk focuses on identifying and incorporating behavioral and manufacturing aspects within the design process. Different strategies for integration within topology optimization will be discussed. This includes consideration of manufacturing-induced material characteristics, which is illustrated through tailoring design to material extrusion-based AM. In material extrusion, a nozzle moves across a build plate and deposits a material bead on a 2D slice of the design. These processes typically induce some degree of anisotropy through weak(er) bonding between adjacent beads. To improve the manufacturability of large-scale designs, the application of a Mixed Integer Linear Programming formulation is discussed for highly restricted volume scenarios. Finally, a new design framework is introduced in which the interactive participation of the design engineer is enabled to resolve more complex mechanic phenomena.

Biographical Sketch: Josephine Carstensen is the Gilbert W. Winslow Career Development (Assistant) Professor in the Department of Civil and Environment Engineering (CEE) at MIT. She leads the Carstensen Group, conducting research that revolves around the engineering question of “how we design the structures of the future?” Her work spans from the development of computational design frameworks for various structural types and design scenarios to experimental investigations that are used to inform necessary algorithmic considerations.

Dr. Carstensen has received awards for both research and teaching, including the National Science Foundation CAREER award and CEE Maseeh Award for Excellence in Teaching. She joined the MIT CEE faculty in 2019 after two years as a lecturer at MIT, jointly appointed in CEE and Architecture.  She received her PhD from Johns Hopkins University in 2017 and holds a B.Sc. and a M.Sc. from the Technical University of Denmark.

Abstract: Current robots are primarily rigid machines that exist in highly constrained or open environments such as factory floors, warehouses, or fields. There is an increasing demand for more adaptable, mobile, and flexible robots that can manipulate or move through complex environments. This problem is currently being addressed in two complementary ways: (i) learning and control algorithms to enable the robot to better sense and adapt to the surrounding environment and (ii) embedded intelligence in mechanical structures. My vision is to create robots that can mechanically conform to the environment or objects that they interact with to alleviate the need for high-speed, high-accuracy, and high-precision controllers. In this talk, I will give an overview of our key challenges and contributions to developing mechanically conformable robots, including soft parallel mechanisms for dexterous manipulation, physically-coupled multi-agent systems, and dynamic origami.

Biographical Sketch: Zeynep Temel is an Assistant Professor with the Robotics Institute at Carnegie Mellon University. Her research focuses on developing robots that can mechanically conform to the environment or objects that they interact with. Prior to joining RI, she was a postdoctoral Fellow at the Microrobotics Lab in Harvard University. She received her Ph.D. from Sabanci University, Turkey, where her work is funded by Turkish Science Foundation. In 2020, she was selected as one of 25 members of the Young Scientists Community of World Economic Forum.

Abstract: Mixing-induced vibrational non-equilibrium was studied in the turbulent shear layer between a high-speed jet and a surrounding hot-air co-flow. The vibrational and rotational temperatures of N2 and O2 were determined by fitting measured spontaneous Raman scattering spectra to a model that allows for different vibrational and rotational temperatures. The mixing of the jet fluid with the co-flow gases occurs over microsecond time scales, which is sufficiently fast to induce vibrational non-equilibrium in the mixture of hot and cold gases. The effect of fluctuating temperatures on the time-averaged Raman measurement was quantified using single-shot Rayleigh thermometry. The Raman scattering results were found to be insensitive to fluctuations except where the flame is present intermittently. Vibrational non-equilibrium was detected in nitrogen but not in oxygen. This difference between species temperatures violates the two-temperature assumption often used in the modeling of high-temperature non-equilibrium flow. A multiple-pass cell was constructed to obtain single-shot Raman scattering measurements in the turbulent shear layer using a pulsed stretched laser. These measurements agreed with the previous time-average results and allowed us to make measurements near the fluctuating base of a lifted flame – a region where time-averaged measurements do not give meaningful results.

Biographical Sketch: Prof. Philip L. Varghese holds the Ernest H. Cockrell Centennial Chair in Engineering at The University of Texas at Austin and has an international reputation in the areas of rarefied and non-equilibrium flows and optical diagnostics for combustion and plasmas. He received his Bachelor of Technology degree from the Indian Institute of Technology in Madras in 1976, an MS from Syracuse University in 1977, and a PhD from Stanford University in 1983 all in Mechanical Engineering. He was a post-doctoral Scholar in the Molecular Physics Laboratory at SRI International and joined UT Austin in 1983 in the department of Mechanical Engineering. He was promoted to Associate Professor in 1988 and transferred to Aerospace Engineering in 1989. He was promoted to full Professor in 1995 and has been the Director of the Center for Aeromechanics Research since 1999. He served as Chair of the Department from 2009-2012.

Among numerous awards he was Fulbright Senior Scholar in France in 1993 and was awarded the Boeing-A.D. Welliver Faculty Fellowship by the Boeing Company in 1998. He received the Lockheed Martin Aeronautics Company Award for Excellence in Engineering Teaching in Spring 2003, and was elected to the Academy of Distinguished Teachers at the University of Texas in 2005. In February 2012 he was selected Professor of the Year by the Senate of College Councils at UT Austin and was awarded The University of Texas System Regents’ Outstanding Teaching Award in August 2016.

Dr. Varghese’s research focuses on understanding the basic molecular processes occurring in high speed, high temperature, and non-equilibrium flows. This is an inter-disciplinary field, requiring a synthesis of physics and chemistry with the more traditional engineering disciplines of fluid mechanics, heat transfer, and thermodynamics. He applies his work to the study of hypersonic and rarefied flows, plasmas, and combustion. He has established a laser diagnostics laboratory for experimental studies in combustion and plasma discharges. He also has an active program in planetary scale simulations of rarefied flows and has developed a novel technique for accurate solutions of the Boltzmann equation using quasi-particle simulation. His research publications have been extensively referenced and a recent search showed over 3800 citations of his work on Google Scholar. He is co-inventor on six US patents related to applications of Raman spectroscopy.

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.