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.

Abstract: The human brain undergoes a myriad of changes during its lifetime. From a mechanics perspective alone, it is mesmerizing how the brain develops during early life, transforms into this highly functional, albeit still very enigmatic, organ that makes us unique, and is subjected to injury, disease, and ultimately age-related degeneration like every other part of the body. Despite extensive efforts to mechanically characterize brain tissue for more than two decades, the relationship between microstructure, state of health, and mechanical behavior remains elusive. On the modeling side, the computational biomechanics community has had extensive interest in modeling traumatic brain injury, neurodegeneration, stroke, surgical guidance, and, the most intensely studied, brain folding during early development. Our group’s motivation to pursue multiphysics modeling of the brain is simple: while the biology of brain aging and many neurological diseases is very well established, its coupling to the brain’s mechanical response in the form of cerebral atrophy and tissue degeneration/damage remains understudied.

In the present talk, we will explore our work on inferring the growth field during brain development, modeling brain shape changes during Alzheimer’s disease, and the mechanical origin of white matter degeneration during brain aging.

 

Biographical Sketch: Johannes Weickenmeier is an assistant professor of mechanical engineering and the director of the Center for Neuromechanics at Stevens Institute of Technology. Dr. Weickenmeier leads the Soft Matter Biomechanics Laboratory that combines medical image analysis, mechanical testing, and numerical methods to understand and predict soft tissue behavior. His group’s current work focuses on understanding and developing physics-based models that describe brain changes during develop, healthy aging, in Alzheimer’s disease, and multiple sclerosis. For more information, go to www.weickenmeierlab.com or follow his group on Twitter @weickenmeierlab.

Abstract: The behavior of materials involve physics at multiple length and time scales: electronic, atomistic, domains, defects, etc.  The engineering properties that we observe and exploit in application are a sum total of all these interactions.  Multiscale modeling seeks to understand this complexity with a divide and conquer approach.  It introduces an ordered hierarchy of scales, and postulates that the interaction is pairwise within this hierarchy.  The coarser-scale controls the finer-scale and filters the details of the finer scale.   Still, the practical implementation of this approach is computationally challenging.  This talk introduces the notion of neural operators as controlled approximations of operators mapping one function space to another and explains how they can be used for multiscale modeling.  They lead to extremely high-fidelity models that capture all the details of the small scale but can be directly implemented at the coarse scale in a computationally efficient manner.  We demonstrate the ideas with examples drawn from first principles study of defects and crystal plasticity study of inelastic impact.

Biographical Sketch: Kaushik Bhattacharya is Howell N. Tyson, Sr., Professor of Mechanics and Professor of Materials Science as well as the Vice-Provost at the California Institute of Technology. He received his B.Tech degree from the Indian Institute of Technology, Madras, India in 1986, his Ph.D. from the University of Minnesota in 1991, and his post-doctoral training at the Courant Institute for Mathematical Sciences during 1991-1993. He joined Caltech in 1993. His research concerns the mechanical behavior of materials, and specifically uses theory to guide the development of new materials. He has received the von Kármán Medal of the Society of Industrial and Applied Mathematics (2020), Distinguished Alumni Award of the Indian Institute of Technology, Madras (2019), the Outstanding Achievement Award of the University of Minnesota (2018), the Warner T. Koiter Medal of the American Society of Mechanical Engineering (2015) and the Graduate Student Council Teaching and Mentoring Award at Caltech (2013).  He served as the editor of the Journal of Mechanics and Physics of Solids during 2004-2015.

Abstract 

In numerical modeling of planetary and stellar convection, taking into account compressibility effects is crucial. However, using the exact equations may not be feasible due to the generation of fast acoustic waves, which distract from the slower convective motions caused by buoyancy. The Oberbeck-Boussinesq model simplifies the calculations by suppressing the acoustic waves making it easier for numerical simulations, but is so simple and pressure effects are relegated to a secondary role. Intermediate models, such as the anelastic and anelastic liquid models, have also been proposed to balance simplicity and accuracy. 

We investigated compressible convection under several different approximations for the thermodynamic state as well as using the exact equations. We tested two different classes of equations of state (EoS): one where entropy depends only on density, resulting in nearly constant density and minimizing non-Oberbeck-Boussinesq effects, and the Birch-Murnaghan equations of state, which are realistic models for condensed matter like the Earth’s mantle and core.  Our study showed that dissipation is closely linked to the fraction of heat flow carried by entropy flux. Additionally, we observe that small-scale convection is prevalent in the flow structure. Our results are mostly discussed in the framework of mantle convection, but the EoS is flexible enough to be applied in the inner core or in icy planets. 

 

Bio 

Jezabel Curbelo is a Ramon y Cajal Research Fellow at Barcelona School of Industrial Engineering at Universitat Politècnica de Catalunya, and currently visiting the Department of Earth and Planetary Sciences at Harvard University. She has previously held positions at various universities, including the Department of Atmospheric and Oceanic Sciences at UCLA and the Laboratoire de Géologie de Lyon. Her PhD thesis (Universidad Autónoma de Madrid, 2014) was awarded with the “2015 Donald L. Turcotte Award” (American Geophysical Union). She has received several awards for her research in geophysical fluid dynamics including the ”Leonardo Fellowships 2022” (BBVA Foundation) and the ”2021 L’Oréal-UNESCO For Women in Science” award (L’Oréal Spain). Her research focuses on the simulation and modeling of nonlinear fluid processes in the ocean and atmosphere and the analysis of convective motions in planetary mantles. Her webpage is web.mat.upc.edu/jezabel.curbelo/.

Abstract:

Turbulence is ubiquitous in natural and engineering systems. It can suppress energy loss in fusion reactors, affects stellar formation, has first order effects on processes critically important to society such as mixing of chemicals and pollutants in the atmosphere and oceans, climate dynamics and high-speed flight. It is therefore critically important to develop fundamental understanding of turbulent processes to improve predictive capabilities of turbulent fluid systems. An important hurdle in characterizing turbulent flows is the presence of extreme events, e.g. in dissipation, velocity gradients etc. These events are often very high-dimensional in nature and require large degrees of freedom/grid points to resolve accurately in simulations. The extreme events become stronger at high Reynolds number (Re, parameter characterizing the strength of turbulence) that are characteristic of realistic flows. Therefore, the focus of much of turbulence research has been to simulate very high-Re flows. This is a challenging computational task as the computational work load can grow as steeply as the fourth power of Reynolds number. Direct simulations of complex turbulent flows at realistic conditions currently remain elusive on the largest supercomputers. In this talk, we present a new theoretical perspective on understanding high-Re turbulence using well-resolved simulations at low to moderate-Re, that can be simulated on supercomputers available today. We will show that features of high-Re turbulence can be studied at finite and small values of Re and they are predictive of the infinite-Re limit. The simulations have the finest small-scale resolution in literature and long time-series. A primary focus is on the universality of small-scales and the scaling of extreme events. The consequences of these fundamental insights on modeling approaches, phenomenological and data-driven, in complex turbulent flows will also be discussed. The work also provides a new perspective on computational study of complex systems at very high values of dynamically relevant parameters. 

 Bio:

Sualeh Khurshid is a Computing Innovation Fellow and Postdoctoral Associate in Mechanical Engineering at Massachusetts Institute of Technology. His research is focused on understanding fundamental characteristics of complex turbulent flows in various regimes using direct numerical simulations and theory. His work includes developing high performance simulation codes and appropriate numerical methods to guide the development of reduced order models using phenomenological and data-driven methods. He completed undergraduate programs in Aerospace Engineering and Physics in 2016 and earned his Ph.D. in Aerospace Engineering in 2021 at Texas A&M University.  

Abstract: The current energy infrastructure is dominated by the combustion of the finite resources of fossil fuels leading to the release of more than 35 billion tons of carbon dioxide (CO₂) per year and, in turn, environmental concerns that are intensifying every year. Therefore, there is an urgent need to manage the available primary energy resources judiciously and devise and implement thermally efficient energy systems and infrastructures to minimize electricity consumption and new CO₂ emission. These thermally driven energy systems can replace their electricity-driven counterparts for applications involving gas separation, space and water heating, space cooling, refrigeration, and energy storage. They can offer additional advantages such as (a) the absence of moving parts, enhancing durability, (b) the option to use nontoxic, non-flammable working fluids, such as water, and (c) low capital cost. However, the state-of-the-art heat-driven adsorption systems, known as temperature swing adsorption (TSA) systems, must undergo a significant overhaul should the electrically-driven systems be replaced with heat-driven systems. Major bottlenecks in their implementation include sluggish heat and mass transfer in porous packed-bed designs that use large adsorbent pellets and large footprints. Additionally, the low thermal conductivity of the adsorbent materials makes their rapid heating and cooling difficult, which is worsened by the presence of void spaces. As a result, their performance remains poorer than the electrically-driven systems.

My talk will explore the design and development of these energy systems from the ground up. I will explore an energy-efficient adsorption heat pump, which uses adsorbent-coated microchannels in detail. Using coated channels results in an operation with a heating time of less than 10% of the total cycle time, opening the possibility for the near-continuous heat pump operation. This highly asymmetric heat pump operation eliminates the primary implementation barrier associated with using an adsorption system in mainstream commercial cooling and heating applications. Silica gel-water pair used in a contactor of the size of a typical refrigerator compressor can provide 300 W of cooling at 5°C with a primary energy COP of 0.25. Tremendous improvement in this performance is possible using high-performance water adsorbents like MIL-101 (Cr). Therefore, along with these feasibility studies, it becomes imperative to understand how to fabricate and characterize these channels and demonstrate their performance through uptake and breakthrough analyses.

Meanwhile, this synthesis step gives rise to several complementary research avenues in particulate flows, additive manufacturing of adsorbent layers, and the rheology of adsorbent slurries, which will be discussed. I will also briefly talk about CO₂ capture and thermal energy storage using this technique. A diverse portfolio of such technologies should contribute toward the rise of the sustainable energy landscape in the near future. 

Biographical Sketch: Darshan joined Florida Tech as an assistant professor of Mechanical Engineering in the Department of Mechanical and Civil Engineering in Spring 2020. He is the principal investigator of the Adsorption and Energy Technology Lab (AETL) at Florida Tech (https://research.fit.edu/pahinkar/). His research focuses on developing scalable and sustainable energy conversion and storage systems using computational and experimental techniques, characterizing integral fundamental transport phenomena, and demonstrating their practical applications. He advises three Ph.D. and three M.S. students, who lead research on various topics based on these energy systems. Before this appointment, Darshan received his B.E. in Mechanical Engineering from the Government College of Engineering, Pune, India, in 2006 and his M.E. in Mechanical Engineering from the Indian Institute of Science, Bangalore, India, in 2009. For the next two years, he worked as a Manager (Development) in Tata Motors Engineering Research Center, Pune, and his work involved thermal management of automobiles. Darshan graduated with a Ph.D. in Mechanical Engineering from Georgia Tech in the fall of 2016. He was a post-doctoral fellow at Georgia Tech Electronics Manufacturing and Reliability Laboratory before joining Florida Tech.

Abstract: The most recent round of climate model physics development at the NASA Goddard Institute for Space Studies (GISS) relied heavily on a library of large-eddy simulation case studies that served as observationally informed benchmarks for the ModelE3 climate model in single-column model mode. Parameter uncertainties were then inputs to an atmosphere-only multi-parameter tuning against satellite data sets, guided by machine learning. Large-eddy simulation case studies are also serving as testbeds for improving understanding of mixed-phase cloud microphysical processes, developing satellite retrieval algorithms, and testing ground- and spaceborne radar and lidar forward simulation software for the GISS climate model. Ongoing work is leading to new and improved case studies for GISS climate model development and other community uses.

Biographical Sketch: Dr. Fridlind’s studies of cloud microphysical properties and processes have concentrated at the intersection of detailed models and rich observational data sets, with an emphasis on aerosol-cloud interactions in ice-containing clouds that are most relevant to climate. Her studies have spanned mixed-phase stratiform clouds from Arctic to Antarctic, tropical to mid-latitude deep convection, mid-latitude continental cumulus and synoptic cirrus, and subtropical stratocumulus. She is a developer of ice microphysics schemes in the DHARMA large-eddy simulation code and, more recently, ice- and mixed-phase microphysics and macrophysics of stratiform clouds in the GISS ModelE3 Earth system model.

Abstract: Elastic metamaterials are structural materials that owe their unique wave manipulation capabilities to their complex internal architecture. Topological metamaterials are a special subclass of metamaterials whose behavior is directly controlled by the topology of their phonon bands. In this talk, I discuss the mechanics of a class of metamaterials known as topological Maxwell lattices. While these systems have been the object of extensive theoretical investigation, their classical treatment has been limited to ideal configurations and confined to the static limit. I will address the opportunities for design that open up when we account for the effect of structural non-idealities and we shift our focus to the dynamic behavior.

I will first discuss the dynamics of lattices in which the ideal hinges that appear in the theoretical models are replaced by structural ligaments capable of supporting bending deformation – a scenario practically encountered in lattices fabricated using cutting techniques or 3D printing. Aided by laser vibrometry experimental data, I will show how the zero-energy floppy edge modes predicted for ideal configurations morph into finite-frequency wave modes that localize on selected edges, resulting in asymmetric wave transport regimes. I will then address whether the topological attributes of Maxwell lattices, which are native to in-plane mechanics, can be exported to the out-of-plane response. I will show that, through appropriate design principles, it is possible to design bilayer structures in which coupling mechanisms transfer the in-plane topological polarization of the individual layers to the out-of-plane degrees of freedom, leaving a signature of topological polarization in the flexural response.

Biographical Sketch: Stefano Gonella is a Professor in the Department of Civil, Environmental and Geo- Engineering at the University of Minnesota. He received Ph.D. and M.S. in aerospace engineering from Georgia Tech in 2007 and 2005, respectively, following a Laurea, also in aerospace engineering, from the Politecnico di Torino (Italy) in 2003. Before joining the University of Minnesota, he spent 3 years as a post-doctoral associate at Northwestern University. His research interests revolve around the modeling, simulation and experimental characterization of dynamical phenomena in architected materials, phononic crystals, and elastic metamaterials. His latest efforts have been directed towards understanding the role of topological states of matter in the design of mechanical metamaterials. He is also interested in the development of methodologies for structural diagnostics through the mechanistic adaptation of concepts of machine learning and computer vision. He was recipient of the NSF CAREER award in 2015.

Abstract: Accurate simulations of combustion and reacting fluid flows require complex, multi-step chemical kinetic models for describing the coupled chemical reactions. These models are often large and mathematically stiff, and contribute to the overall high computational expense of simulating practical phenomena relevant to energy, transportation, and aerospace applications. In this talk, I will introduce these issues, summarize the state-of-the-art in methods used to reduce computational costs, and describe some recent contributions from my group on adaptive preconditioning to accelerate implicit integration of stiff chemical kinetics. I will discuss how these developments, and others, are available in the open-source library Cantera. Finally, I will discuss how my group has extended strategies and methods from combustion modeling to other domains such as modeling of neutron transport and ocean biogeochemistry.

Biographical Sketch: Dr. Kyle Niemeyer is Associate Professor and Welty Faculty Fellow in the School of Mechanical, Industrial, and Manufacturing Engineering at Oregon State University. He received his PhD in Mechanical Engineering from Case Western Reserve University in 2013. Dr. Niemeyer’s research focuses on computational modeling of reacting and non-reacting fluid flows, with a particular interest in numerical methods and high-performance computing. He is also an ardent advocate of open science, and serves as Associate Editor-in-Chief at the Journal of Open Source Software. He is currently working as a AAAS Science and Technology Policy Fellow with the the Industrial Efficiency & Decarbonization Office at the US Department of Energy.