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.