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.

Abstract: Kansei Engineering (KE) is a method designed by Mitsuo Nagamachi in the 1980s to translate consumers’ feelings and perceptions of a product (Kansei) into design elements. Its applications are used for new product development cases commonly in the automotive, construction machinery, electric home appliances, office machinery, house construction, costume and cosmetic industries (Nagamachi, 2002).  The word Kansei generally refers to sensitivity, sensibility, feeling and emotion.  In product design discipline, understanding user behavior and feelings and applying them to artifacts is crucial. Kansei Engineering providing data about the emotional connections between the design features and user perceptions, clearly defines the problem space by starting with the span the semantic space and span the space of properties steps where the possible/potential design features are selected to be tested (Schütte et al. 2004). It enables modelling the relationship between the design features and the corresponding feelings of the users empirically with quantitative data analysis. This talk will review our research between 2017 and 2022, on application of Kansei Engineering methodology in design process of novice designers (Erol, 2022; Erol & Leblebici Basar, 2020; 2022).

Biographical Sketch: Deniz Leblebici-Basar, Ph.D. is assistant professor at Istanbul Technical University, Istanbul, Turkey. Has received her Doctoral, Master of Science and Bachelor of Science degrees in Industrial Design from Istanbul Technical University. She has been serving as a researcher and faculty at Istanbul Technical University since 2003. She studied design cognition and worked as a research scholar at the Cognition and Language Lab, University at Albany, State University of New York, Albany, U.S.A., in 2009 and 2015. She has been awarded several national and international grants on her academic research areas; cognitive processes of designing activity and cognitive modeling of the design process, user experience design, user experience psychology and university- industry collaborations. Between 2016-2018 she has served as Vice Dean responsible of administrative services at the Faculty of Architecture, ITU. Between 2018-2020 she has served as Visual Communications Director of Istanbul Technical University. She is in the editorial board of AZ ITU Journal of Architecture since 2020.

Abstract: A story about how focus led Brian into mechanical engineering, the field of design, and the various journeys that these foundations led to. This includes the design of such wide ranging products as automated cow milking systems, combustion engines and table saw safety systems. It also includes starting a company with the added challenge of hardware in the aftermath of the 2008 recession by being the first startup to ever crowdfund. He’ll share how he raised millions in venture capital, fell off the venture track in 2016 and learned how to build a business from there profitably – as well as his experience building overseas offices in Asia and Ukraine.  And he’ll share how his company keeps innovating after ten years in business, including his latest solution to foster the development of the skill of focus in education.

Biographical Sketch: Brian is the co-CEO and co-Founder of Swivl, Inc. – an educational technology company with solutions in over 50,000 schools and universities worldwide.  Brian has a BS in Mechanical Engineering at the University of New Hampshire and has an MS in Mechanical Engineering from Stanford University with a depth in Design. Since Stanford, he was a lecturer at Stanford – teaching Introduction to Visual Thinking to product design majors – and worked in diverse industry markets like consumer electronics, power tools and medical devices as a product design consultant. He founded Swivl in 2010 and grew it quietly into one of the more successful robotics companies of the last decade with over 150 employees worldwide. He’s a serial innovator and continues to develop new hardware and software solutions for the educational market to this day.