Author: Orlando E

Fourth Annual Women in STEM Conference Gains Traction

Panelists Ran Feng, Kristina Wagstrom, and Zongjie Wang during the Women in STEM Frontiers in Research Expo

Women in the science, technology, engineering, and mathematics fields are experiencing a new period of growth, acceptance, and respect in the modern workforce.

But when UConn alumna Jeanine Armstrong Gouin studied civil engineering in the 1980s, it was hard to feel welcome in an engineering building that didn’t even have a women’s bathroom.

Despite the dreary beginning, Gouin (who graduated in 1987, about four years before the Castleman Building installed women’s restrooms) delivered an inspirational message to an audience of young female STEM students last week.

The Women in STEM Frontiers in Research Expo (WiSFiRE) was held on Friday at the UConn Storrs campus. It brought together university undergraduate and graduate students, faculty, staff, alumni, and STEM employees and supporters.

WiSFiRE was one of the first conferences in the region to specifically highlight the work of women researchers in STEM. That mission has been solidified through a recent endowment by Gouin.

Read More @ UConn Today

Sooting tendency measurements for formulating sustainable fuels that reduce soot emissions

Dr. Charles McEnallySpeaker: Dr. Charles McEnally – Yale University
Date: Sep 13, 2024; Time: 2:30 PM Location: PWEB 175

Abstract: The transition from fossil fuels to sustainable fuels offers a unique opportunity to select new fuel compositions that will not only reduce net carbon dioxide emissions, but also improve combustor performance and reduce emissions of other pollutants.  A particularly valuable goal is finding fuels that reduce soot emissions.  These emissions cause significant global warming, especially from aviation since soot particles are the nucleation site of contrails.  Furthermore, soot contributes to ambient fine particulates, which are responsible for millions of deaths worldwide each year.  Fortunately, soot formation rates depend sensitively on the molecular structure of the fuel, so fuel composition provides a strong lever for reducing emissions.  Sooting tendencies measured in laboratory-scale flames provide a scientific basis for selecting fuels that will maximize this benefit.  We have developed new techniques that expand the range of compounds that can be tested by reducing the required sample volume and increasing the dynamic range.  This has many benefits, but it is particularly essential for the development of structure-property relationships using machine learning algorithms: the accuracy and predictive ability of these relationships depends strongly on the number of compounds in the training set and the coverage of structural features.

 

Biographical Sketch: Charles received a Ph.D. in Mechanical Engineering from the University of California at Berkeley in 1994, where he studied with Catherine Koshland and the late Robert Sawyer.  Since then, he has been in the Chemical Engineering Department at Yale University where he works with Professor Lisa Pfefferle.  His research interest is combustion of sustainable fuels.

 

Learning neural operators accurately, efficiently, reliably, and in one shot

dr. lu

Speaker: Dr. Lu Lu – Yale University
Date: Sep 20, 2024; Time: 2:30 PM Location: PWEB 175

Abstract: As an emerging paradigm in scientific machine learning, deep neural operators pioneered by us can learn nonlinear operators of complex dynamic systems via neural networks. In this talk, I will present the deep operator network (DeepONet) to learn various operators that represent deterministic and stochastic differential equations. I will also present several extensions of DeepONet, such as DeepM&Mnet for multiphysics problems, DeepONet with proper orthogonal decomposition or Fourier decoder layers, MIONet for multiple-input operators, and multifidelity DeepONet. I will demonstrate the effectiveness of DeepONet and its extensions to diverse multiphysics and multiscale problems, such as bubble growth dynamics, high-speed boundary layers, electroconvection, hypersonics, geological carbon sequestration, full waveform inversion, and astrophysics. Deep learning models are usually limited to interpolation scenarios, and I will quantify the extrapolation complexity and develop a complete workflow to address the challenge of extrapolation for deep neural operators. Moreover, I will present the first operator learning method that only requires one PDE solution, i.e., one-shot learning, by introducing a new concept of local solution operator based on the principle of locality of PDEs.

Biographical Sketch: Dr. Lu Lu is an Assistant Professor in the Department of Statistics and Data Science at Yale University. Prior to joining Yale, he was an Assistant Professor in the Department of Chemical and Biomolecular Engineering at University of Pennsylvania from 2021 to 2023, and an Applied Mathematics Instructor in the Department of Mathematics at Massachusetts Institute of Technology from 2020 to 2021. He obtained his Ph.D. degree in Applied Mathematics at Brown University in 2020, master’s degrees in Engineering, Applied Mathematics, and Computer Science at Brown University, and bachelor’s degrees in Mechanical Engineering, Economics, and Computer Science at Tsinghua University in 2013. His current research interest lies in scientific machine learning, including theory, algorithms, software, and its applications to engineering, physical, and biological problems. His broad research interests focus on multiscale modeling and high performance computing for physical and biological systems. He has received the 2022 U.S. Department of Energy Early Career Award, and 2020 Joukowsky Family Foundation Outstanding Dissertation Award of Brown University. He is also an action editor of Journal of Machine Learning.

Xu Receives the ASME Young Investigator Award

professor hong yi xu

Dr. Xu received the 2024 ASME Design Automation Young Investigator Award in recognition of his research at the intersection of Microstructure Material Design, design for Additive Manufacturing, Design of Mixed Stochasticity Structural Systems, and Uncertainty Quantification.

This award is presented annually to honor an exceptional young investigator making significant contributions in design automation, with a focus on areas such as design representation, optimization, evaluation, and integration. The award was given at the ASME Design Automation Conference, held from August 25-28, 2024, in Washington, DC. The conference gathers international experts in the field of design automation each year.

Driven to Succeed: UConn Formula SAE Makes History with 4th Place Win

The annual competition, organized by SAE International, challenges students to conceive, design, fabricate, develop, and compete with high performance “formula” vehicles.

The UConn Formula SAE team is revved up after earning a record-breaking ranking. During a three-day competition at Michigan International Speedway May 8-11, UConn competed against 118 other national and international teams and placed 4th overall, the highest in UConn’s history.

UConn Formula SAE has 80 members, of which 67 attended the competition at the Michigan International Speedway. This was the most of any team present. (Photo courtesy of Milton Levin)

“The team was totally ecstatic,” explains UConn Formula SAE (FSAE) President and mechanical engineering major Abhiemanyu Sukumaran ’24 (ENG). “As they were announcing the overall places, we heard 8,7,6, etc. Then they called our name, and everyone started jumping and screaming for joy! We celebrated like we won the national championship. It was bliss to have broken the record for our highest placement ever.”

Read more on the UConn Today website.

Droplets under Extreme Conditions: A shocking story

Abstract

I will first present a portable setup to generate shock waves using the exploding wire technique. Subsequently, I will showcase how droplets of various kinds (liquid metal, water, and polymeric liquids) interact and breakup in the shock wave and associated flow. I will also show the various instabilities that develop prior to breakup that are universal in nature. Lastly, I will showcase some results on shock-droplet flame interactions with analyses on flame extinction and droplet breakup.

Biographical Sketch

Prof. Saptarshi Basu received his PhD in Mechanical Engineering from University of Connecticut in 2007 with Prof. B. M Cetegen before joining University of Central Florida as an Assistant Professor. In 2010, he relocated to India and joined the prestigious Indian Institute of Science in Bangalore where he is currently the Pratt and Whitney Chair Professor in the Department of Mechanical Engineering.

Prof. Basu primarily works on multiphase systems, especially droplets at multiple length and timescales across multiple application domains ranging from surface patterning to combustion. Recently Prof. Basu have done extensive research on transmission of aerosols during COVID and on the efficacy of facemasks. His research marries fundamental aspects of classical fluid mechanics like vortex dynamics and swirling flows and the more interdisciplinary aspects of interfacial transport as in droplets to offer unprecedented insights into multiphase systems.

He is a fellow of Indian National Academy of Engineering, ASME, Institute of Physics, Royal Aeronautical Society and Royal Society of Chemistry. Prof. Basu is the recipient of DST Swarnajayanti Fellowship (equivalent of PECASE) in Engineering. Prof. Basu is a co-founder of a Biotech startup specializing in AI based Point of Care Diagnostics and a technical advisor to a deep tech startup involved in micro gas turbines. Prof. Basu serves as an editor/guest editor of several journals like Nature-scientific reports, Experiments in Fluids and European Physical Journal Special Topics. Prof. Basu’s research is extensively funded by Department of Defence, Indian Space Research Organization, Department of Science and Technology, Indo-German Science and Technology Center, Indo-US Clean Energy Center, NSF and industries like Siemens and Tata Motors. Prof. Basu has guided more than 20 PhD students in his career and published over 200 journal articles including many in Journal of Fluid Mechanics, Physics of Fluids, Combustion & Flame, Langmuir, Proc. Roy. Soc. etc.

Prof. Anna Tarakanova Receives a $3M RO1 Grant From the National Institutes of Health

Researchers from the School of Mechanical, Aerospace, and Manufacturing Engineering in UConn’s College of Engineering, pursue groundbreaking research to understand the impact of aging-related physicochemical modifications on the structure and function of elastin, a vital protein that imparts elasticity and recoil function to many connective tissues in the human body, including within elastic arteries. These modifications play a significant role in age-related diseases such as diabetes, motivating the importance of studying elastin’s behavior in aging arteries.

Led by Dr. Anna Tarakanova, the project has been awarded a $3 million R01 grant from the National Institutes of Health (NIH) through 2028 to further investigate elastin and its role in arterial biomechanics in health and aging.

Read more in the UConn Today article.

Mechanistic Interactions at Scale in Energy Storage

Abstract: Advances in electrical energy storage systems are critical for vehicle electrification, renewable energy integration into the electric grid, and electric aviation. Recent years have witnessed an urgent need to accelerate innovation toward realizing improved and safe utilization of high energy and power densities, for example, in lithium-ion and advanced battery chemistries. These are complex, dynamical systems that include coupled processes encompassing electronic, ionic, and solid-state diffusive transport, electrochemical reactions at electrode/electrolyte interfaces, mechanical stress generation, and thermal transport in porous electrodes. This presentation will highlight the importance of the underlying mechanistic interactions at scale in the design of novel paradigms in exemplar energy storage architectures.

Biographical Sketch: Partha P. Mukherjee is a Professor of Mechanical Engineering and a University Faculty Scholar at Purdue University. His prior appointments include Assistant Professor and Morris E. Foster Faculty Fellow of Mechanical Engineering at Texas A&M University (2012-2017), Staff Scientist at Oak Ridge National Laboratory (2009-2011), Director’s Research Fellow at Los Alamos National Laboratory (2008-2009), and Engineer at Fluent India (currently Ansys Inc., 1999-2003). He received his Ph.D. in Mechanical Engineering from Pennsylvania State University in 2007. His awards include Scialog Fellows’ recognition for advanced energy storage, University Faculty Scholar and Faculty Excellence for Early Career Research awards from Purdue University, The Minerals, Metals & Materials Society Young Leaders Award, and invited presentations at the U.S. National Academy of Engineering Frontiers of Engineering symposium and Gordon Research Conference – Batteries, to name a few. His research interests are focused on mesoscale physics and stochastics of transport, chemistry, and materials interactions, including an emphasis on the broad spectrum of energy storage and conversion.

On the Unsteady Interaction between Turbulence and Structures/Canopies

Abstract: The characterization and quantification of the coupling between flow and flexible structures and dominant oscillation modes remain open problems. Environmental science, energy, structural design, and locomotion applications require a comprehensive understanding of these phenomena. Canopy flows, encompassing extensive arrays of rigid or flexible structures, hold significant interest. Ubiquitous in natural environments and spanning multiple scales, they are instrumental in the transport of scalar and inertial particles. This presentation will provide insights from both theoretical perspectives and controlled laboratory experiments. I will discuss the role of key parameters modulating the unsteady dynamics of flows, individual structures, and canopies. These parameters comprise flow velocity, turbulence, structural stiffness, aspect ratio, tip effects, layout, and submergence within open channel flows. For this purpose, I will present data from particle image velocimetry (PIV), particle tracking velocimetry (PTV), and force balance analyses, highlighting turbulence, motion patterns, and unsteady loads on selected structures.

Biographical Sketch: Dr. Chamorro is an Associate professor in the Department of Mechanical Science and Engineering at the University of Illinois at Urbana-Champaign and is affiliated with the Departments of Aerospace Engineering, Civil and Environmental Engineering, and Geology. His research interests include turbulence, particle dynamics, boundary layer processes, aerodynamics, turbulence and structure interaction, wind energy, marine and hydrokinetic energies, and the development of advanced flow diagnostics. He has published 135 peer-reviewed articles in leading journals, has participated in over 140 presentations in technical symposia, and serves as scientific chair on Energy, Electrical Eng, Electronics and Mechanics (W&T7) at the Research Foundation Flanders (FWO) in Belgium. Chamorro is Associate Editor of the Journal of Renewable and Sustainable Energy, the Journal Frontiers in Energy Research, and the Journal of Energy Engineering. He leads the Renewable Energy and Turbulent Environment group, which uses a versatile experimental approach that combines state-of-the-art techniques, including 2D/3D particle image velocimetry, computer vision, and 3D particle tracking velocimetry.