http://s.uconn.edu/meseminar11/19/21

Password: 1234

Abstract: I will provide an account of the interesting dynamics exhibited by droplets at multiple length and time scales in completely different domains, namely gas turbines and COVID-19. In the first part of my talk, I will provide some insights into the dynamics of spray-swirl interaction with particular focus on droplet transport, breakup and dispersion. I will show how the fundamental insights gained through such interactions can be used to design a new class of atomizers in gas turbines. In the second part of my talk, I will discuss how the spread of COVID can happen through respiratory droplets and fomites. In this part, I will provide a detailed exposition of how respiratory droplet dynamics can be combined with a pandemic model to provide a first principle insights into infection spread rates. We will show through experiments using surrogate fluids how such models can be experimentally verified rigorously. Subsequently, I will show how fomites form and how the virions are embedded in the crystal network using both contact free as well as sessile droplets.

 

Biographical Sketch: Prof. Saptarshi Basu is currently DRDO Chair Professor in the department of mechanical engineering at IISc. Prof. Basu primarily works on multiphase systems, especially droplets at multiple length and timescales across multiple application domains. 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 in engineering.   

http://s.uconn.edu/meseminar11/5/21

Abstract: While there is significant interest in using tissue engineering techniques to create tendon and ligament replacements, no engineered biomaterial has been successful in replicating their physiological function. This is because there is a fundamental lack of understanding of how to produce a robust tensile load-bearing biological tissue. Previous work suggests that tendon maturation is driven by rapid increases in collagen fibril length and molecular crosslinking mediated by mechanical stimulation due to muscle activity. However, the effect of mechanical stimulation on the tensile mechanics of developing tendons and the functional significance of the structural changes that occur during development are still unclear. To address this knowledge gap, we investigated the multiscale structure-function relationships of embryonic tendons during normal development and following the loss of mechanical stimulation via immobilization. Using multiscale mechanical testing, we found that the strain transmitted to the collagen fibrils in tendons at embryonic days 16, 18, and 20 is less than the strain applied to the tissue, suggesting the collagen fibrils remain discontinuous throughout embryonic development. However, the ratio of the fibril strains to the tissue strains increased with developmental age; this indicates that more strain is being transmitted to the fibrils and that there is less interfibrillar sliding, which is consistent with an increase in the average fibril length and an increase in the macroscale mechanics during this period of development. Additionally, there was a decrease in the macroscale tensile modulus and the fibril: tissue strain ratio with flaccid (but not rigid) immobilization, suggesting that complete loss of mechanical stimulation inhibits fibril elongation and strain transmission to the collagen fibrils, resulting in impaired functional maturation. Consistent with these mechanical assessments, we found that collagen fibril bundling was impaired with immobilization. Interestingly, while the enthalpy required to denature the tendons increased with increasing age, there was no effect with immobilization. This suggests that although intermolecular crosslinks in embryonic tendons increase with development, the loss of tensile mechanical properties with immobilization is potentially not due to a reduction in functional crosslinking. Together, these data suggest that the key structural change induced by mechanical stimulation during tendon development is an increase in the strain transmitted to the collagen fibrils, which is consistent with fibril elongation. These data provide fundamental insight into the mechanisms driving tendon development and will guide the design of improved techniques for engineering tendon/ligament replacements.

 

Biographical Sketch: Dr. Szczesny is an Assistant Professor at the Pennsylvania State University with a joint appointment in the Departments of Biomedical Engineering and Orthopaedics & Rehabilitation. He completed his postdoctoral training in 2017 as an NIH NRSA F32 Fellow and obtained a PhD in bioengineering in 2015 at the University of Pennsylvania. Prior to his doctorate, Dr. Szczesny developed medical implants as a design engineer for Aesculap Implant Systems and as a research assistant at the Helmholtz Institute for Biomedical Technology in Aachen, Germany. He obtained a MS in mechanical engineering at the Massachusetts Institute of Technology in 2005 and a BS in mechanical engineering at the University of Pennsylvania in 2003. In recognition of his contribution to the field of tendon biomechanics and mechanobiology, Dr. Szczesny was an ORS New Investigator Recognition Award (NIRA) finalist, won 1st place in the SB3C PhD competition (twice), and received the 2015 Acta Student Award. Dr. Szczesny’s current research examines how cells in tendon sense the mechanics of their local microenvironment (e.g., strains, stiffness) and how their response drives changes in tissue mechanical properties during tendon degeneration, repair, and development. The ultimate goals of this work are to identify the causes of tendon pathology, discover novel therapeutic options, and direct the design of biomaterials that can recapitulate the behavior of native tissue.

http://s.uconn.edu/meseminar10/29/21

 Abstract: Redox flow batteries (RFBs) are an emerging energy storage technology that offers unique advantages for long-duration, grid-scale energy storage due to their ability to decouple energy and power ratings and the associated unprecedented scalability. Despite their promise, the relatively higher capital cost of RFBs limits their commercial viability and widespread adoption. One possible approach to reduce the capital cost is to improve the performance (i.e., increased energy and/or power density) of state-of-the-art systems for less material use, which consequently reduces cell costs. In this talk, an overview of the presenter’s most recent research toward high-performance RFBs will be given. In particular, the following three research projects will be summarized:

1. Natural selection as a toolkit to overcome practical limitations in non-aqueous redox flow batteries (NRFB) – The performance characteristics of the mushroom inspired NRFB electrolyte using a suite of electrochemical and operando spectro-electrochemical data will be reported.
2.  Overcoming the active material solubility limitation in RFBs via redox-targeting reactions – Recent efforts to reveal the fundamental principles of indirect redox-targeting reactions necessary to enable the rational design of high-energy density RFBs will be presented.
3. Manufacturing of fabric-electrodes using machine learning based screening platforms – Critical factors underpinning electrode performance are elucidated. The structure-property-performance linkages of commercially available electrodes will be discussed.

 

Biographical Sketch: Dr. Ertan Agar is an Assistant Professor in the Department of Mechanical Engineering and the director of Electrochemical Energy Systems and Transport Laboratory (E2STL) at the University of Massachusetts Lowell. He earned his Ph.D. degree in Mechanical Engineering from Drexel University. His Ph.D. dissertation work was a combined experimental and modeling effort, which was aimed at understanding the species transport mechanisms governing capacity fade in vanadium redox flow batteries. Following his doctoral studies, Dr. Agar worked as a post-doctoral researcher in the Chemical Engineering Department at Case Western Reserve University. In this role, he worked on performance diagnostics of flowable slurry electrodes. His research interest includes design and diagnostics of flow-assisted electrochemical systems for energy and water applications (e.g., redox flow batteries, photoelectrochemical storage and water treatment cells), mass/charge transport phenomena, and electrochemical reaction kinetics. Dr. Agar is an active member of the Electrochemical Society and International Society of Electrochemistry. He also serves as the Faculty Lead for the UML I-Corps Site Program and the Regional Northeast I-Corps Hub.

https://s.uconn.edu/meseminar10/15/21

Abstract: Since Additive Manufacturing (AM) processes can fabricate complex part shapes and material compositions, it released significant amount of freedom for designers to design innovative products. In general, parts that are good candidates for AM tend to have complex geometries, low production volumes, special combinations of properties or characteristics. Most of existing design methods and approaches are well established for conventional manufacturing processes which tend to limit the complexity and potential multi-functionalities of products considerably. Given the unique characteristics of AM, Prof. Zhao and her team have proposed a new definition for the term — Design for Additive Manufacturing (also known as DfAM or DFAM) — as “a general type of design methods or tools whereby functional performance and/or other key product life-cycle considerations such as manufacturability, reliability, and cost can be optimized subjected to the capabilities of additive manufacturing technologies”. Most research in DFAM field only focuses on specific topics without considering AM process specific characteristics. AM technology connects design, material properties, process settings, end-product quality, and potential post-process operations intimately. When DFAM is applied, AM process-specific capabilities and constraints must be considered at early design stage. Thus, rooted from the proposed definition, this talk will report Prof. Zhao and her team’s recent work on developing novel design strategies and geometric modeling techniques to support multi-functional design concept generation and multi-scale highly complex CAD model realization with manufacturability analysis applied at early design stage.

Biographical Sketch: Dr. Yaoyao Fiona Zhao is an Associate Professor and William Dawson Scholar at the Department of Mechanical Engineering in McGill University, in Montreal, Canada. Since Dr. Zhao joined McGill University in 2012, she has established the Additive Design and Manufacturing Laboratory (ADML) which is one of the leading research laboratories in additive manufacturing field. Her research expertise lies in the general field of design and manufacturing including the exploration of new design methods, developing efficient numerical simulation method for additive manufacturing processes, manufacturing informatics, application of machine learning in design and manufacturing, sustainable product development and intelligent manufacturing. Her team is leading the research in Design for Additive Manufacturing with the development of new design methods to achieve multi-functionalities, less part count, better functional and sustainability performance. Her team is also leading the efforts on developing methods and guidelines for manufacturing industry to adopt machine learning and AI as an effective tool for global competition.

http://s.uconn.edu/meseminar10/8/21

Abstract: The COVID-19 pandemic has accelerated the development and manufacturing of vaccines at an unprecedented speed. This has been enabled by the emergence of biotechnology platforms such as mRNA and Viral Vectors. In this seminar, I will outline the engineering aspects of such platforms and the modern biology behind their evolution.

Biographical Sketch: Dr. Vijay Srinivasan is a Senior Advisor in the Engineering Laboratory at the National Institute of Standards and Technology, Gaithersburg, Maryland. He joined NIST in 2009, after 26 years at IBM Research during which he was also an Adjunct Professor at the Columbia University, New York. Dr. Srinivasan has published widely and is a Fellow of ASME and AAAS.

http://s.uconn.edu/meseminar10/1/21

Abstract: The development of aerospace products suffers from chronic cost and schedule overruns and derivative designs, while innovative products are needed on time and on budget.  We’ll examine the primary failure modes of conventional system-architecting practice that have led to these symptoms and how to avoid them.  We’ll consider how to answer the 5 key questions with which product-development teams struggle – how to determine the best performance achievable given a set of technologies, how to find a small diverse set of high-value architectures, how to identify the technology options that are critical for early investment before the final architecture is selected, how to set technology-development targets, and how to know what properties a new technology would need to be worth its development.  People are actually poorly suited to certain architecting tasks; we’ll describe how people’s skills tell us to which architecting tasks they’re really well suited to contribute.  Finally, we’ll describe a methodical approach to system architecture that researchers at Raytheon Technologies Research Center have been developing and successfully applying for product architecting across RTX.

Biographical Sketch: Dr. Zeidner leads the development and application of RTX’s DISCOVER ecosystem of advanced methods and tools for the conceptual design of RTX products, from the component level of product engineering, all the way up to the campaign level of multi-product operations. Dr. Zeidner has 25 years of experience in the development of system architectural-design methods and collaborative processes and tools for engineering innovation and decision-making.  His areas of expertise include design-space exploration, system modeling, risk analysis and software architecture. While at RTX, he has developed the Concept Generation and Selection (CGS) process, which has been successfully applied to over 200 projects across RTX’s business units, enabling large, non-collocated teams to brainstorm productively. He holds 4 patents and has earned fifteen internal RTRC awards for technical achievement. Prior to joining RTRC, Dr. Zeidner taught and conducted research as an Assistant Professor of Manufacturing Engineering at Boston University on the topics of design-to-manufacture and advanced software-development methods.  Dr. Zeidner obtained his PhD in Civil Engineering in 1983 from Princeton University.

http://s.uconn.edu/meseminar9/24

Password: 1234

Abstract: Lithium-ion batteries have been used extensively in the past decade in a variety of applications from portable devices to airplanes and electric vehicles. Battery packages used in electric vehicles experience dynamic loadings, shocks, and large deformations during normal operation as well as in a crash scenario. It is of paramount importance to battery manufacturers and the automotive industry to better understand how the cells deform under such loadings and what conditions might damage a cell and lead to failure. This talk will focus on the experimental methods used to characterize material properties of lithium-ion batteries under large mechanical loading. Then deployments of these material models for simulating crash response of batteries will be discussed. The models that will be discussed are capable of predicting the profile of deformation and the onset of short circuit in batteries in the cases of mechanical abusive loads.

Biographical Sketch: Elham Sahraei is an Assistant Professor and Director of Electric Vehicle Safety Lab at Temple University. She was the co-director of the MIT Battery Modeling Consortium, a multi-sponsor industrial program supported by major automotive and battery manufacturers from 2011 till 2019. Her research is focused on computational modeling of lithium-ion batteries for electric vehicles. Dr. Sahraei earned her Ph.D. from the George Washington University in 2011, and completed two years of post-doctoral training at Massachusetts Institute of Technology in 2013, where she became a Research Scientist afterwards. She is currently a principal investigator on safety of lithium-ion batteries under combined mechanical-electrical loading for Office of Naval Research. She has also been an investigator on several Ford-MIT alliance projects, and she is the inventor of “Collision Safety Structure,” a structure for controlled buckling of driver seats that reduces perils of frontal crashes.

http://s.uconn.edu/meseminar9/17/21

Abstract: Developing polymers with desirable properties has historically relied on trial-and-error, which can take long time, and there is no guarantee of success. Machine learning has become an integral part of materials design, and it can potentially impact polymer development in a positive way. However, the lack of open-source data has impeded the development of machine learning-guided polymer development, i.e., polymer informatics. In this talk, I will discuss our effort in generating polymer structures using machine learning models trained on existing polymer database. I will describe the process we used to generate the PI1M database (PI1M refers to 1 million polymers for Polymer Informatics) . I will also talk about the challenges ahead of the continued research in this direction. In addition, I will introduce our work in generating polymer properties using molecular dynamics simulations and discuss the challenges to be addressed. Machine learning models for several polymer properties (e.g., thermal properties, gas separation performance) will be presented. I will also highlight the need of a greater community effort to advance the polymer informatics field.

 

Biographical Sketch: Dr. Tengfei Luo is a Professor in the Department of Aerospace and Mechanical Engineering (AME) with a concurrent appointment in the Department of Chemical and Biomolecular Engineering (CBE) at the University of Notre Dame (UND). Before joining UND, he was a postdoctoral associate at MIT (2009-2011) after obtaining his PhD from Michigan State University (2009). Dr. Luo’s research focuses on exploring the chemistry-conformation-property relationships of polymers using molecular simulations, machine learning and experiments. He is an ASME Fellow (2019), JSPS Invitational Fellow (2019), DuPont Young Professor Awardee (2016), DARPA Young Faculty Awardee (2015), and Air Force Summer Faculty Fellow (2015). 

http://s.uconn.edu/meseminar9/10/21

Abstract: It is commonly believed that understanding, building, and controlling complex engineering systems require data-driven methods beyond first principles. Often these methods are boasted as “AI”. Yet data-driven methods are known to lack risk certification, i.e., we don’t have principled knowledge about when they will fail or how badly if they do. This challenge has created a recent surge of efforts in “baking” first principles into data-driven methods, leading to new learning theories, algorithms, and applications. In this talk, I will go through three studies across the spectrum where first principles play a significant role in mitigating the risks induced by data-driven models. The first study investigates an optimal solution generator governed by the Karush-Kuhn-Tucker conditions, with applications to accelerating topology optimization. The second discusses the approximation of open-loop solutions to the Hamilton-Jacobi-Isaacs equations for incomplete-information differential games, with applications to safer human-robot interactions. The last investigates certifiable attribution of generative models, with applications to DeepFake regulation.

 

Biographical Sketch: Dr. Yi Ren is an Assistant Professor with the Department of Aerospace and Mechanical Engineering at Arizona State University. He got his BEng in Automotive Engineering from Tsinghua University in 2007 and his PhD in Mechanical Engineering in 2012 from the University of Michigan, Ann Arbor, where he also worked as a Postdoc before moving to ASU in 2015. His research focuses on developing robust machine learning methods for risk-sensitive engineering systems, with applications to structure/materials design and autonomous driving. He has won multiple NSF grants from system engineering, materials science, robotics, and cybersecurity programs, the Amazon Machine Learning Award (2019), and the Best Paper Award at the ASME International Design and Engineering Technical Conferences (2015). He leads the planning of an ASU NSF AI institute for 4D materials design.