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.

http://s.uconn.edu/meseminar4/23/21

Abstract: After billions of years of evolution, it is no surprise that biological materials are treated as an invaluable source of inspiration in the search for new materials. Additionally, developments in computation spurred the fourth paradigm of materials discovery and design using artificial intelligence. Our research aims to advance design and manufacturing processes to create the next generation of high-performance engineering and biological materials by harnessing techniques integrating artificial intelligence, Multiphysics modeling, and multiscale experimental characterization. This work combines computational methods and algorithms to investigate design principles and mechanisms embedded in materials with superior properties, including bioinspired materials. Additionally, we develop and implement deep learning algorithms to detect and resolve problems in current additive manufacturing technologies, allowing for automated quality assessment and the creation of functional and reliable structural materials. These advances will find applications in robotic devices, energy storage technologies, orthopedic implants, among many others. In the future, this algorithmically driven approach will enable materials-by-design of complex architectures, opening up new avenues of research on advanced materials with specific functions and desired properties.

Biographical Sketch: Grace X. Gu is an Assistant Professor of Mechanical Engineering at the University of California, Berkeley. She received her PhD and MS in Mechanical Engineering from the Massachusetts Institute of Technology and her BS in Mechanical Engineering from the University of Michigan, Ann Arbor. Her current research focuses on creating new materials with superior properties for mechanical, biological, and energy applications using multiphysics modeling, artificial intelligence, and high-throughput computing, as well as developing intelligent additive manufacturing technologies to realize complex material designs previously impossible. Gu is the recipient of several awards, including the 3M Non-Tenured Faculty Award, MIT Technology Review 35 Innovators Under 35, Johnson & Johnson Women in STEM2D Scholars Award, Royal Society of Chemistry Materials Horizons Outstanding Paper Prize, and SME Outstanding Young Manufacturing Engineer Award.

http://s.uconn.edu/meseminar4/9/21

Abstract: Soft machines are transforming the fields of robotics and biomedical devices in that they are capable of sustaining large deformation and interacting safely with human beings. Soft active materials can change their shapes or volumes in response to external stimuli, such as light, heat and electric fields, and are important building blocks of soft machines. The recent advance of 3D printing techniques allows manufacturing of soft materials into complex structures. Designing and fabricating soft structures with predictable actuation and programmable functionalities are the major efforts in the field. In this seminar, I will first talk about our recent progress in controlling and modeling spatiotemporal reconfiguration of soft active materials. By spatially patterning photo-responsive liquid crystal elastomers, we have shown morphing of flat sheets into designed three-dimensional geometry. To predict the spatiotemporal responses of photo-responsive hydrogels, we have developed a nonlinear field theory based on the nonequilibrium thermodynamics to capture the coupled reaction-diffusion kinetics. Further accounting the inertia effect, we have predicted and demonstrated self-excited photo-responsive hydrogel oscillators that can autonomously vibrate under constant light irradiation. Tuning the properties of soft materials through sophisticated chemical synthesis is often challenging. To overcome this limitation, I will demonstrate how we are able to vary the responses of soft materials by designing and fabricating them into mechanical metamaterials, which are materials with microarchitectures. Our efforts in designing phase-transforming metamaterials and energy-absorbing metamaterials will be discussed.

Biographical Sketch: Dr. Lihua Jin is an assistant professor in the Department of Mechanical and Aerospace Engineering at the University of California, Los Angeles (UCLA). Before joining UCLA in 2016, she was a postdoctoral scholar at Stanford University. In 2014, she obtained her PhD degree in Engineering Sciences from Harvard University. Prior to that, she earned her Bachelor’s and Master’s degrees from Fudan University in 2006 and 2009. Jin’s group conducts research on mechanics of soft materials, stimuli-responsive materials, instability and fracture, and soft robotics. Lihua was the winner of Haythornthwaite Research Initiative Grant from American Society of Mechanical Engineers in 2016, Extreme Mechanics Letters Young Investigator Award in 2018, Hellman Fellowship in 2019, and UCLA Faculty Career Development Award in 2020.

http://s.uconn.edu/meseminar4/2/21

Abstract:

We are in the midst of experiencing both the Big Data Revolution and the emergence of the Second Quantum Revolution. The amount of data available is doubling yearly, and artificial intelligence (AI), in particular machine learning (ML) methods are playing an increasingly important role in analyzing this data and using it to deduce new models of processes. Moreover, quantum mechanical phenomena have evolved into many core technologies and are expected to be responsible for many of the key advances of the future. Quantum computing (QC), in particular, has the potential to revolutionize computational modeling and simulation. The importance of these fields to the global economy and security are well recognized, promoting an even more rapid growth of the related technologies in the upcoming decades. This growth is fueled by large investments by governments and leading industries. An arena in which both QC and ML are promoted to play a more significant role is high performance computing. Since the early 1980s, computational simulations have been known as the 3rd pillar of science, and are now being augmented by the 4th paradigm formed by the big data revolution.

This lecture is focused on recent work in which use is made of modern developments in QC and ML to tackle some of the most challenging problems in turbulent combustion. The computational approach is via a stochastic model termed the Filtered Density Function (FDF). This model, originally developed by this lecturer, provides one of the most systematic means of describing the unsteady evolution of reactive turbulence. It is demonstrated that, if devised intelligently, ML can aid in developments of high fidelity FDF closures, and QC provides a significant speed-up over classical FDF simulators.

Bio Sketch:

Dr. Peyman Givi is Distinguished Professor and James T. MacLeod Professor of Mechanical Engineering and Petroleum Engineering at the University of Pittsburgh. Previously he held the position of University at Buffalo Distinguished Professor of Aerospace Engineering at SUNY-Buffalo. He has also had frequent visiting appointments at the NASA Langley & Glenn centers, and received the NASA Public Service Medal. He has also worked at Flow Research Company as a Researcher in Applied Mechanics. Givi is among the first 15 engineering faculty nationwide who received the White House Presidential Faculty Fellowship from President George H.W. Bush. He also received the Young Investigator Award of the Office of Naval Research, and the Presidential Young Investigator Award of the National Science Foundation.

Givi is currently the Deputy Editor of AIAA Journal. He is also on the Editorial Boards of Combustion Theory and Modelling, Computers & Fluids, and Journal of Applied Fluid Mechanics. He is Fellow of AAAS, AIAA, APS and ASME, and was named ASME Engineer of the Year in Pittsburgh in 2007. He received Ph.D. from the Carnegie Mellon University (PA), and BE from the Youngstown State University (OH) where he is named a Distinguished Alumnus.

 

http://s.uconn.edu/meseminar3/26/21

Abstract: Microneedles enable minimally invasive access to the body interior. This access can be used to administer drug formulations to precise locations in the skin or the eye, and can be used to access interstitial fluid in the skin. Three applications of microneedle technology will be discussed.

Our first project is motivated by the need for improved drug delivery to the skin, especially for dermatological indications. Building off work with microneedle patches that employ micron-scale, solid needles to administer drugs and vaccines to the skin, we developed particles with microscopic needles that painlessly create micropores upon rubbing onto the skin. These STAR particles dramatically increased skin permeability, enabling, for example, improved treatment of melanoma with topical drug (5-fluorouracil) in the mouse.

Our second project is motivated by an interest in sampling tissue interstitial fluid (ISF) as a novel source of biomarkers. Because ISF is hard to collect, we developed a method to sample ISF from human skin through micropores created by microneedles. We identified valuable and sometimes unique biomarkers in ISF collected from human participants when compared to companion plasma samples based on mass spectrometry analysis, which can facilitate research and enable new diagnostic tests. Because ISF does not clot, biomarkers in ISF could be continuously monitored.

Our third project is motivated by the need for improved glaucoma treatments. We developed a method to inject a crosslinked hyaluronic acid hydrogel into the suprachoroidal space of the eye using a hollow microneedle. As a drug-free, non-surgical technique, we were able to reduce intraocular pressure in rabbits for four months after a single injection by a mechanism believed to involve increased flow of aqueous humor from the eye due to expansion of the suprachoroidal space.

These are examples of how microneedle technology can be used for a diversity of applications with the common theme of accessing a specific location in the body with sub-millimeter precision using a low-cost, simple-to-use technology.

 

Biographical Sketch: Mark Prausnitz is Regents’ Professor and J. Erskine Love, Jr. Chair of Chemical & Biomolecular Engineering at the Georgia Institute of Technology. He earned a BS degree from Stanford University and PhD degree from MIT, both in chemical engineering. Dr. Prausnitz and colleagues carry out research on biophysical methods of drug delivery using microneedles, lasers, ionic liquids and other microdevices. Their research focuses on transdermal, ocular and intracellular delivery of drugs and vaccines. Dr. Prausnitz teaches an introductory course on engineering calculations, as well as two advanced courses on pharmaceuticals. He has published almost 300 journal articles and has co-founded five start-up companies including Micron Biomedical and Clearside Biomedical.

http://s.uconn.edu/meseminar3/19/21

Abstract: The National Science Foundation (NSF) supports work in all fields of science and engineering, including biomedical engineering. That said, biomedical engineering researchers can face challenges in finding the right ‘home’ and scope for their work at NSF. This presentation will provide a broad overview of the mission of NSF and how it relates to the biomedical engineering community, including emerging initiatives and responses to the current disruption of the research enterprise. Descriptions of select programs at the National Science Foundation that fund work relevant to the biomedical engineering community will be covered. Best practices in proposal preparation and practical tips to optimize interaction with your program director will also be discussed. Bring your questions along!

 

Biographical Sketch: Laurel Kuxhaus, PhD, is the program director of Biomechanics & Mechanobiology within the Division of Civil, Mechanical and Manufacturing Innovation at the National Science Foundation. Concurrently, she is an Associate Professor of Mechanical & Aeronautical Engineering at Clarkson University, where she directs the Orthopaedic Biomechanics Laboratory. Her laboratory work spans the field of orthopaedic biomechanics including injury biomechanics of both hard and soft tissues and design of both orthopaedic implants and assistive technology devices. She holds B.S. (Engineering Mechanics) and B.A. (Music) degrees from Michigan State University, an M.S. (Mechanical Engineering) from Cornell University, and a Ph.D. (Bioengineering) from the University of Pittsburgh. In 2018, she was elected to Fellow status of the American Society of Mechanical Engineers (ASME) and has previously served as a member of the Executive Committee of the Bioengineering Division of ASME. More recently (2018-19), she spent a year on Capitol Hill working in science and technology policy as an ASME Congressional Fellow.