https://s.uconn.edu/meseminar

Abstract: Modern machine learning techniques, such as deep neural networks, are transforming many disciplines ranging from transportation to healthcare, by uncovering patterns in big data and making accurate predictions. They have also shown promising results for discovering design ideas, which is crucial for creating new products and enabling innovation. These automated computational design methods can support human experts, who typically create designs by a time-consuming process of iteratively exploring ideas using experience and heuristics. However, there are still challenges remaining in synthesizing new designs while navigating the exploration-exploitation trade-off. In this talk, we will discuss a few main challenges faced by data-driven generative models, which are unique to design problems, and show how novel architectures of performance-augmented Generative Adversarial Networks (named PaDGAN, MO-PaDGAN, and PcDGAN) address these challenges. By applying these algorithms to the airfoil synthesis problem, we will show how these methods outperform state-of-art design parametrization methods with large improvements in design performance, diversity, and novelty for single and multiple objectives. The talk will conclude by highlighting some broader applications and open challenges in data-driven design.

Biographical Sketch: Dr. Faez Ahmed is an Assistant Professor in Mechanical Engineering at the Massachusetts Institute of Technology (MIT), where he leads the Design Computation and Digital Engineering (DeCoDE) lab. His research focuses on developing new machine learning and optimization methods to study complex engineering design problems. His recent work includes proposing automated design synthesis methods to generate novel designs, creating the first provably-optimal algorithm for the diverse matching problem, and building computationally efficient ways for combining physics with human expert knowledge to design new products. Before joining MIT, Faez was a Postdoctoral Fellow at Northwestern University and completed his Ph.D. in Mechanical Engineering at the University of Maryland. He was employed in the railway and mining industry in Australia, where he pioneered data-driven predictive maintenance and renewal planning efforts.

Webex Link:  https://s.uconn.edu/meseminar

Abstract: Proton exchange membrane fuel cells (PEMFCs) provide clean and efficient conversion of chemical energy into electrical energy, and fuel cell electric vehicles offer attractive range, weight, and refueling times when compared to similar technologies. However, challenges in infrastructure, performance, durability, and cost hinder wide-spread adoption. PEMFC cost, performance, and durability are closely tied to the Pt catalyst in PEMFC electrodes. Efforts to lower the Pt loading or replace the catalyst altogether show poor performance or low durability. This difficulty highlights the challenges related to optimizing processes in the PEMFC catalyst layer (CL), a heterogeneous region made up of carbon, catalyst, gas, and ion-conducting polymer electrolyte phases where key limiting phenomena occur in PEMFCs.
In this presentation we cover the challenges and progress in developing structure-property-function relationships for the Nafion polymer phase in the PEMFC cathode catalyst layer. Work is presented in terms of developing a modeling tool for predicting and optimizing PEMFC performance based on CL design parameters (e.g., microstructure, nanostructure, and catalyst loading). To understand the influence of CL Nafion on PEMFC performance, Nafion properties as a function of film thickness, thermochemical conditions, and polymer structure at solid support interphases must be better understood. We use neutron reflectometry, combined with complementary thin-film techniques, to develop quantitative structure-property relationships for thin-film Nafion, for deploying in our model framework. Insights include an evaluation of modeling approaches for the PEMFC CL microstructure, quantifying Nafion water uptake and conductivity for varying thicknesses and solid substrates, and non-intuitive design principles for PEMFC CL performance. We conclude by highlighting remaining knowledge gaps and next steps for improved understanding and control in PEMFC catalyst layers.

Biographical Sketch: Steven C. DeCaluwe is an Associate Professor of Mechanical Engineering at the Colorado School of Mines in Golden, CO. He received his BS in mathematics and elementary education from Vanderbilt University (2000). After teaching elementary school for three years, he earned a PhD in mechanical engineering from the University of Maryland (2009) before serving as a postdoctoral fellow at the NIST Center for Neutron Research (2009–2012). He has been at the Colorado School of Mines Department of Mechanical Engineering, where he leads the CORES Research Group (cores-research.mines.edu). His research employs operando diagnostics and numerical simulation to bridge atomistic and continuum-scale understanding of electrochemical energy devices, with a focus on processes occurring at material interfaces and in reacting flows. Applications include lithium-ion batteries, beyond lithium-ion batteries (Li-O2 and Li-S), and proton exchange membrane fuel cells.

Webex Link:http://s.uconn.edu/meseminarf90

Abstract: The extracellular matrix (ECM) of an artery endows the tissue its load bearing and damage resistance capacities. This talk will focus on the complex interplay between the multiscale ECM structural inhomogeneity and mechanics of large elastic arteries. Our recent studies integrating multiphoton imaging and quantification, biomechanical characterization, and computational modelling showed that ECM structural inhomogeneity exists at multiple structural levels of the arterial wall. At the intralemellar level, varying fiber orientation distribution and undulation contributes to local ECM mechanical properties. At the interlamellar level, transmural variation in in-plane fiber orientation distribution determines the anisotropic mechanical behavior of the elastin network. Furthermore, the waviness gradient among the elastic lamellar layers plays an important role in maintaining tissue homeostasis. Finally, structural inhomogeneity in transmural interlamellar fibers and the development and propagation of aortic dissection will be discussed.

Biographical Sketch: Dr. Katherine Zhang is a Professor at Boston University’s Departments of Mechanical and Biomedical Engineering, and Division of Materials Science and Engineering, and the Associate Chair for the Graduate Program in the Department of Mechanical Engineering. She received her B.S. degree in Engineering Mechanics from Tsinghua University; and her M.S. and Ph.D. degrees in Mechanical Engineering from University of Colorado at Boulder, where she was also a postdoc for two years. In 2006, Dr. Zhang became an Assistant Professor at Boston University and established the Multi-Scale Tissue Biomechanics Laboratory. Her research focuses on vascular biomechanics and the multi-scale mechanics and mechanobiology of the extracellular matrix. Dr. Zhang was awarded the Clare Boothe Luce Assistant Professorship in 2006, the Young Faculty Award from DARPA in 2007, and the Faculty Early Career Development (CAREER) Award from the NSF in 2010. Dr. Zhang was elected a Fellow of the American Society of Mechanical Engineers (ASME) in 2018.

Webex Link: http://s.uconn.edu/meseminarf90

Abstract: Internet of Things (IoT) represents the convergence of
three major and irreversible technology trends, namely (i) embedded sensing/smart devices, (ii) pervasive connectivity, and (iii) real-time analytics and contextual intelligence. The ability to collect and share relevant data across a wide range of devices, coupled with the ability to make real- time decisions, results in an unprecedented opportunities for system modeling, monitoring, and prognosis. In this talk, several new data analytics techniques tailored for IoT-enabled smart and connected systems will be introduced, including modeling and prognosis of condition monitoring signals using B-spline based mixed effects model, degradation model considering environmental factors, and stochastic decision making. The advantageous features of the proposed methods are demonstrated through numerical studies and real world case studies.

Biographical Sketch: Shiyu Zhou is the Vilas Distinguished Achievement Professor in the Department of Industrial and Systems Engineering and the Director of IoT Systems Research Center at the University of Wisconsin-Madison. His research focuses on data-driven modeling, monitoring, diagnosis, and prognosis for engineering systems with particular emphasis on manufacturing and after-sales service systems. He has established methods for modeling, analysis, and control of Internet-of-Things (IoT) enabled smart and connected systems, variation modeling, analysis, and reduction for complex manufacturing processes, and process control methodologies for emerging nano-manufacturing processes. He has won a large number of highly competitive federal research grants. His research also attracted significant interests from industry and received significant direct funding support from various companies. He is a recipient of a CAREER Award from the National Science Foundation and the Best Application Paper Award from IIE Transactions. He is now the director of IoT Systems Research Center at UW-Madison and a fellow of IISE, ASME, and SME.

Webex Link: http://s.uconn.edu/meseminar

Abstract: Cell therapy is one of the most promising new treatment approaches over the last decades, demonstrating great potential in treating cancers, including leukemia and lymphoma. For example, the chimeric antigen receptor (CAR) T cell therapy has shown innovative therapeutic effects in clinical trials, leading to a recent approval by FDA as a new cancer treatment modality. However, production of such therapeutic cells is extremely expensive due to the complexity of biomanufacturing processes/systems and the intrinsic patient-to-patient variability. There is an urgent need for research and development for scalable, cost- effective biomanufacturing technologies. One of such technologies is real-time process sensing, monitoring and control. This seminar presents an ongoing research work which aims at developing a real-time process sensing and monitoring technique for an important process step in cell manufacturing: cell expansion. The additively manufactured, impendence-based biosensors developed in this project can measure cell density in a bioreactor during the cell expansion process. A data analytics method is developed and incorporated to enhance the effectiveness of the biosensors for monitoring personalized manufacturing of cells from different patients by eliminating conventional sensor calibration steps. The current results and planned future work will be discussed.

Biographical Sketch: Dr. Chuck Zhang is the Harold E. Smalley Professor at H. Milton Stewart School of Industrial & Systems Engineering in Georgia Institute of Technology. His current research interests include additive manufacturing, biomanufacturing, cybersecurity for manufacturing, and advanced composite and nanomaterials manufacturing. Dr. Zhang has managed or conducted numerous research projects sponsored by major federal agencies including National Science Foundation, National Institute of Standards and Technology, Department of Defense, and Department of Veterans Affairs, as well as industrial companies such as ATK, Cummins, Delta Air Lines, Lockheed Martin and Siemens. He is a fellow of IISE. Dr. Zhang has published over 200 refereed journal articles and 220 conference papers. He also holds 25 U.S. patents.

Webex Link: http://s.uconn.edu/meseminarf90

Abstract: Fluid-Structure Interaction (FSI) is a multiphysics phenomenon that occurs when moving or deformable structures interact with internal or surrounding fluid flows. The coupling between the dynamics of the fluid and mechanics of the structure often gives rise to unexpected behaviors vital to many science and engineering problems. In this presentation, I will discuss a new computational FSI framework developed based on isogeometric and immersogeometric analysis with application to the modeling and simulation of biomedical and aerospace problems. The fully-coupled FSI formulation is derived using the augmented Lagrangian approach to enforce kinematic and traction constraints and naturally accommodates nonmatching and non- boundary-fitted fluid-structure interfaces. This novel method can make direct use of the CAD boundary representation of a complex design structure and effectively deal with FSI problems involving large deformations of the fluid domain, including changes of topology. The key ingredients to achieving high simulation accuracy will be reviewed. The proposed FSI framework is applied to engineering and science applications at different scales, ranging from studying complex military aircraft tail buffeting due to different angles of attack to understanding prosthetic heart valve leaflet flutter under physiological conditions. The findings and challenges will be shown and discussed in detail.

Biographical Sketch: Ming-Chen Hsu is an Associate Professor in the Department of Mechanical Engineering at Iowa State University. He received his MS degree in Engineering Mechanics from UT Austin in 2008 and PhD degree in Structural Engineering from UC San Diego in 2012. From 2012 to 2013, he was a postdoctoral fellow at the Institute for Computational Engineering and Sciences at UT Austin before joining Iowa State University. He is the recipient of the 2019 USACM Gallagher Young Investigator Award and is listed as a Web of Science Highly Cited Researcher from 2016 to 2019. He has published over 70 peer- reviewed journal papers and serves on several national and international professional society committees on computational methods and applications. His research focuses on computational mechanics, engineering, and sciences with an emphasis on fluid-structure interaction problems.

Webex Link: http://s.uconn.edu/meseminarf90

Abstract: The high demand for engineering lightweight materials with an optimal strength-toughness balance is driving the research towards the design of innovative materials with great performance. Composites generally represent the best option for structural applications, offering a good stiffness-strength balance, combined with a low weight. However, the reduced toughness of composite materials often represents a limitation for their structural applications. Many researchers tried to overcome this limitation by implementing nature-inspired features into the composite design, leading to a new class of composites with improved toughness: the biomimetic composites. Natural hierarchical materials, indeed, represent a good source of inspiration for new material design. In particular, bone is a promising candidate, showing a great combination of stiffness and strength, a remarkable toughness, and a lightweight structure that provides support to a wide class of animal bodies. The mysterious reason behind seem to lie in hierarchy. This talk will show different case studies of biomimetic composites, inspired by different hierarchical levels of bone tissue and realized by different manufacturing techniques (e.g. 3D-printing additive, lamination). Each case study investigates the effect of a hierarchical sub-structure on the tissue-level properties and behavior, through a combined numerical-experimental approach, highlighting the role of the characteristic structural features on activating specific mechanisms. This research embraces the fundamental understanding of biological structural materials and the effective transferable technologies for the bio-inspired design and fabrication of novel material systems.

Biographical Sketch: Flavia Libonati received a Ph.D. in Mechanical Engineering from Polytechnic University of Milan in 2013, followed by a postdoctoral associate position in the same university. In 2014, she became Assistant Professor in Mechanical Engineering at Polytechnic University of Milan and then in 2019 she was appointed as Associate Professor at the University of Genoa. Since 2014 she is also Research Affiliate at MIT, where she has been Visiting Research Scholar in 2016-2017, and recently appointed Research Affiliate at the Italian Institute of Technology. Her primary research interests are in the field of biological composites and biomimetic materials, with a special focus on the design and manufacturing of bio-inspired multiscale 3D-composite and smart materials for future engineering applications, through a multiscale numerical and experimental approach. She is the recipient of several awards and fellowships and is a member of renowned scientific society.

Webex Link: http://s.uconn.edu/meseminarf90

Abstract: Engineered materials that integrate advances in polymer chemistry, nanotechnology, and biological sciences have the potential to create powerful medical therapies. Dr. Khademhosseini’s group is interested in developing ‘personalized’ solutions that utilize micro- and nanoscale technologies to enable a range of therapies for organ failure, cardiovascular disease and cancer. In enabling this vision he works closely with clinicians (including interventional radiologists, cardiologists and surgeons). For example, he has developed numerous techniques in controlling the behavior of patient-derived cells to engineer artificial tissues and cell- based therapies. His group also aims to engineer tissue regenerative therapeutics using water-containing polymer networks called hydrogels that can regulate cell behavior. Specifically, he has developed photo-crosslinkable hybrid hydrogels that combine natural biomolecules with nanoparticles to regulate the chemical, biological, mechanical and electrical properties of gels. These functional scaffolds induce the differentiation of stem cells to desired cell types and direct the formation of vascularized heart or bone tissues. Since tissue function is highly dependent on architecture, he has also used microfabrication methods, such as microfluidics, photolithography, bioprinting, and molding, to regulate the architecture of these materials. He has employed these strategies to generate miniaturized tissues. To create tissue complexity, he has also developed directed assembly techniques to compile small tissue modules into larger constructs. It is anticipated that such approaches will lead to the development of next-generation regenerative therapeutics and biomedical devices.

Biographical Sketch: Ali Khademhosseini is currently the CEO and Founding Director at the Terasaki Institute for Biomedical Innovation. Previously, he was a Professor of Bioengineering, Chemical Engineering and Radiology at the University of California-Los Angeles (UCLA). He joined UCLA as the Levi Knight Chair in November 2017 from Harvard University where he was Professor at Harvard Medical School (HMS) and faculty at the Harvard-MIT’s Division of Health Sciences and Technology (HST), Brigham and Women’s Hospital (BWH) and as well as associate faculty at the Wyss Institute for Biologically Inspired Engineering. At Harvard University, he directed the Biomaterials Innovation Research Center (BIRC) a leading initiative in making engineered biomedical materials. Dr. Khademhosseini is an Associate Editor for ACS Nano. He served as the Research Highlights editor for Lab on a Chip. He is on the editorial boards of numerous journals including Small, RSC Advances, Advanced Healthcare Materials, Biomaterials Science, Journal of Tissue Engineering and Regenerative Medicine, Biomacromolecules, Reviews on Biomedical Engineering, Biomedical Materials, Journal of Biomaterials Science-Polymer Edition and Biofabrication. He received his Ph.D. in bioengineering from MIT (2005), and MASc (2001) and BASc (1999) degrees from University of Toronto both in chemical engineering.