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.

https://s.uconn.edu/meseminar3/12

Abstract:

This talk will discuss a novel dynamic subgrid-scale (SGS) modeling approached called Coherent-vorticity Preserving (CvP) Eddy-Viscosity Correction [1], which has been designed for very rapid evaluation of the SGS vortical activity, enabling local and instantaneous modulation of the turbulent eddy viscosity. The CvP-LES approach has been validated against large-scale direct-numerical simulation (DNS) employing a new block-spectral adaptive mesh refinement code named VAMPIRE [2], providing new insights into complex dynamics of high-Reynolds-number vortex dominated flows. The CvP-LES has performed exceptionally well in complex vortical flows such as double helical vortices [3] and trefoil knotted vortices [4], demonstrating a drastic reduction in computational time while being able to correctly predict the evolution of global quantities such as the total helicity, which rely on very small-scale non-equilibrium turbulent production events.

[1] J-B Chapelier, B Wasistho, and C Scalo. A Coherent-vorticity Preserving Eddy-viscosity Correctionfor Large-Eddy Simulation. Journal of Computational Physics, 359:164–182, 2018.

[2] Xinran Zhao and Carlo Scalo. A Compact-Finite-Difference-Based Numerical Framework for Adaptive-Grid-Refinement Simulations of Vortex-Dominated Flows. In AIAA Scitech 2020 Forum, 2020.

[3] J-B. Chapelier, B. Wasistho, and C. Scalo. Large-Eddy Simulation of Temporally Developing DoubleHelical Vortices. Journal of Fluid Mechanics, 863:79–113, 01 2019.

[4] Xinran Zhao, Zongxin Yu, Jean-Baptiste Chapelier, and Carlo Scalo. Direct numerical and large-eddysimulation of trefoil knotted vortices. Journal of Fluid Mechanics, 910:A31, 2021.

Biographical Sketch:

Dr. Carlo Scalo is an Associate Professor in the School of Mechanical, and Aeronautical and Astronautical Engineering (by courtesy) at Purdue University. His research interests focus on computational aeroacoustics, vortex dynamics, low- and high-speed turbulent boundary layers, and hypersonics. Dr. Scalo has received three distinct Young Investigator Program (YIP) Awards from the Department of Defense in: hypersonic boundary layer transition (Air Force), hypersonic boundary layer turbulence (Navy) and vortex dynamics (Army). Dr. Scalo is also the founder of HySonic Technologies – a Purdue start-up that received SBIR funding from the US Navy for the design of a new generation of aeroshells for hypersonic vehicles.

https://s.uconn.edu/meseminar3/5

Abstract:

Energy demand and the need to reduce emissions have pushed combustion research towards the development of more efficient, environmentally-friendly engines. In lean-burn systems both high efficiency and low emissions can be achieved in principle by controlling the flame temperature; however the short resident times in practical systems and the unsteady coupling between turbulent mixing, thermochemistry and acoustics makes the realisation of such systems very challenging. Large eddy simulations are attractive to predict this unsteadiness but require modelling of the turbulence-flame interaction at the small scales, and this modelling becomes particularly challenging at high-pressure, realistic conditions due to the limited amount of available validation data and the need to keep the computational cost relatively low. In this seminar recent development of LES modelling in the context of presumed PDF approaches will be discussed and their predictive abilities and limitations analysed for conditions relevant for gas turbines. An application to a bi-stable combustor will be then presented at the end of the talk. 

Biographical Sketch:

Dr Ivan Langella is an Assistant Professor in Sustainable Aircraft Propulsion at the faculty of Aerospace Engineering at TU Delft. He obtained his Master degree in Naples, Italy, in 2011, and his PhD in Mechanical Engineering from University of Cambridge, UK, in 2016. Since then he has worked as a postdoctoral associate at the University of Cambridge until June 2018 on advanced combustion systems of aeronautical interests in collaboration with Rolls-Royce and DLR Germany. From June 2018 to April 2020 he has worked as Lecturer in Thermofluids science and Engineering at Loughborough University, UK, before joining the research group in Delft.

https://s.uconn.edu/meseminar2/26/21

Abstract:

This talk will present a machine learning framework that builds interpretable macroscopic surrogate elasto-plasticity models inferred from sub-scale direction numerical simulations (DNS) or experiments with limited data. To circumvent the lack of interpretability of the classical black-box neural network, we introduce a higher-order supervised machine learning technique that generates components of elasto-plastic models such as elasticity functional, yield function, hardening mechanisms, and plastic flow. The geometrical interpretation in the principal stress space allows us to use convexity and smoothness to ensure thermodynamic consistency. The speed function from the Hamilton-Jacobi equation is deduced from the DNS data to formulate hardening and non-associative plastic flow rules governed by the evolution of the low-dimensional descriptors. By incorporating a non-cooperative game that determines the necessary data to calibrate material models, the machine learning generated model is continuously tested, calibrated, and improved as new data guided by the adversarial agents are generated. A graph convolutional neural network is used to deduce low-dimensional descriptors that encodes the evolutional of particle topology under path-dependent deformation and are used to replace internal variables. The resultant constitutive laws can be used in a finite element solver or incorporated as a loss function for the physical-informed neural network run physical simulations.

Biographical Sketch:

Dr. Sun is an associate professor at Columbia University since 2014. He obtained his PhD from Northwestern in 2011 and worked as a senior member of technical staff at Sandia National Laboratories from 2011-2013. His research focuses on theoretical, computational, and data-driven mechanics for multiphase materials with complex microstructures. He is the recipient of several awards including the IACM John Argyris Award, ICE Zienkiewicz medal Prize, the ASCE EMI Da Vinci Award, the NSF CAREER award, Young Investigator Awards from Army Research Office and Air Force Office of Scientific Research.

 

https://s.uconn.edu/meseminar2/19

Abstract: In this talk, we will attempt to address the following two questions concerning Covid-19 type infectious respiratory disease spread: 

1. Can we identify the relative importance of the different dominant transmission routes of the SARS-CoV-2 virus? Initially Covid-19 was assumed to spread by large droplets whereas of late the airborne route has been recognized to be important. Knowledge of the most dominant transmission route is necessary to design corresponding disease mitigation strategies.

2. Can we construct a disease spread model from the flow physics of transmission? Present, widely used epidemiological models do not account for the flow physics that underpin disease transmission.

To these ends, the probability of infection caused by inhaling virus-laden droplets and their desiccated nuclei are individually calculated. At typical, air-conditioned yet quiescent indoor space, for average viral loading, cough droplets of initial diameter between 10-50 micron are found to have the highest infection probability while it is the airborne droplet nuclei, due to their persistence (especially in poorly ventilated spaces), contribute most to the disease spread. Combined with molecular collision theory adapted to calculate the frequency of contact between the susceptible population and the aerosol cloud, infection rate constants are derived ab initio, leading to a Susceptible-Exposed-Infectious-Recovered (SEIRD) model applicable for any respiratory event – vector combination. Viral load, minimum infectious dose, and dilution of the respiratory jet/puff by the entraining air are shown to mechanistically determine specific physical modes of transmission and variation in the basic reproduction number, from first-principle calculations.

Biographical Sketch: Prof. Swetaprovo Chaudhuri works in turbulent reacting flows and propulsion and is known for his contributions on turbulent flame stabilization, propagation, and structure using experiments, theory, and computations. After his BE from Jadavpur University (2006), he earned his PhD from the Department of Mechanical Engineering, University of Connecticut in 2010, working under Prof. B. M. Cetegen. He worked at Princeton University as a research staff with Prof. C K. Law and then at the Indian Institute of Science, as an Assistant/Associate Professor. Subsequently, he joined the University of Toronto Institute for Aerospace Studies as a tenured Associate Professor. Prof. Chaudhuri has authored/co-authored over hundred articles in top journals, conferences, and books, and has been honored by ASME, UConn, INSA, IAS, UTIAS. He is an elected Associate Fellow of AIAA (class of 2021) and a member of its Propellants and Combustion technical committee.