2022 Seminar Archive

Cellular NeuroMechanics – Concussions, Traumatic Brain Injury and the mysterious Havana Syndrome

Christian Franck

Grainger Institute for Engineering Professor
Department of Mechanical Engineering
University of Wisconsin-Madison
Madison, Wisconsin

Current prediction, prevention and diagnosis strategies for mild traumatic brain injuries, including concussions, are still largely in their infancy due to a lack of detailed understanding and resolution of how physical forces give rise to tissue injury at a cellular level. In this talk I will present some recent work on our current understanding of the origin of concussions and traumatic brain injuries and how cells in the brain interpret and react to the physical forces of trauma. Specifically, I will show that the path to a better understanding of traumatic injuries involves addressing a variety of finite deformation, rate-dependent soft matter and cell mechanics problems along the way. Finally, I will provide an update on how our current understanding of the cellular neuromechanics cannot only help shed light on improving our prediction of TBI but also enable us to dissect the physical origin of emerging injuries such as the Havana Syndrome.

Christian Franck is a mechanical engineer specializing in cellular biomechanics and new experimental mechanics techniques at the micro and nanoscale. He received his B.S. in aerospace engineering from the University of Virginia in 2003, and his M.S. and Ph.D. from the California Institute of Technology in 2004 and 2008. Dr. Franck held a post-doctoral position at Harvard investigating brain and neural trauma. He was an assistant and associate professor in mechanics at Brown University from 2009 - 2018, and is now the Grainger Institute for Engineering Professor in Mechanical Engineering at the University of Wisconsin-Madison.

His lab at the University of Wisconsin-Madison has developed unique three-dimensional full-field imaging capabilities based on multiphoton microscopy and digital volume correlation. Current application areas of these three-dimensional microscopy techniques include understanding the 3D deformation behavior of neurons in the brain during traumatic brain injuries, and the role of non-linear material deformations in soft matter.

He is the acting director of the Center for Traumatic Brain Injury at the University of Wisconsin-Madison and the ONR-funded Physics-based Neutralization of Threats to Human Tissues and Organs (PANTHER) program, which consists of over 24 PIs nationwide. Key objectives of the Panther program are in better detection, prediction, and prevention of traumatic brain injuries by providing accelerated translation from basic science discovery to civilian and warfighter protection solutions.

Wednesday, January 12, 2022

3:30 PM

Seminar will be by Zoom only.

The Zoom webinar is at
https://usc.zoom.us/j/93987337017?pwd=MWd2dXBSL1FaR1RPaHNscjJ1NW80UT09.

Elucidating the Thermodynamic Origins of Reaction Heterogeneity in Lithium-Ion Batteries

Ming Tang

Associate Professor
Department of Materials Science and NanoEngineering
Rice University
Houston, TX

During battery cycling, pronounced reaction non-uniformity frequently develops at multiple length scales within electrodes, which adversely impacts battery performance and life by inducing capacity under-utilization, stress concentration and over-(dis)charging. While heterogeneous reactions are typically attributed to mass transport limitations, thermodynamic factors also play an important role and need to be clarified for developing effective mitigation strategies. At the particle level, we reveal how stress could destabilize the lithium (de)lithiation front in single crystalline and polycrystalline intercalation compounds. Stress also provides a fundamental thermodynamic driving force for dendrite growth on lithium metal anodes, which is shown to be effectively suppressed by stress relief. At the cell level, we discover that the reaction distribution within the porous electrode is strongly influenced by how the equilibrium potential of the active material varies with the state of charge. Two types of reaction behavior are identified for common electrode materials, which have significant implications for their applications in thick electrodes. Based on this finding, an analytical model is formulated to provide highly efficient battery performance predictions and optimization in place of traditional battery cell simulations.

Ming Tang is an Associate Professor in the Department of Materials Science and NanoEngineering at Rice University. After receiving a Ph.D. degree in Materials Science and Engineering from MIT, He worked at Lawrence Livermore National Laboratory as a Lawrence Postdoctoral Fellow and then a staff scientist. In 2013 he joined Shell Oil as a materials and corrosion engineer, and became an assistant professor at Rice University in 2015. His group is currently interested in applying combined modeling and experimental methods to understand mesoscale phenomena in energy storage systems and use the acquired knowledge to guide microstructure design. He is a recipient of the DOE Early Career Award.

Wednesday, January 19, 2022

3:30 PM

Seminar will be by Zoom only.

The Zoom webinar is at https://usc.zoom.us/j/93987337017?pwd=MWd2dXBSL1FaR1RPaHNscjJ1NW80UT09.

host: Renuka Balakrishna

Cool Fuel: Engineering Liquid Hydrogen for the Future of Zero-Carbon Transportation

Jacob Leachman

Associate Professor
School of Mechanical and Materials Engineering
Washington State University
Pullman, WA

The new HydrogenShot initiative launched by the US Department of Energy has the ambitious goal of reducing hydrogen fuel production costs to $1 for 1 kg in 1 decade. Behind the scenes of this goal is an incredible logistics challenge to store and distribute the massive amounts of hydrogen needed. Currently over 90% of small merchant hydrogen is distributed via cryogenic liquid tanker truck. However, modern hydrogen liquefiers have specific energy consumptions only 30% of what is theoretically achievable for ~30 tonne/day systems approaching $100M in cost. Clearly, hydrogen liquefaction cycles must fundamentally change to massively scale with clean energy resources. Once liquefied, the next challenge is minimizing parasitic heat transfer that results in boil-off losses typically between 7-40%. New paradigms for liquid hydrogen storage are needed to minimize these losses. Although many challenges remain to be solved, the purpose of this talk is to emphasize the new tools and opportunities making this cool fuel an exciting research area for several decades to come.

Jacob Leachman is an Associate Professor in the School of Mechanical and Materials Engineering at Washington State University (WSU). He initiated the Hydrogen Properties for Energy Research (HYPER) laboratory at WSU in 2010 to advance cryogenic and/or hydrogen systems. To this day the HYPER laboratory remains the only US academic laboratory focusing on cryogenic hydrogen. He earned a B.S. degree in Mechanical Engineering in 2005 and a M.S. degree in 2007 from the University of Idaho. His master’s thesis has been adopted as the foundation for hydrogen fueling standards and custody exchange, in addition to winning the Western Association of Graduate Schools Distinguished Thesis Award for 2008. He completed his Ph.D. in the Cryogenic Engineering Laboratory at the University of Wisconsin-Madison in 2010 under the advice of John Pfotenhauer and Greg Nellis. He is the lead author of the reference text “Thermodynamic Properties of Cryogenic Fluids: 2nd Edition” and “Cool Fuel: The Science and Engineering of Liquid Hydrogen” which is in development. In 2018 he received the Roger W. Boom Award from the Cryogenic Society of America.

Wednesday, January 26, 2022

3:30 PM

Zumberge Hall of Science, Room 252 (ZHS 252)

The Zoom webinar is at https://usc.zoom.us/j/93987337017?pwd=MWd2dXBSL1FaR1RPaHNscjJ1NW80UT09.

host: Bradley

Passive and Active Control of Turbulent Jets and Flames — A CFD Research

Artur Tyliszczak

Professor
Faculty of Mechanical Engineering and Computer Science
Częstochowa University of Technology
Częstochowa, Slaskie, Poland

Interest in flow control techniques is driven by a possible improvement of performance, safety and efficiency of various technical devices. Existing strategies of steering and controlling fluid flows can be divided into two approaches: passive and active. The former is based on shaping the flow domains and is usually optimized for specific flow conditions. The latter requires an external energy input (an excitation, forcing), which can be varying in response to the instantaneous flow behavior. The active methods are thus more costly but also much more flexible. Under a variety of different flow regimes, they result in a better overall response than the passive methods. In this talk, I will focus on the CFD study of passive and active control applications for jets and flames. In the latter case, proper flow control is especially important as the efficiency of combustion processes is directly related to fuel-oxidizer mixing — a process, which we would like to have under full control. I will discuss to what extent the flow field can be modified and controlled by the selection of shapes of jet nozzles or tuning of excitation parameters.

Artur Tyliszczak is a Professor in the Faculty of Mechanical Engineering and Computer Science at Częstochowa University of Technology (CUT) in Poland. He leads the CFD Research Group. He earned an M.S. degree in Mechanical Engineering in 1997 from the CUT and a PhD degree in 2002 from the CUT and von Karman Institute for Fluid Dynamics (Belgium). He worked at Cambridge University (UK) as a Marie-Curie Experienced Researcher (2010-2011) and a visiting professor (2016). His group works on the development of high-order numerical methods for CFD and their applications for open and near-wall non-reacting and reacting flows. Currently, his main research concentrates on passive and active flow control in jet type flows and flows in porous and granular layers. Artur Tyliszczak is a recipient of prestigious individual awards from the Polish scientific community, the Ministry of Science and Education, the Polish Academy of Science, the Polish Association of Theoretical and Applied Mechanics. Recently he received a Senior Award from the Fulbright Commission for his stay at USC.

Wednesday, February 2, 2022

3:30 PM

Zumberge Hall of Science, Room 252 (ZHS 252)

The Zoom webinar is at https://usc.zoom.us/j/93987337017?pwd=MWd2dXBSL1FaR1RPaHNscjJ1NW80UT09.

host: Dmaradzki

Integrated Sensing and Actuation for Robust Flight Systems

Kristi Morgansen

Professor
William E. Boeing Department of Aeronautics and Astronautics
University of Washington
Seattle, WA

A fundamental element of effective operation of autonomous systems is the need for appropriate sensing and processing of measurements to enable desired system actions. Model-based methods provide a clear framework for careful proof of system capabilities but suffer from mathematical complexity and lack of scaling as probabilistic structure is incorporated. Conversely, learning methods provide viable results in probabilistic and stochastic structures, but they are not generally amenable to rigorous proof of performance. A key point about learning systems is that the results are based on use of a set of training data, and those results effectively lie in the convex hull of the training data. This presentation will focus on use of model-based nonlinear empirical observability criteria to assess and improving and bounding performance of learning pose (position and orientation) of rigid bodies from computer vision. A particular question to be addressed is what sensing data should be captured to best improve the existing training data. The particular tools to be leveraged here focus on the use of empirical observability gramian techniques being developed for nonlinear systems where sensing and actuation are coupled in such a way that the separation principle of linear methods does not hold. These ideas will be discussed relative to both engineering applications in the form of motion planning for range and bearing only navigation in autonomous vehicles, vortex position and strength estimation from pressure measurements on airfoils, and effective strain sensor placement on insect wings for inertial measurements.

Kristi Morgansen received a BS and a MS in Mechanical Engineering from Boston University, respectively in 1993 and 1994, an S.M. in Applied Mathematics in 1996 from Harvard University and a PhD in Engineering Sciences in 1999 from Harvard University. Until joining the University of Washington, she was first a postdoctoral scholar then a senior research fellow in Control and Dynamical Systems at the California Institute of Technology. She joined the William E. Boeing Department of Aeronautics and Astronautics in the summer of 2002 as an assistant professor and is currently Professor and Chair of the department. She is also co-Director of the UW Space Policy and Research Center (UW SPARC) and is the Director of the Washington NASA Space Grant Consortium. She has received a number of awards, most recently Fellow of AIAA and member of the Washington State Academy of Sciences.

Professor Morgansen’s research interests focus on nonlinear systems where sensing and actuation are integrated, stability in switched systems with delay, and incorporation of operational constraints such as communication delays in control of multi-vehicle systems. Applications include both traditional autonomous vehicle systems such as fixed-wing aircraft and underwater gliders as well as novel systems such as bio-inspired underwater propulsion, bio-inspired agile flight, human decision making, and neural engineering. The results of this work have been demonstrated in estimation and path planning in unmanned aerial vehicles with limited sensing, vorticity sensing and sensor placement on fixed wing aircraft, landing maneuvers in fruit flies, joint optimization of control and sensing in dynamical systems, and deconfliction and obstacle avoidance in autonomous systems and in biological systems including fish, insects, birds, and bats.

Prof. Morgansen’s research focuses on guidance, navigation, control for autonomous underwater, surface, air and space systems. She is an advocate for project-based learning, inclusive engineering, multidisciplinary collaboration, and STEAM.

Wednesday, February 9, 2022

3:30 PM

The Zoom webinar is at https://usc.zoom.us/j/93987337017?pwd=MWd2dXBSL1FaR1RPaHNscjJ1NW80UT09.

Where Do Flows Separate and How Does That Affect the Optimal Control Location?

Gustaaf Jacobs

Professor
Department of Aerospace Engineering
San Diego State University
San Diego, CA

Flow separation can degrade performance in many engineering systems, through reduced lift, increased drag, and decreased efficiency. To alleviate the effects of flow separation on aerodynamic performance, active flow control has been considered since the inception of the field of aerodynamics. Open-loop flow control strategies based on various actuator technologies — such as plasma actuators, fluidic oscillators, and synthetic jets — have been shown to effectively alter separated flows, and in some cases to even yield complete reattachment. Most analyses start from the placement of an actuator at an intuitively optimal location near the separation point and/or near the Kutta condition. Optimal placement, however, requires a detailed understanding of non-linear flow separation and wake feedback that is often counterintuitive. In this talk, I will discuss recent developments in Lagrangian analysis of flow separation. This kinematic analysis promises the objective identification of separation lines as zero-mass flux "material" lines whose footprint is analytically defined from first-principle. The separation profiles start with a subtle upwelling of Lagrangian fluid tracers upstream of the separation point. Using a data-driven technique (using DNS data) I will show that these upwelling locations may well point to optimal actuator locations that require minimal control effort.

Gustaaf Jacobs received a M.Sc. in Aerospace Engineering from the Delft University of Technology in 1998, where after graduation, he was appointed to a Research Associate. He received a Ph.D. in Mechanical Engineering from the University of Illinois at Chicago. Following graduation in 2003, he was appointed Visiting Assistant Professor in the Division of Applied Mathematics at Brown University. He later combined this position with a Postdoctoral Fellowship at the Department of Mechanical Engineering at the Massachusetts Institute of Technology. As of 2006 he was appointed Assistant Professor of Aerospace Engineering at San Diego State University and was promoted to Associate Professor in 2010 and Full Professor in 2014. In 2001 he received the Provost’s Award for Graduate Research at the University of Illinois at Chicago. In 2002, he was awarded a University Fellowship at the University of Illinois. He received an AFOSR Young Investigator Award in 2009. He became an Associate Fellow of AIAA in 2013. The research interests of Professor Jacobs can broadly be defined in the area of computational multiphase, and multiscale flow physics modeling and simulation using high-order methods. Emphasis is on simulation and analysis of particle-laden flows and flow separation in complex geometries, to aid flow control relating to combustion optimization and drag reduction.

Wednesday, February 16, 2022

3:30 PM

Zumberge Hall of Science, Room 252 (ZHS 252)

The Zoom webinar is at https://usc.zoom.us/j/93987337017?pwd=MWd2dXBSL1FaR1RPaHNscjJ1NW80UT09.

host: Spedding

Exterior Algebra and the Proportional Selective Modification of Dynamical Systems, from Rotors to Nonlinear Lattices

James Hanna

Associate Professor
Department of Mechanical Engineering
University of Nevada, Reno
Reno, NV

This is the story of a seemingly trivial problem, born of quarantine, that surprised me by turning into something more interesting. I will introduce a new technique for adding dissipation or otherwise modifying dynamical systems to selectively change any number of conserved quantities, while only reducing the total number of conserved quantities by one. I will first present a naïve approach to a simple example, a textbook problem of a specially damped rotor often used to explain the failure of the Explorer 1 satellite. Then (in joint work with M. Aureli), we generalize the approach to any number of dimensions and conserved quantities. The resulting dynamics drives the modified system to a nontrivial state of the original system.

James Hanna is Associate Professor in the department of Mechanical Engineering at the University of Nevada, Reno, which he joined in 2019 from Virginia Tech. A lapsed materials scientist, he spent several years impersonating a postdoctoral physicist at UMass Amherst, and currently performs mechanics without a license. He is interested in applications of geometry to theoretical and experimental classical mechanics, and is currently thinking about shell buckling, cable snapping, pseudomomentum and material symmetry, new formulations of elasticity, and a few other things.

Wednesday, February 23, 2022

3:30 PM

Zumberge Hall of Science, Room 252 (ZHS 252)

The Zoom webinar is at https://usc.zoom.us/j/93987337017?pwd=MWd2dXBSL1FaR1RPaHNscjJ1NW80UT09.

host: Plucinsky

Non-Equilibrium Behavior in Combustion, Planetary Atmospheres, and Compressible Flows

Michael Burke

Associate Professor
Mechanical Engineering Department
Columbia University
New York, NY

Chemically reacting flows are often interpreted and computed under the premise that all chemical species have a range of energies in their rotational and vibrational modes that are well described by the Boltzmann or thermal distribution at the local temperature. Of course, breakdown in this premise can occur naturally as a result of chemical reactions, light absorption, and/or shock waves. The manifestations of this breakdown on unimolecular reactions, where non-thermally distributed molecular ensembles dissociate, are well known to give rise to pressure-dependent reactions in combustion, photochemical reactions in the Earth’s atmosphere, and induction time lags in reactions following shock waves. By contrast, manifestations of non-equilibrium behavior on bimolecular reactions, where non-thermally distributed molecules react with other species, are generally less understood and historically less appreciated. Here, I describe three distinct tales of such non-equilibrium behavior across varied application domains. In particular, I present results from ab initio master equation calculations that shed light on previous hypotheses and experimental observations and reveal new processes involving non-equilibrium induced by chemistry in combustion, photons in the Earth’s atmosphere, and shock waves in compressible flows. Namely, the rovibrationally excited ephemeral complexes, formed from association of two molecules, with a third molecule give rise to a fourth, long-forgotten type of phenomenological reaction, involving three chemical reactants, that impacts macroscopic combustion behavior; the vibrationally excited complexes, formed upon photon absorption, collide with oxygen to produce radicals even for low photon energies in the Earth’s troposphere; and the rovibrationally cold molecular ensembles encountered following shock waves not only slow the reaction timescales but also change the main chemical pathways.

Michael Burke is an Associate Professor of Mechanical Engineering at Columbia University, where he also holds affiliate appointments in Chemical Engineering and the Data Science Institute. Prior to joining Columbia in 2014, Burke earned his Ph.D. in Mechanical and Aerospace Engineering in 2011 at Princeton University, where he was a Wallace Memorial Honorific Fellow, and he worked as a Director’s Postdoctoral Fellow in the Chemical Sciences and Engineering Division at Argonne National Laboratory. Burke is a recipient of the National Science Foundation’s CAREER award, the Combustion Institute’s Research Excellence Award, the Combustion Institute’s Hiroshi Tsuji Early Career Researcher Award, and the American Chemical Society’s PRF Doctoral New Investigator Award. His publications have been featured in the “News and Views” section of Nature Chemistry, selected as the Feature Article in Combustion and Flame, and chosen for the Distinguished Paper Award at the 31st International Symposium on Combustion. His research combines physics and data across multiple scales to unravel and predict outcomes of complex reacting systems in varied application domains with major emphases on theoretical chemistry of non-equilibrium processes, multiscale data-driven modeling, and high-throughput experiments selected by optimal design.

Wednesday, March 2, 2022

3:30 PM

Zumberge Hall of Science, Room 252 (ZHS 252)

The Zoom webinar is at https://usc.zoom.us/j/93987337017?pwd=MWd2dXBSL1FaR1RPaHNscjJ1NW80UT09.

host: Egolfopoulos

Data-Driven Discovery of Governing Equations with Deep Learning and Sparse Identification Techniques

Joseph Bakarji

Research Associate
Mechanical Engineering Department
University of Washington
Seattle, WA

Machine learning techniques promise to offer the ultimate form of automation, particularly when applied to computational modeling and simulation. As a consequence, the computational scientist's narrative now revolves around discovering physics directly from data, with as little assumptions about the underlying physical system as possible. I briefly go over the latest attempts to accomplish this goal and focus on my recent work in combining deep learning with sparse identification of differential equations. First, I show how probability distribution function (PDF) equations can be inferred from Monte Carlo simulations for coarse-graining and closure approximations. Second, I present our latest results on discovering dimensionless groups from data, using the Buckingham Pi theorem as a constraint. And third, I go over the deep delay autoencoder algorithm that reconstructs high dimensional models from partial measurements as motivated by Takens' embedding theorem. I finally highlight the limitations of these methods and propose a few directions for future research.

Joseph Bakarji is currently a postdoctoral fellow in the department of mechanical engineering at the University of Washington, working with Steven Brunton and Nathan Kutz. He received his PhD in 2020 from Stanford University where he developed multiscale stochastic models for granular materials and data-driven closure models for uncertainty quantification. Joseph received the Henry J. Ramey, Jr. and the Frank G. Miller fellowship awards in 2018 and 2020 respectively. His current research focuses on combining deep learning and sparse identification methods, to discover interpretable physical models in complex systems from data.

Wednesday, March 9, 2022

3:30 PM

Zumberge Hall of Science, Room 252 (ZHS 252)

The Zoom webinar is at https://usc.zoom.us/j/93987337017?pwd=MWd2dXBSL1FaR1RPaHNscjJ1NW80UT09.

host: Kanso

Status and Outlook for Controlled Fusion as a Firm Zero-Carbon Energy Source

George Tynan

Professor
Mechanical and Aerospace Engineering
UC San Diego
LaJolla, CA

Controlled fusion research has been pursued since the 1950s by most of the world's developed economies due to many attractive characteristics of this seemingly elusive technology. In 2021, inertial confinement fusion experiments at LLNL reached the threshold of fusion ignition while magnetic confinement experiments in the UK demonstrated that the ITER device nearing completion in France should, for the first time, produce a burning plasma in which fusion heating dominates the system. In parallel, a rapidly developing industry with $4B of private-sector funding has emerged and is pursuing a wide variety of approaches for controlled fusion. This talk will summarize the key elements of these developments, and sketch out the characteristics that fusion-based energy systems will need to demonstrate if they are to compete economically in the emerging zero-carbon energy system of the mid-century.

George Tynan studies the fundamental physics of turbulent transport in hot confined plasmas using both smaller scaled laboratory plasma devices as well as large scale fusion experiments located around the world. In addition, he is investigating how solid material surfaces interact with the boundary region of fusion plasmas, and how the materials are modified by that interaction. He is also interested in the larger issue of transitioning to a sustainable energy economy based upon a mixture of efficient end use technologies, large scale deployment of renewable energy sources, and incorporation of a new generation of nuclear technologies such as advanced fission and fusion reactor systems. He received his Ph.D. in 1991 from the Department of Mechanical, Aerospace, and Nuclear Engineering at the University of California, Los Angeles. He then spent several years studying the effect of sheared flows on plasma turbulence on experiments located in the Federal Republic of Germany and at Princeton Plasma Physics Laboratory, and worked in industry developing plasma sources for use in investigating the creation of submicron-scale semiconductor circuits. He joined the UCSD faculty in 1999 where he worked to establish a graduate program in plasma physics within the School of Engineering. He has served as Associate Vice Chancellor for Research, Associate Dean of Engineering, is co-founding Director of the UC San Diego Deep Decarbonization Initiative, and is currently Department Chair of Mechanical and Aerospace Engineering at the UC San Diego Jacobs School of Engineering.

Wednesday, March 23, 2022

3:30 PM

Zumberge Hall of Science, Room 252 (ZHS 252)

The Zoom webinar is at https://usc.zoom.us/j/93987337017?pwd=MWd2dXBSL1FaR1RPaHNscjJ1NW80UT09.

Computational Models of Cardiovascular Function

Shawn Shadden

Professor
Department of Mechanical Engineering
UC Berkeley
Berkeley, CA

Combining medical imaging and other forms of clinical data with first principles-, phenomenological- and/or statistical-based computational modeling has become an important avenue in cardiovascular research, including for disease diagnosis, treatment planning and scientific discovery. In this talk, I will provide some background on the field of computational modeling of cardiovascular biomechanics and will discuss some of our recent work focused on methods to improve personalization and efficiency of this modeling process. Namely, I will discuss developments on machine learning approaches to facilitate image-based model construction and parameterization, some of our work on reduced order modeling to facilitate efficient computation of common physical quantities of clinical importance, and where we might be headed.

Shawn Shadden is a Professor and Vice Chair of Mechanical Engineering at the University of California, Berkeley and a core member of the UCSF-UC Berkeley Graduate Program in Bioengineering. His research focuses on the computational modeling of cardiovascular biomechanics and the advancement of theoretical and numerical methods to quantify complex fluid flow. He is recipient of an NSF CAREER Award, a Bakar Faculty Fellow Award, Hellman Faculty Fellow Award, and the American Heart Association’s Established Investigator Award. His lab helps develop the SimVascular software platform, which is broadly used in the field of computational cardiovascular research.

Wednesday, March 30, 2022

3:30 PM

Zumberge Hall of Science, Room 252 (ZHS 252)

The Zoom webinar is at https://usc.zoom.us/j/93987337017?pwd=MWd2dXBSL1FaR1RPaHNscjJ1NW80UT09.

host: Pahlevan

Functional Interpretation for Transverse Arches of Human Foot

Shreyas Mandre

Associate Professor
Mathematics Institute
University of Warwick
Coventry, UK

Fossil record indicates that the emergence of arches in human ancestral feet coincided with a transition from an arboreal to a terrestrial lifestyle. Propulsive forces exerted during walking and running load the foot under bending, which is distinct from those experienced during arboreal locomotion. I will present mathematical models with varying levels of detail to illustrate a simple function of the transverse arch. Just as we curve a dollar bill in the transverse direction to stiffen it while inserting it in a vending machine, the transverse arch of the human foot stiffens it for bending deformations. A fundamental interplay of geometry and mechanics underlies this stiffening -- curvature couples the soft out-of-plane bending mode to the stiff in-plane stretching deformation. In addition to presenting a functional interpretation of the transverse arch of the foot, this study also indicates a classification of flat feet based on the skeletal geometry and mechanics.

Shreyas Mandre is an applied mathematician, an engineer, and a scientist.

Before moving to Warwick, he served as an Assistant Professor in the School of Engineering at Brown University from 2010 to 2019. He was also a Lecturer in Applied Mathematics at Harvard University. He received my Ph.D. in Mathematics from the University of British Columbia in 2006. His undergraduate education was in Mechanical Engineering from the Indian Institute of Technology Bombay followed by an M.S. from Northwestern University in the same subject.

His research spans continuum mechanics, biomechanics, and applied mathematics, with applications to biology and engineering.

Wednesday, January 6, 2022

3:30 PM

Zumberge Hall of Science, Room 252 (ZHS 252)

The Zoom webinar is at https://usc.zoom.us/j/93987337017?pwd=MWd2dXBSL1FaR1RPaHNscjJ1NW80UT09.

host: Kanso

Properties of Turbulent Channel Flow Similarity Solutions

Joseph Klewicki

Professor and Head of the School of Electrical, Mechanical and Infrastructure Engineering
Faculty of Engineering and IT
University of Melbourne
Parkville, Victoria, Australia

The notion of similarity solutions and their connection to the scaling problem in turbulent wall-flows are briefly introduced. Analytical evidence is then presented indicating that the flow in fully developed turbulent channel flow formally admits a similarity solution. High resolution direct numerical simulation (DNS) data are then used to investigate the properties of the similarity solutions for the mean velocity and Reynolds shear stress. The solutions and their associated similarity structure are used to generate a number of new results. These include a cogent specification for the both the inner and outer boundaries of the inertial sublayer and a variety of well-founded ways to estimate the key parameter ϕ_c at finite Reynolds number. Extensions of the analytical arguments by Klewicki et al. (Phys. Rev. E, 90, 2014, p. 063015) lend further support to their conjecture that at large Reynolds number ϕ_c → (1 + √5)/2, or equivalently, the von Karman constant is given by k = 2/(3 + √5). The primary non-rigorous aspects of the analysis are critiqued, and the connections between the channel solutions and those in the other canonical wall-flows are briefly discussed.

Joseph Klewicki is Head of the School of Electrical, Mechanical and Infrastructure ("EMI") Engineering in the Faculty of Engineering and IT at the University of Melbourne, Australia. He is also the Faculty Director of Infrastructure. He is a Fellow of the American Physical Society, the American Society of Mechanical Engineers, and the Australasian Fluid Mechanics Society. He is also a Distinguished Alumnus of the Michigan State University (MSU)'s Department of Mechanical Engineering, and received his BS (1983), MS (1985) and PhD (1989) degrees from MSU, Georgia Tech and MSU respectively. As a researcher, Professor Klewicki specializes in experimental methods in fluid mechanics, turbulent and unsteady flows, vorticity dynamics, boundary layers, and atmosphere surface layer phenomena. He conducts much of his research on the fluid dynamics of turbulent shear flows, with a special emphasis on wall-bounded turbulent flow and their Reynolds number scaling. This research involves both analytical and experimental studies, including the development of experimental methods, and involves other complex and turbulent flows. More recently, he has also begun to study phenomena specifically relevant to geophysical flows, namely those that include the effects of three-dimensionality, stratification, and rotation.

Thursday, April 14, 2022

10:00 AM

Laufer Conference Room (OHE 406)

host: Spedding

Predictive Modeling of Oscillating Foil Wake Dynamics

Jennifer Franck

Assistant Professor
Department of Engineering Physics
University of Wisconsin-Madison
Madison, Wisconsin

Swimming and flying animals rely on the fluid around them to provide lift or thrust forces, leaving behind a distinct vortex wake in the fluid. The structure and size of the vortex wake is a blueprint of the animal’s kinematic trajectory, holding information about the forces and also the size, speed and direction of motion. This talk will introduce a bio-inspired oscillating turbine, which can be operated to generate energy from moving water through lift generation, in the same manner as flapping birds or bats. This style of turbines offers distinct benefits compared with traditional rotation-based turbines such as the ability to dynamically shift its kinematics for changing flow conditions, thus altering its wake pattern. Current efforts lie in predicting the vortex formation and dynamics of the highly structured wake such that it can be utilized towards cooperative motion within arrays of oscillating foils. Using numerical simulations, this talk will discuss efforts towards linking the fluid dynamic wake signature to the underlying foil kinematics, and investigating how that effects the energy harvesting performance of downstream foils. Two machine learning methodologies are introduced to classify, cluster and identify complex vorticity patterns and modes of energy harvesting, and inform more detailed modeling of arrays of oscillating foils.

Jennifer Franck is an Assistant Professor in the Department of Engineering Physics at the University of Wisconsin-Madison. She leads the Computational Flow Physics and Modeling Lab, using computational fluid dynamics (CFD) techniques to explore the flow physics of unsteady and turbulent flows. Ongoing research projects are in the areas of bio-inspired flows and the fluid dynamics of renewable energy systems with current projects funded by NSF and ARPA-E. Prior to joining the UW-Madison faculty in 2018, she was faculty at Brown University. She received her undergraduate degree in Aerospace Engineering from University of Virginia, followed by a M.S. and Ph.D. from California Institute of Technology. Following her PhD, she was awarded an NSF Postdoctoral Fellowship hosted at Brown University to computationally explore fluid dynamics mechanics of flapping flight.

Wednesday, April 20, 2022

3:30 PM

Zumberge Hall of Science, Room 252 (ZHS 252)

The Zoom webinar is at https://usc.zoom.us/j/93987337017?pwd=MWd2dXBSL1FaR1RPaHNscjJ1NW80UT09.

Control Theoretic Applications for Biomedical Therapeutics

Michaëlle N. Mayalu

Assistant Professor
Department of Mechanical Engineering
Stanford University
Stanford, CA

The body uses feedback control strategies at intermolecular, intercellular and interorgan levels to maintain health and fight disease. Using mathematical models to understand and predict these control strategies gives insight into a wide array of biomedical applications ranging from engineered cell-based therapies to diet-based modulation of brain function.

For engineered cell-based therapies, cooperative feedback control of cell population density is an integral part in many genetic designs. In this multicellular coordination problem, control action takes place on two levels: i) individual cells can activate or repress relevant genes, ii) cells can access the ensemble state of the entire population as obtained through diffusible signaling molecules. These genetically altered cells can provide new and improved functionalities and act as “smart therapies” to make decisions based on intercellular communication and the environment. However, previous population controller genetic designs are not robust to mutational invasions.

For diet-based modulation of brain function, diet can initiate multiple interorgan feedback control systems that effect brain signaling and contribute to cognitive performance. Specifically, diet-mediated gut microbial signals influence nervous, immune, and bloodstream pathways which connect to memory function within the brain. It is desired to use diet to modulate gut microbiota as a novel therapy for maintaining cognitive performance. However, relationships between diet, changes in gut microbiota, activation of interorgan pathways, and alterations in brain signaling are not well understood.

In this talk I present mathematical frameworks from an integrated control theoretic, computational biology and healthcare perspective that: i) characterize genetic designs for robust feedback control of cell population and ii) elucidate the connections between diet and cognitive performance. These modeling frameworks share the underlying structure where communication between agents contribute to the prediction of a collective response. In healthcare contexts, this allows for better understanding and manipulation of the connection between therapeutic targets and dominant patterns within the biological process. Using these models, we further analyze internal mechanisms, performance properties, and derive general design principles and functional relationships in the context of the aforementioned biomedical therapies.

Michaëlle N. Mayalu is an Assistant Professor of Mechanical Engineering. She received her Ph.D., M.S., and B.S., degrees in Mechanical Engineering at the Massachusetts Institute of Technology. She was a postdoctoral scholar at the California Institute of Technology in the Computing and Mathematical Sciences Department. She was a 2017 California Alliance Postdoctoral Fellowship Program recipient and a 2019 Burroughs Wellcome Fund Postdoctoral Enrichment Program award recipient.

Dr. Michaëlle N. Mayalu's area of expertise is in mathematical modeling and control theory of synthetic biological and biomedical systems. She is interested in the development of control theoretic tools for understanding, controlling, and predicting biological function at the molecular, cellular, and organismal levels to optimize therapeutic intervention.

She is the director of the Mayalu Lab whose research objective is to investigate how to optimize biomedical therapeutic designs using theoretical and computational approaches coupled with experiments. Initial project concepts include: i) theoretical and experimental design of bacterial "microrobots" for preemptive and targeted therapeutic intervention, ii) system-level multi-scale modeling of gut associated skin disorders for virtual evaluation and optimization of therapy, iii) theoretical and experimental design of "microrobotic" swarms of engineered bacteria with sophisticated centralized and decentralized control schemes to explore possible mechanisms of pattern formation. The experimental projects in the Mayalu Lab utilize established techniques borrowed from the field of synthetic biology to develop synthetic genetic circuits in E. coli to make bacterial "microrobots". Ultimately the Mayalu Lab aims to develop accurate and efficient modeling frameworks that incorporate computation, dynamical systems, and control theory that will become more widespread and impactful in the design of electro-mechanical and biological therapeutic machines.

Website: https://mayalulab22.sites.stanford.edu/

Wednesday, August 24, 2022

3:30 PM

Seaver Science Library, Room 202 (SSL 202)

A Zoom invitation to this seminar will also be posted here.

host: Nguyen

Macroscopic Forcing Method: A Computational Method for Evaluation of Turbulence Closure Operators

Ali Mani

Associate Professor
Department of Mechanical Engineering
Stanford University
Stanford, CA

This study presents a numerical procedure, which we call the macroscopic forcing method (MFM), which reveals the differential operators acting upon the mean fields of quantities transported by underlying fluctuating flows. Specifically, MFM can reveal differential operators associated with turbulent transport of scalars and momentum. We present this methodology by considering canonical problems with increasing complexity. For spatially homogeneous and statistically stationary systems, we observe that eddy diffusivity can be approximated by an operator of the form D/√(1-l²∇²), where l is the mixing length, which in turbulent flows is on the order of the large-eddy size and D is the Boussinesq limit eddy diffusivity. We show a cost-effective generalization of MFM for analysis of non-homogeneous and wall-bounded flows, where eddy diffusivity is found to be a non-local and non-isotropic operator acting on the macroscopic gradient of transported quantities. Towards the end of this talk, application of MFM on a canonical separated flow will be presented where the tensorial eddy viscosity is quantified, and its anisotropy is shown to be the key missing piece in RANS predictions.

Ali Mani is an associate professor of Mechanical Engineering at Stanford University. He is a faculty affiliate of the Center for Turbulence Research and a member of Institute for Computational and Mathematical Engineering at Stanford. He received his PhD in Mechanical Engineering from Stanford in 2009. Prior to joining the faculty in 2011, he was a senior postdoctoral associate at Massachusetts Institute of Technology in the Department of Chemical Engineering. His research group builds and utilizes large-scale high-fidelity numerical simulations, as well as methods of applied mathematics, to develop quantitative understanding of transport processes that involve strong coupling with fluid flow and commonly involve turbulence or chaos. His teaching includes the undergraduate engineering math classes and graduate courses on fluid mechanics and numerical analysis. He is the recipient of an Office of Naval Research Young Investigator Award (2015), NSF Career Award (2016), and Tau Beta Pi Teaching Honor Roll (2019).

Wednesday, August 31, 2022

3:30 PM

Seaver Science Library, Room 202 (SSL 202)

The Zoom webinar is at
https://usc.zoom.us/j/98775609685?pwd=a2lSd01oY0o2KzA4VWphbGxjWk5Qdz09.

host: Domaradzki

Advances in Exhaled Breath Metabolomics Analysis and Diagnostics

Cristina Davis

Professor
and
Associate Vice Chancellor for Interdisciplinary Research and Strategic Initiatives
Mechanical and Aerospace Engineering
University of California, Davis
Davis, CA

There is an entire field of research dedicated to the chemical analysis of exhaled breath, and the enticing promise of non-invasive medical diagnostics and monitoring. Biomarker detection and identification in breath rests on appropriate sampling and analysis protocols, which are now well established. There is compelling evidence breath chemicals change over time in response to illness and overall health and exposures. Exhaled breath is comprised of both breath gas vapors (CO2, NO, volatile organic compounds) and small diameter breath aerosols (with proteins, peptides, drugs and large metabolites frequently observed in blood). We have advanced controlled breath sampling systems for exhaled breath vapors (EBV) and exhaled breath condensate (EBC) which samples both the gas and aerosol breath fractions. We have also developed mass spectrometry-based analysis methodologies and directly compare metabolite coverage in EBC to guide sampling and methodology choices. We have used this approach to measure large molecules in EBC that are physiologically relevant (e.g. drugs and inflammatory biomarkers), and we have developed devices appropriate to use in clinical settings.

Cristina Davis Cristina Davis is a Professor of Mechanical and Aerospace Engineering at the University of California, Davis (Davis, CA). Her research group focuses on creating miniature analytical sensor systems for mobile chemical detection platforms and human performance monitoring. Final system integration of her devices yields analyzers that are specifically tailored for various high impact application areas including biomedical monitoring and surveillance for precision medicine.

Prof. Davis earned her BS degree (1994) at Duke University with a double major in mathematics and biology. She went on to complete her MS (1996) and PhD (1999) in biomedical engineering at the University of Virginia focusing on novel biosensor research. She then worked on silicon-chip based biosensors during a postdoctoral fellowship at The Johns Hopkins University. She then worked in industry in Switzerland and then to become a Principal Member of the Technical Staff and the founding Group Leader of Bioengineering at The Charles Stark Draper Laboratory (Cambridge, MA) Having spent almost a half-decade in the national labs and industry, she returned to academia in November 2005.

She served as a Member of the Scientific Advisory Board (SAB) for the United States Air Force (2014-2018), and is a Fellow of the American Institute for Medical and Biological Engineering (AIMBE), the American Association for the Advancement of Science (AAAS) and National Academy of Inventors (NAI). She has been a Co-Founder and Scientific Advisor to three start-ups based on her research.

Wednesday, September 7, 2022

3:30 PM

The Zoom webinar is at https://usc.zoom.us/j/98775609685?pwd=a2lSd01oY0o2KzA4VWphbGxjWk5Qdz09.

host: Ronney

Adaptive Scale-Similar Closure: Toward the Most General Stabilized Subgrid Model for Multi-Physics LES

Werner J.A. Dahm

ASU Foundation Professor of Aerospace and Mechanical Engineering
School for Engineering of Matter, Transport and Energy
Arizona State University
Tempe, AZ

This seminar presents an adaptive scale-similar closure approach that can dynamically represent any subgrid term accurately and stably even at the smallest resolved scales of a simulation. The approach is based on scale similarity and generalized representations from the complete and minimal tensor representation theory of Smith (1971). At each point, the local tensor polynomial coefficients adapt to the local turbulence state via system identification at a test-filter scale and rescaling to the LES-scale. The methodology is demonstrated by applying it to the subgrid stress and subgrid scalar flux. Resulting fields for the subgrid terms and production rates are nearly indistinguishable from corresponding true fields, and are far more accurate than traditional subgrid models. Stability is ensured by a physics-based rational Boolean stabilization method, which uses the local subgrid production and subgrid redistribution rates to determine how individual subgrid components must be rescaled to provide local backward-transfer reduction or forward-transfer amplification. This produces only very small changes in the highly accurate fields for the subgrid terms and production rates that result from this new closure methodology. Together, adaptive scale-similar closure and rational Boolean stabilization essentially solve two key problems that have previously limited the accuracy of multi-physics large eddy simulations.

Werner J.A. Dahm is Professor Emeritus of Aerospace Engineering at the University of Michigan, where he was on the faculty for 25 years, and since 2010 has been the ASU Foundation Professor of Mechanical and Aerospace Engineering at Arizona State University. Previously he served in the Pentagon as the Chief Scientist of the U.S. Air Force, and in numerous senior technical advisory roles, including on the Air Force Scientific Advisory Board since 2005 and as Chair of the Board from 2014-2017. He is an AIAA Fellow, an APS Fellow, and recipient of the Air Force Decoration for Exceptional Civilian Service and the Secretary of the Air Force Distinguished Public Service Award.

Wednesday, September 14, 2022

3:30 PM

Seaver Science Library, Room 202 (SSL 202)

The webinar will also be on Zoom at
https://usc.zoom.us/j/98775609685?pwd=a2lSd01oY0o2KzA4VWphbGxjWk5Qdz09.

host: Ronney

Wall-Models for Large-Eddy Simulations of Turbulent Flows via Scientific Multi-Agent Reinforcement Learning

H. Jane Bae

Assistant Professor
Graduate Aerospace Laboratories
California Institute of Technology
Pasadena, CA

The predictive capabilities of turbulent flow simulations, critical for aerodynamic design and weather prediction, hinge on the choice of turbulence models. The abundance of data from experiments and simulations and the advent of machine learning have provided a boost to turbulence modeling efforts. However, simulations of turbulent flows remain hindered by the inability of heuristics and supervised learning to model the near-wall dynamics. We address this challenge by introducing scientific multi-agent reinforcement learning (SciMARL) for the discovery of wall models for large-eddy simulations (LES). In SciMARL, discretization points act also as cooperating agents that learn to supply the LES closure model. The agents self-learn using limited data and generalize to higher Reynolds numbers in reproducing key flow quantities. We test the discovered wall model to canonical flat plate boundary layers, which shows good predictable capabilities outside the Reynolds numbers used to train the model. We will discuss extensions to this model for flows with pressure-gradient effects.

Jane Bae is an Assistant Professor of Aerospace at the Graduate Aerospace Laboratories at Caltech. She received her Ph.D. in Computational and Mathematical Engineering from Stanford University in 2018. She was a postdoctoral fellow in the Graduate Aerospace Laboratories at Caltech and the Institute for Applied Computational Science at Harvard University before joining the Caltech faculty. Her main research focuses on computational fluid mechanics, in particular on modeling and control of wall-bounded turbulence.

Wednesday, September 21, 2022

3:30 PM

Seaver Science Library, Room 202 (SSL 202)

The Zoom webinar is at https://usc.zoom.us/j/98775609685?pwd=a2lSd01oY0o2KzA4VWphbGxjWk5Qdz09.

host: Bermejo-Moreno

Multimaterial Additive Manufacturing toward Shape Changing Functional Devices and 4D Printing

H. Jerry Qi

Professor
School of Mechanical Engineering
Georgia Institute of Technology
Atlanta, GA

3D printing (additive manufacturing) where materials are deposited in a layer-by-layer manner to form a 3D solid has seen significant advances in the recent decades. 3D printing has the advantage in creating a part with complex geometry from a digit file, making them an idea candidate for making architected materials. Multimaterial 3D printing is an emerging field in recent years in additive manufacturing. It offers the advantage of placement of materials with different properties in the 3D space with high resolution, or controllable heterogeneity. In this talk, we present our recent progress in developing multimaterial additive manufacturing methods. In the first approach, we present a new development of a novel multi-material multi-method (m4) 3D printing where we integrate four types of additive manufacturing methods and two complementary methods into one platform. In the second approach, we recently developed a novel grayscale digit light processing (DLP) 3D printing method where we can print a part with gradient material properties. We further explore on how to use multimaterial 3D printing to fabricate architected materials and demonstrate their advantage, including direct 4D printing of 2D lattice structures, lattice structures with changing shape driven by liquid crystal elastomers, and 3D lattice structures by gradient materials.

H. Jerry Qi is a professor in the School of Mechanical Engineering at Georgia Institute of Technology and is the site director of NSF IUCRC on Science of Heterogeneous Additive Printing of 3D Materials (SHAP3D). He received his undergraduate and graduate degrees from Tsinghua University and a ScD degree from MIT. After one-year postdoc at MIT, he joined University of Colorado Boulder as an assistant professor and moved to Georgia Tech in 2014. Prof. Qi’s research is in the broad field of nonlinear mechanics of polymeric materials and focuses on developing fundamental understanding of multi-field properties of soft active materials through experimentation and constitutive modeling then applying these understandings to application designs. He and his collaborators have been working on a range of soft active materials, including shape memory polymers, shape memory elastomeric composites, light activated polymers, covalent adaptable network polymers, for their interesting behaviors such as shape memory, light actuation, surface patterning, surface welding, healing, and reprocessing. In recent years, he has been working on investigating integrating active materials with 3D printing. He and his collaborators pioneered the 4D printing concept. Prof. Qi is a recipient of NSF CAREER award (2007) and was elected to an ASME Fellow in 2015.

Wednesday, September 28, 2022

3:30 PM

Seaver Science Library, Room 202 (SSL 202)

The Zoom webinar is at https://usc.zoom.us/j/98775609685?pwd=a2lSd01oY0o2KzA4VWphbGxjWk5Qdz09.

host: Chen

What Makes Turbulence Tick?

Beverley J. McKeon

Theodore von Karman Professor of Aeronautics
GALCIT
California Institute of Technology
Pasadena, CA

In this lecture, I will utilize the classical approaches and tools of the modern day – theoretical analysis and data-driven methods, respectively – together with novel laboratory experiments to illuminate key features of nonlinear interactions in the Navier-Stokes equations. Focusing on a spatio-temporal representation of turbulence near walls – an omnipresent phenomenon in large-scale transport and transportation - interscale interactions are identified and quantified, then reduced to key elements responsible for sustaining turbulence. Methods to obtain data-driven representations of both linear and nonlinear dynamics will be discussed, along with some implications for the modeling of wall turbulence. The work has benefited from funding by the US ONR and AFOSR over a period of years, which is gratefully acknowledged.

Beverley J. McKeon is the Theodore von Karman Professor of Aeronautics at the Graduate Aerospace Laboratories at Caltech (GALCIT) and former Deputy Chair of the Division of Engineering & Applied Science. Effective January 2023, she will be a Professor of Mechanical Engineering at Stanford University. She received her B.A., M.A. and M.Eng. from the University of Cambridge in the United Kingdom, and an M.A. and Ph.D. in Mechanical and Aerospace Engineering from Princeton University under the supervision of Lex Smits. She completed postdoctoral research and a Royal Society Dorothy Hodgkin Fellowship at Imperial College London before arriving at Caltech in 2006. Her research interests include interdisciplinary approaches to manipulation of boundary layer flows using morphing surfaces, fundamental investigations of wall turbulence and the influence of the wall at high Reynolds number, the development of resolvent analysis for modeling turbulent flows, and assimilation of experimental data for efficient low-order flow modeling. Prof. McKeon is a Fellow of the APS and the AIAA and the recipient of a Vannevar Bush Faculty Fellowship from the DoD in 2017, the Presidential Early Career Award (PECASE) in 2009 and an NSF CAREER Award in 2008 as well as Caltech’s Shair Program Diversity Award, Graduate Student Council Excellence in Mentoring Award and Northrop Grumman Prize for Excellence in Teaching. She currently serves as co-Lead Editor of Physical Review Fluids, as Physical Sciences co-captain on the National Academies Decadal Survey on Biological and Physical Sciences Research in Space 2023-32, and on the editorial board of the Annual Review of Fluid Mechanics, and is a past editor-in-chief of Experimental Thermal and Fluid Science. She is the current Chair, and APS representative, of the US National Committee on Theoretical and Applied Mechanics.

Wednesday, October 5, 2022

3:30 PM

Seaver Science Library, Room 202 (SSL 202)

The Zoom webinar is at
https://usc.zoom.us/j/98775609685?pwd=a2lSd01oY0o2KzA4VWphbGxjWk5Qdz09.

host: Bermejo-Moreno

Sensing and Monitoring using Nanocomposite Sensors and Hybrid Copper Conductive Inks

Simon S. Park

Professor
Micro Engineering Dynamics and Automation Laboratory (MEDAL)
Dept. of Mechanical & Manufacturing Engineering
University of Calgary
Alberta, Canada

Highly accurate, miniaturized components that consist of a variety of materials will play key roles in the future development of a broad spectrum of products, such as wearable devices, lab-on-chips, subminiature actuators and sensors. With the advent of the Internet of Things (IoTs) and Industrie 4.0, the development of miniature and reliable devices will be far-reaching in the enhancement of quality of life and economic growth.

Smart polymeric nanocomposites are promising new materials applicable as media for nano-patterned surfaces. Much attention is being paid to carbon-based nanoparticles as fillers in polymer matrices, due to their outstanding mechanical, electrical and thermal properties. In particular, carbon nanotubes (CNTs) and graphenes are effective in the fabrication of electrically and thermally conductive polymer composites compared to metallic particles or carbon black, mainly due to their high aspect ratios (i.e. ~100-1000).

The sensors consisted of polymer reinforced with multi-walled carbon nanotubes (MWCNTs)/graphenes using a variety of manufacturing techniques. The sensors were electrically poled to generate piezoelectric phases. Both the piezoresistive and piezoelectric characteristics of the nanocomposite were utilized for improved performance of the sensors.

Another important aspect is cost effective manufacturing of conductive electrode patterns onto flexible substrates is vital for multifunctional and flexible systems. Conventional chemical etching, vacuum deposition and electrodeless plating are expensive and potentially hazardous to flexible substrates. Others have used metallic nanoparticle inks, such as silver nanoparticles, through inkjet printing, but the high cost of silver nanoparticles prevents mass production. We have recently developed a simple method to prepare hybrid copper-silver conductive tracks through flash light sintering. We demonstrate some of examples of the sensors and hybrid copper electrodes developments.

Currently Simon S. Park is a professor at the Schulich School of Engineering, Dept. of Mechanical and Manufacturing Engineering, University of Calgary. He is a professional engineer in Alberta, and is an associate member of CIRP (Int. Academy of Production Engineers) from Canada. Dr. Park received bachelor and master’s degrees from the University of Toronto, Canada. He then continued his PhD at the University of British Columbia, Canada. He has worked in several companies including IBM manufacturing where he was a procurement engineer for printed circuit boards and Mass Prototyping Inc. dealing with rapid prototyping systems. In 2004, Dr. Park formed the Micro Engineering, Dynamics, and Automation Laboratory (MEDAL, www.ucalgary.ca/medal) to investigate the synergistic integration of both subtractive and additive processes that uniquely provide productivity, flexibility and accuracy to the processing of complex components. His research interests include micro machining, nano engineering, CNT nanocomposites, and alternative energy applications. He has also founded several start-up companies in sensing and oil extractions. He held a strategic chair position in AITF Sensing and monitoring. He is also an associate editor of the Journal of Manufacturing Processes, SME (Elsevier) and International Journal of Precision Engineering and Manufacturing-Green Technology (Springer). Currently, he is directly supervising 40 students and scholars.

Wednesday, October 19, 2022

3:30 PM

This Zoom-only webinar is at https://usc.zoom.us/j/98775609685?pwd=a2lSd01oY0o2KzA4VWphbGxjWk5Qdz09.

host: Chen

Construction of Hydrodynamic Models for Nonequilibrium Flows: Application to Hypersonics

Marco Panesi

Associate Professor
Center for Hypersonics and Entry Systems Studies
Department of Aerospace Engineering
University of Illinois at Urbana-Champaign
Urbana, IL

The simulation of the aerothermal environment surrounding vehicles moving at hypersonic speed is a complex problem due to its multi-physics and multi-scale nature. Progress in accurately modeling these systems has been hindered by the lack of reliable physical models for the thermochemical and transport processes that dominate the dynamics of the flow. The most physically consistent description of nonequilibrium flows relies on the direct numerical solution of the kinetic equations for each internal state of the gas particles. However, for problems of interest, the exponentially large many degrees of freedom, and the wide range of spatial and temporal scales involved, make these equations unsolvable.

This talk outlines a new paradigm for constructing predictive modeling and simulation tools from a fundamental physics perspective, rejecting the empiricism that has prevented progress in modeling hypersonic flows for decades. Inspired by model reduction strategies developed in statistical physics, this work addresses the challenges of the combinatorial explosion of the possible configurations of the system,obtaining new governing equations by projecting the master equation onto a few lower-dimensional subspaces. The distribution function within each subspace is then reconstructed using the Maximum Entropy Principle, thus ensuring compliance with the Detailed Balance.

I will cover the critical aspects involved in model development: (1) using direct numerical simulationto study the fundamental physics; (2) derivation of a reduced-order set of equations that give an accurateand physical consistent description of the physics at a much-reduced computational cost: (3) Validationand uncertainty quantification.

Dr. Marco Panesi is currently a Professor in the Aerospace Engineering Department and director of the Center for Hypersonics and Entry System Studies (CHESS) at the University of Illinois at Urbana-Champaign. In 2009, he received a Ph.D. degree from the von Karman Institute for Fluid Dynamics. He completed a post-doc with the PECOS center, one of the five DOE-funded PSAAP centers, at Oden’s Institute. Prof. Panesi joined the faculty in the Department of Aerospace Engineering at the University of Illinois at Urbana-Champaign as an assistant professor in August 2012.

Prof. Panesi has won several awards, including the Vannevar Bush Faculty Fellowship (VBFF), the Young Investigator Program (YIP) award from AFOSR, and the Early Career Faculty award from NASA. He has won the Best Paper/Presentation Awards at AIAA conferences several times. In 2015, he received the Award on Physical Modelling at the Symposium on Aerothermodynamics for Space Vehicles (ESA) for his contribution to the fundamentals of Aerothermodynamics.

Wednesday, November 2, 2022

3:30 PM

Seaver Science Library, Room 202 (SSL 202)

and via Zoom at

https://usc.zoom.us/j/98775609685?pwd=a2lSd01oY0o2KzA4VWphbGxjWk5Qdz09.

host: Pantano

Functional Kirigami From Cuts in Sheets to Muscles, Grippers, and Actuators

Douglas P. Holmes

Associate Professor
Department of Mechanical Engineering
Boston University
Boston, MA

It is far easier to bend an object than it is to stretch it, and so how does one design thin structures capable of stretching and adopting complex shapes? In recent years, engineers have used cuts in thin sheets to provide local regions that can easily deform. Termed kirigami, in reference to the ancient Japanese art of folding and cutting paper, kirigami-based mechanical metamaterials have provided a simple way to endow a generic material with extraordinary properties.

This seminar will serve as an overview of our work on cutting thin sheets and shells to enable the design of functional mechanical metamaterials. We create linear actuators, artificial muscles, soft robotic grippers, and mechanical logic units by systematically cutting and stretching thin sheets. Lattice cuts, in which cuts are oriented perpendicular to the stretching direction, provide a simple way to enhance the stretchability of a thin sheet. We show that certain lattice configurations are more stretchable than others, while certain configurations produce an array of bistable unit cells. The bistability provides a means to tune the stiffness of the structure in situ, while also providing a means for mechanical logic. We demonstrate the how to switch between stable states using magnetic actuation. Lattice cuts on curved sheets, i.e. kirigami shells, enable additional functionality. The natural curvature of the sheet causes the bistable lattices to curve together and close around an object, which enables the kirigami shells to act as soft robotic grippers. We will discuss the optimal kirigami geometry for a robotic gripper, and describe how the structures perform at both grasping and holding irregular objects. Finally, non–lattice cuts open a range of actuation possibilities. We show that cuts on the edge of the sheet enable local rotation, a soft mode of deformation, while cuts in the bulk of the sheet generate a local lift, a stiff mode of deformation. Coupling these soft rotation modes with stiff lift modes enables us to generate kirigami linear actuators, that exhibit pitch, yaw, roll, and lift in response to uniaxial stretching. The underlying buckling mechanism is independent of thickness to a first order approximation, and thus these results translate down to 2D materials such as graphene and MoS2, as we demonstrate with MD simulations.

host: Plucinsky

Reduced Order Modeling and Inverse Design of Flexible Structures by Machine Learning

M. Khalid Jawed

Assistant Professor
Department of Mechanical and Aerospace Engineering
University of California at Los Angeles
Los Angeles, CA

Recent advances in highly deformable structures necessitate simulation tools that can capture nonlinear geometry and nonlinear material. We propose machine learning, neural networks (NN) in particular, to capture this nonlinearity and solve highly nonlinear inverse problems in structural mechanics. Two representative problems will be discussed in this talk.

In the first problem, we use NN to reduce the number of variables and speed up the simulation by orders of magnitude. As a test case, we explore the dynamical simulation of a slinky, a pre-compressed elastic helix that is widely used as a toy for children. However, most often the deformation of a slinky can be fully captured by the deformation of its helix axis. Instead of simulating the entire helical structure, the axis of the helix is a reduced-order representation of this system. We use NN to store the elastic forces of the slinky in its reduced-order representation, utilizing the concept of neural ordinary differential equations. The NN is trained using data from a fine-grained 3D rod simulation called the Discrete Elastic Rods (DER). Once the elastic forces in the reduced representation are stored in the NN, force balance equations can be solved in this representation for the dynamic simulation. This results in savings in computational time without much impact on its physical accuracy.

In the second problem, we explore shape-morphing structures that spontaneously transition from planar to 3D shapes. This is a transformative technology with broad applications in soft robotics and deployable systems. However, realizing these morphing structures that can achieve certain target shapes is challenging and typically involves a painstaking process of trials and errors with complex local fabrication and actuation. We propose a rapid design approach for fully soft structures that can achieve targeted 3D shapes through a fabrication process that happens entirely on a 2D plane. By combining the strain mismatch between layers in a composite shell and locally relieving stress by creating kirigami cuts, we are able to create 3D free buckling shapes from planar fabrication. However, the large design space of the kirigami cuts and strain mismatch presents a challenging task of inverse form finding. We develop a symmetry-constrained active learning approach to learn how to explore the large design space strategically. Interestingly, we report that, given a target 3D shape, multiple design solutions exist, and our physics-guided machine learning approach can find them in a few hundred iterations. Desktop-controlled experiments and finite element simulations are in good agreement in examples ranging from peanuts to flowers.

Acknowledgment: Our lab is supported by the National Science Foundation (Award numbers: IIS-1925360, CMMI-2053971, CMMI-2101751, CAREER-2047663, OAC-2209782, CNS-2213839), the National Institute of Food and Agriculture of the US Department of Agriculture (Award # 2021-67022-34200, 2022-67022-37021), and the Department of Energy (Smart Manufacturing Institute, UCLA).

M. Khalid Jawed is an Assistant Professor in the Department of Mechanical and Aerospace Engineering of the University of California, Los Angeles, and the Principal Investigator of the Structures-Computer Interaction Laboratory. He received his Ph.D. and Master's degrees in Mechanical Engineering from the Massachusetts Institute of Technology in 2016 and 2014, respectively. He holds dual Bachelor's degrees in Aerospace Engineering and Engineering Physics from the University of Michigan, Ann Arbor. He also served as a Postdoctoral Researcher at Carnegie Mellon University. He received the NSF CAREER Award in 2021, the outstanding teaching award from UCLA in 2019, the outstanding teaching assistant award from MIT in 2015, and the GSNP best speaker award at the American Physical Society March Meeting in 2014.

Dr. Jawed's research interests lie at the intersection of structural mechanics and robotics, emphasizing a data-driven and artificially intelligent approach to the modeling and design of programmable smart structures. Current research projects include robotic manipulation of flexible structures, numerical simulation of highly deformable structures, soft robotics, and robotics for precision agriculture.

Wednesday, November 16, 2022

3:30 PM

Seaver Science Library, Room 202 (SSL 202), and

On Zoom at https://usc.zoom.us/j/98775609685?pwd=a2lSd01oY0o2KzA4VWphbGxjWk5Qdz09.

host: Nguyen

Generalisable 3D Printing Error Detection and Correction via Neural Networks

Sebastian Pattinson

Assistant Professor
Department of Engineering
University of Cambridge
Cambridge, UK

Material extrusion is the most widespread additive manufacturing method but its application in end-use products is limited by vulnerability to errors. Humans can detect errors but cannot provide continuous monitoring or real-time correction. Existing automated approaches are not generalisable across different parts, materials, and printing systems. In this talk I will discuss recent work in our lab where we train a multi-head neural network using images automatically labelled by deviation from optimal printing parameters. The automation of data acquisition and labelling allows the generation of a large and varied extrusion 3D printing dataset, containing 1.2 million images from 192 different parts labelled with printing parameters. The thus trained neural network, alongside a control loop, enables real-time detection and rapid correction of diverse errors that is effective across many different 2D and 3D geometries, materials, printers, toolpaths, and even extrusion methods.

Sebastian Pattinson is an Assistant Professor in the Department of Engineering at the University of Cambridge. His group develops 3D printers that learn how to make things better and uses these to make better medical devices. Before joining the Cambridge, Sebastian was a postdoctoral fellow in the Department of Mechanical Engineering at MIT focusing on 3D printed materials and devices. He received Ph.D. and Masters degrees in the Department of Materials Science & Metallurgy at the University of Cambridge, where he developed nanomaterial synthesis methods. His awards include a UK Academy of Medical Sciences Springboard award; US National Science Foundation postdoctoral fellowship; UK Engineering and Physical Sciences Research Council Doctoral Training Grant; MIT Translational Fellowship; and a (Google) X Moonshot Fellowship.

Wednesday, November 30, 2022

3:30 PM

The Zoom webinar is at https://usc.zoom.us/j/98775609685?pwd=a2lSd01oY0o2KzA4VWphbGxjWk5Qdz09.

host: Zhao