Conferences, Lectures, & Seminars
Events for November
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AME Seminar
Wed, Nov 02, 2022 @ 03:30 PM - 04:30 PM
Aerospace and Mechanical Engineering
Conferences, Lectures, & Seminars
Speaker: Marco Panesi, University of Illinois Urbana-Champaign
Talk Title: Construction of Hydrodynamic Models for Nonequilibrium Flows: Application to Hypersonics
Abstract: 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.
Biography: 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 Odens 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.
Host: AME Department
More Info: https://ame.usc.edu/seminars/
Webcast: https://usc.zoom.us/j/98775609685?pwd=a2lSd01oY0o2KzA4VWphbGxjWk5Qdz09Location: Seaver Science Library (SSL) - 202
WebCast Link: https://usc.zoom.us/j/98775609685?pwd=a2lSd01oY0o2KzA4VWphbGxjWk5Qdz09
Audiences: Everyone Is Invited
Contact: Tessa Yao
Event Link: https://ame.usc.edu/seminars/
This event is open to all eligible individuals. USC Viterbi operates all of its activities consistent with the University's Notice of Non-Discrimination. Eligibility is not determined based on race, sex, ethnicity, sexual orientation, or any other prohibited factor. -
AME Seminar
Wed, Nov 09, 2022 @ 03:30 PM - 04:30 PM
Aerospace and Mechanical Engineering
Conferences, Lectures, & Seminars
Speaker: Douglas Holmes, Boston University
Talk Title: TBD
Host: AME Department
More Info: https://ame.usc.edu/seminars/
Webcast: https://usc.zoom.us/j/98775609685?pwd=a2lSd01oY0o2KzA4VWphbGxjWk5Qdz09Location: Seaver Science Library (SSL) - 202
WebCast Link: https://usc.zoom.us/j/98775609685?pwd=a2lSd01oY0o2KzA4VWphbGxjWk5Qdz09
Audiences: Everyone Is Invited
Contact: Tessa Yao
Event Link: https://ame.usc.edu/seminars/
This event is open to all eligible individuals. USC Viterbi operates all of its activities consistent with the University's Notice of Non-Discrimination. Eligibility is not determined based on race, sex, ethnicity, sexual orientation, or any other prohibited factor. -
AME Seminar
Wed, Nov 16, 2022 @ 03:30 PM - 04:30 PM
Aerospace and Mechanical Engineering
Conferences, Lectures, & Seminars
Speaker: Khalid Jawed, UCLA
Talk Title: Reduced Order Modeling and Inverse Design of Flexible Structures by Machine Learning
Abstract: 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).
Biography: 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. Jaweds 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.
Host: AME Department
More Info: https://ame.usc.edu/seminars/
Webcast: https://usc.zoom.us/j/98775609685?pwd=a2lSd01oY0o2KzA4VWphbGxjWk5Qdz09Location: Seaver Science Library (SSL) - 202
WebCast Link: https://usc.zoom.us/j/98775609685?pwd=a2lSd01oY0o2KzA4VWphbGxjWk5Qdz09
Audiences: Everyone Is Invited
Contact: Tessa Yao
Event Link: https://ame.usc.edu/seminars/
This event is open to all eligible individuals. USC Viterbi operates all of its activities consistent with the University's Notice of Non-Discrimination. Eligibility is not determined based on race, sex, ethnicity, sexual orientation, or any other prohibited factor. -
AME Seminar (Virtual)
Wed, Nov 30, 2022 @ 03:30 PM - 04:30 PM
Aerospace and Mechanical Engineering
Conferences, Lectures, & Seminars
Speaker: Sebastian Pattinson, University of Cambridge
Talk Title: Generalisable 3D Printing Error Detection and Correction via Neural Networks
Abstract: 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.
Biography: 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.
Host: AME Department
More Info: https://ame.usc.edu/seminars/
Webcast: https://usc.zoom.us/j/98775609685?pwd=a2lSd01oY0o2KzA4VWphbGxjWk5Qdz09WebCast Link: https://usc.zoom.us/j/98775609685?pwd=a2lSd01oY0o2KzA4VWphbGxjWk5Qdz09
Audiences: Everyone Is Invited
Contact: Tessa Yao
Event Link: https://ame.usc.edu/seminars/
This event is open to all eligible individuals. USC Viterbi operates all of its activities consistent with the University's Notice of Non-Discrimination. Eligibility is not determined based on race, sex, ethnicity, sexual orientation, or any other prohibited factor.
