Seminars

— Laufer Lecture —

The Finite Element Method and Isogeometric Analysis: Past, Present, Future

Thomas J.R. Hughes

John O. Hallquist Distinguished Chair in Computational Mechanics
Peter O'Donnell, Jr. Chair in Computational and Applied Mathematics
Lead Computational Mechanics Group
Professor Aerospace Engineering & Engineering Mechanics

Oden Institute for Computational Engineering and Sciences
University of Texas at Austin
Austin, TX

I will begin by probing into the past to discover the origins of the Finite Element Method (FEM), and then trace the evolution of those early developments to the present day in which the FEM is ubiquitous in science, engineering, mathematics, and medicine, and the most important discretization technology in Computational Mechanics.

However, despite its enormous success, there are still problems with contemporary technology, for example, building meshes from Computer Aided Design (CAD) representations is labor intensive, and a significant bottleneck in the design-through-analysis process; the introduction of geometry errors in computational models that arise due to feature removal, geometry clean-up and CAD “healing,” necessary to facilitate mesh generation; the inability of contemporary technology to “close the loop” with design optimization; and the failure of higher-order finite elements to achieve their full promise in industrial applications.

These issues are addressed by Isogeometric Analysis (IGA), the vision of which was first presented in a paper published October 1, 2005 [1]. Since then, IGA has become a focus of research within both FEM and CAD and is now a mainstream analysis methodology that has provided a new paradigm for computational model development [2-4]. The key concept utilized in the technical approach is the development of a new foundation for FEA, based on rich geometric descriptions originating in CAD, more tightly integrating design and analysis. Industrial applications and commercial software developments have expanded recently.

I will briefly present the motivation leading to IGA, its status, recent progress, areas of current activity, and what it offers for analysis model development and the design-through-analysis process. I will also argue that IGA provides an alternative and more robust approach to higher-order finite element analysis, filling the gap between low-order, geometrically versatile approaches and high-order, geometrically restrictive spectral methods. Finally, I will speculate on the future, the technologies that will prevail, computer developments, and the role of machine learning.

[1] T.J.R. Hughes, J.A. Cottrell and Y. Bazilevs, “Isogeometric Analysis: CAD, Finite Elements, NURBS, Exact Geometry and Mesh Refinement,” Computer Methods in Applied Mechanics and Engineering, 194, (2005) 4135-4195.

[2] J.A. Cottrell, T.J.R. Hughes and Y. Bazilevs, “Isogeometric Analysis: Toward Integration of CAD and FEA,” Wiley, Chichester, U.K., 2009.

[3] Special Issue on Isogeometric Analysis, (eds. T.J.R. Hughes, J.T. Oden and M. Papadrakakis), Computer Methods in Applied Mechanics and Engineering, 284, 1-1182, (1 February 2015).

[4] Special Issue on Isogeometric Analysis: Progress and Challenges, (eds. T.J.R. Hughes, J.T. Oden and M. Papadrakakis), Computer Methods in Applied Mechanics and Engineering, 316, 1-1270, (1 April 2017).

Thomas J.R. Hughes holds B.E. and M.E. degrees in Mechanical Engineering from Pratt Institute and an M.S. in Mathematics and Ph.D. in Engineering Science from the University of California at Berkeley. He taught at Berkeley, Caltech, and Stanford before joining the University of Texas at Austin. At Stanford he served as Chairman of the Division of Applied Mechanics, Chairman of the Department of Mechanical Engineering, Chairman of the Division of Mechanics and Computation, and held the Crary Chair of Engineering.

Dr. Hughes is an elected member of the U.S. National Academy of Sciences, the U.S. National Academy of Engineering, the American Academy of Arts and Sciences, the Royal Society of London, the Austrian Academy of Sciences (Section for Mathematics and the Physical Sciences), the Istituto Lombardo Accademia di Scienze e Lettere (Mathematics Section), and the Academy of Medicine, Engineering and Science of Texas. Dr. Hughes is a Fellow of the AAAS, AIAA, ASCE, ASME, the U.S. Association for Computational Mechanics (USACM), the International Association for Computational Mechanics (IACM), the American Academy of Mechanics (AAM), the Society for Industrial and Applied Mathematics (SIAM), and the Engineering Mechanics Institute of ASCE. Dr. Hughes is a Founder and past President of USACM and IACM, past Chairman of the Applied Mechanics Division of ASME, past Chairman of the US National Committee on Theoretical and Applied Mechanics, and co-editor emeritus of the international journal, Computer Methods in Applied Mechanics and Engineering. He is an Honorary Member of the Japanese Association for Computational Mechanics (JACM).

Dr. Hughes is one of the most widely cited authors in Engineering Science. He has been elected to Distinguished Member, ASCE’s highest honor, and has received ASME’s highest honor, the ASME Medal. He has also been awarded the Walter L. Huber Civil Engineering Research Prize and von Karman Medal from ASCE, the Timoshenko, Worcester Reed Warner, and Melville Medals from ASME, the von Neumann Medal from USACM, the Gauss-Newton Medal from IACM, the Computational Mechanics Award from the Japan Society of Mechanical Engineers (JSME), the Grand Prize from the Japan Society of Computational Engineering and Science (JSCES), the Computational Mechanics Award from JACM, the Humboldt Research Award for Senior Scientists from the Alexander von Humboldt Foundation, the Wilhem Exner Medal from the Austrian Association für SME (Öesterreichischer Gewerbeverein, OGV), the International Scientific Career Award from the Argentinian Association for Computational Mechanics (AMCA), the SIAM/ACM (Association for Computing Machinery) Prize in Computational Science and Engineering, the Southeastern Universities Research Association (SURA) Distinguished Scientist Award, the O.C. Zienkiewicz Medal from the Polish Association for Computational Mechanics (PACM), the A.C. Eringen Medal from the Society for Engineering Science (SES), the Ralph E. Kleinman Prize from SIAM, the Monie A. Ferst Award of Sigma Xi, The Scientific Research Honor Society, and the William Benter Prize in Applied Mathematics from the Liu Bie Ju Centre for Mathematical Sciences, City University of Hong Kong.

Wednesday, August 28, 2024

Location: The Franklin Suite
(Tutor Campus Center, 3rd Floor)

Lunch reception will begin at 12 noon

Zoom wil begin at 12:30 pm

The Zoom webinar is at
https://usc.zoom.us/j/94634476349?pwd=5I3aFQUoV4sLjbxKf6PhwhBbyDFcjZ.1

host: Oberai

Modeling Drag and Heat Transfer on Riblets and Roughness

Daniel Chung

Associate Professor
Department of Mechanical Engineering
The University of Melbourne
Melbourne, Australia

Riblets are a surface texture that reduce skin-friction drag in turbulent flow, and can now be found on in-service aircraft. Riblet features are smaller than the smallest vortices of turbulence. On the fuselage of a passenger aircraft, riblet spacing is about 100 microns. Riblet performance is notoriously sensitive to the fine details of their micro-structure, with optimal performance thought to require sharp tips, which are impossible to manufacture and maintain in practice. Thus, their successful application requires careful lifetime management of performance benefits, balanced against manufacturing, installation and maintenance costs. Key to this balancing act is our ability to accurately predict riblet performance given the inevitable micro-structure imperfections. To this end, I will discuss our group’s flow-physical modeling of the interaction between detailed riblet shapes and the near-wall vortices of turbulence; the outcome is a consistent improvement in accuracy of performance predictions across diverse riblet shapes.

Predicting rough-wall heat transfer has been a longstanding challenge, especially when new surface topographies are encountered. The heat-transfer coefficient of accreted ice on aircraft is different from that of engineered heat-exchanger surface textures. The best we can do are empirical correlations, which are not reliable. It is widely known that rough-wall heat transfer is not analogous to skin friction, i.e. not Reynolds analogy, but, then, what is it? With access now to the detailed temperature and flow fields near roughness features, I will show that heat transfer peaks at regions of the surface that are exposed to the oncoming flow, and, at these regions, a local version of Reynolds analogy survives. These insights allow us to develop a simple physics-based model of heat transfer that accounts for topography and working-fluid variations.

Daniel Chung is an associate professor in the Department of Mechanical Engineering at the University of Melbourne. He obtained his bachelor's degree in engineering and computer science from the University of Melbourne in 2003, and his PhD in aeronautics from Caltech in 2009. He was a postdoc at the Jet Propulsion Laboratory before joining the University of Melbourne in 2012. Daniel's research is in computational fluid mechanics, where he tries to distil turbulent flows into simplified problems and to build physics-based models for prediction. Recently, he has been interested in turbulent flow and thermal convection over rough surfaces, riblets and sea waves, including control. Daniel is currently on a sabbatical at USC until the end of November, hosted by Prof Mitul Luhar, and is keen to explore collaborations.

Wednesday, September 4, 2024

3:30 PM

Science & Engineering Library, Room 202 (SSL 202)

The Zoom webinar is at https://usc.zoom.us/j/96060458816?pwd=8LmoG2q6vBCQubqqWpcizd2F1bxqsH.1

host: Bermejo-Moreno, Luhar

Laser Induced Graphene: 2D-to-3D Transformation

Jian “Javen” Lin

Associate Professor of Mechanical and Aerospace Engineering and the William R. Kimel Faculty Fellow in Engineering
Mechanical and Aerospace Engineering Department
University of Missouri
Columbia, MO

Since disclosed in 2014, laser induced graphene (LIG) has been explored for applications in various fields, ranging from materials science, environment to sensor and electronics. Despite much progress, due to limitation of the technology advances, the reports are quite restricted to planar (2D) device fabrication capability. To tackle this challenge, in this talk, we will discuss new strides in advancing the capability from 2D to 3D to unlock LIG potential in multifunctional 3D devices. The first technological advance is to develop a 5-axis laser processing platform in 2023. With the two additional two degrees of freedom, the laser beam can be focused on any arbitrary surfaces so that freeform laser induction (FLI) of representative LIG, metals, and metal oxides as high-performance sensing and electrode materials for 3D conformable electronics was realized. Based on this success, in 2024, we made a new progress in developing a freeform multimaterial assembly platform (FMAP) by integrating 3D printing (fused filament fabrication (FFM), direct ink writing (DIW)) with the FLI technique. 3D printing performs the 3D polymer material assembly, while the FLI in-situ synthesizes functional materials (LIG, metals, and semiconductors) on or within any predesigned locations of the 3D structures by synergistical, programmed control system actuation. By this robotic fabrication platform, a crossbar LED circuit, touchpad for human-machine interactions, multiple sensors, sensor-enveloped springs, 3D micro electromagnets, force feedback manipulators, and microfluidic reactors with embedded heating elements were demonstrated to show versatility and effectiveness of the methodology. Finally, we will discuss how artificial intelligence, generative models can be applied to such a robotic system to push it toward a fully autonomous fabrication system.

References: Nat. Commun., 5, 5714, 2014; Adv. Funct. Mater. 33 (1), 2210084, 2023; Nat. Commun., 15 (1), 4541, 2024.

Jian “Javen” Lin is an Associate Professor of Mechanical and Aerospace Engineering and the William R. Kimel Faculty Fellow in Engineering at University of Missouri (MU), where he was an Assistant Professor from 2014 to 2020. Prior to MU, he was a postdoctoral research associate in the Department of Mechanical Engineering & Materials Science at Rice University under guidance of Dr. James M. Tour from 2011 to 2014. He got his B.S. in Mechanical and Automation Engineering from Zhejiang University in 2007. He then studied at University of California-Riverside and received his M.S. in Electrical Engineering and Ph.D. in Mechanical Engineering in 2010 and 2011, respectively. Dr. Lin was awarded the ORAU Ralph E. Powe Junior Faculty Enhancement Award In 2015, received an Emerging Young Investigator award from Journal of Material Chemistry in 2016 and Sony Faculty Innovation Award in 2020. Since 2019, he has been continuously listed in Top 2% Scientists in the World by Stanford Advanced Study Institute. Dr. Lin’s research group dedicates research in materials and advanced manufacturing to promote biomedical, energy, and robotics fields. His research lies in two main clusters: 1) autonomous manufacturing powered by artificial intelligence and robotics; 2) 3D/4D printing. He has published ~ 120 journal papers and 6 issued patents with Google Scholar citations of > 13,000.

Wednesday, September 11, 2024

3:30 PM

Science & Engineering Library, Room 202 (SSL 202)

The Zoom webinar is at https://usc.zoom.us/j/96060458816?pwd=8LmoG2q6vBCQubqqWpcizd2F1bxqsH.1

host: Chen

Solution of Physics-Constrained Inverse Problems Using Conditional Diffusion Models

Agnimitra Dasgupta

Postdoctoral Researcher
Department of Aerospace and Mechanical Engineering
University of Southern California
Los Angeles, CA

Inverse problems involve deducing the cause from observed effects and are ubiquitous across several science and engineering disciplines. Generally ill-posed, an inverse problem often has multiple solutions. The Bayesian paradigm remains popular for the statistical treatment of inverse problems because it is useful for characterizing the relative plausibility of different solutions. However, Bayesian inference is computationally intractable in most practical scenarios. Some recurring challenges include summarizing available data into informative priors, sampling high-dimensional posteriors, and the need for multiple evaluations of a compute-intensive numerical model, likely black-box and mis-specified, for the forward physics. This talk will introduce conditional score-based diffusion models for solving inverse elasticity problems. A conditional score-based diffusion model uses a neural network to approximate the target posterior distribution’s ‘score function’, defined as the gradient of the logarithm of the density. Subsequently, Langevin dynamics enables the generation of new realizations from the target posterior. Training the diffusion model requires a supervised dataset, and forward model simulations can easily construct it. Therefore, the proposed approach is simulation-based and likelihood-free, and there is no need for gradient computations through the forward physics model. Moreover, the diffusion model is reusable for different measurement instances, unlike conventional MCMC-based inference, which amortizes the cost of inference. This talk will demonstrate the efficacy of conditional score-based diffusion model-driven inference on several physics-constrained inverse problems, primarily concerning inverse elasticity problems, that involve synthetic and real experimental data.

Agnimitra Dasgupta is a Postdoctoral Research Associate in the Aerospace and Mechanical Engineering Department at the University of Southern California (USC). He obtained his Ph.D. in Civil Engineering from USC and a Master's in Civil Engineering from the Indian Institute of Science. Agnimitra's research interest lies at the intersection of uncertainty quantification and scientific machine learning with applications ranging from the health to infrastructure sectors. Agnimitra received the Provost’s Fellowship from USC between 2017 and 2021.

Wednesday, September 18, 2024

3:30 PM

Science & Engineering Library, Room 202 (SSL 202)

The Zoom webinar is at https://usc.zoom.us/j/96060458816?pwd=8LmoG2q6vBCQubqqWpcizd2F1bxqsH.1

host: Oberai

Modal Decomposition for the Discovery of Nonlinear Flow Physics

Oliver Schmidt

Associate Professor
Department of Mechanical and Aerospace Engineering
University of California at San Diego
La Jolla, CA

Modal decomposition techniques are at the forefront of uncovering nonlinear flow physics from large experimental and numerical datasets, particularly in complex engineering and natural flows. Among the most prominent of these techniques are Proper Orthogonal Decomposition (POD) and Dynamic Mode Decomposition (DMD), which extract the energetically and dynamically most relevant flow features, respectively. While both methods yield accurate low-dimensional representations of flow dynamics, neither provides direct, quantitative insight into the nonlinear interactions that govern these dynamics. The common approach remains to rely on power or cross-spectral peaks as heuristic indicators of nonlinear interactions.

In this talk, I will present a novel orthogonal triadic decomposition technique that systematically identifies and quantifies nonlinear flow phenomena. By extracting flow structures linked to triadic nonlinear interactions—the core mechanism of energy transfer in turbulence—this method offers a powerful new tool for physical discovery. I will demonstrate its application in two examples: cylinder flow, a canonical flow example, and large-eddy simulation data of a plasma-actuated twin rectangular jet, a complex engineering flow. These cases illustrate how this decomposition technique not only improves our understanding of nonlinear interactions but also lays the groundwork for future reduced-order models of complex flows.

Oliver Schmidt is an Associate Professor in the Department of Mechanical and Aerospace Engineering at UC San Diego's Jacobs School of Engineering and a recipient of the NSF CAREER award. Prior to joining UC San Diego, he was a Postdoctoral Scholar in Mechanical and Civil Engineering at the California Institute of Technology. He earned his Ph.D. in Aeronautical Engineering from the University of Stuttgart in 2014. His research centers on physics-based modeling and computational fluid dynamics, with applications spanning aerospace sciences, high-energy laser systems, and physical oceanography. His work is supported by the AFOSR, ONR, DOE, and NSF.

Wednesday, September 25, 2024

3:30 PM

Science & Engineering Library, Room 202 (SSL 202)

The Zoom webinar is at https://usc.zoom.us/j/96060458816?pwd=8LmoG2q6vBCQubqqWpcizd2F1bxqsH.1

host: Bermejo-Moreno

Lighting the Fuse to Enable Metamaterials for Passive, Adaptive Flow Control

Andres Goza

Assistant Professor
Department of Aerospace Engineering
University of Illinois at Urbana-Champagne
Urbana, IL

Unsteady flow control is challenging in many engineering domains. Active techniques are costly, energy-intensive, and heavy, while passive approaches often lack robustness in handling complex flow dynamics. Metamaterials are structures with engineered architecture, allowing for catered response behaviors to stimuli. These structures offer a transformative potential for flow control by flow-metamaterial interaction, FMI. FMI could allow engineers to leverage architected structures to passively and adaptively produce desired flow responses.

To capitalize on this potential, however, we must first identify which classes of metamaterials are most promising for different flow scenarios, and understand how to align the key metamaterial behaviors with the relevant flow length- and timescales to enable favorable flow-structure interplay. This understanding must account for the behavior of the fully coupled flow-metamaterial system, which will generally yield dynamics with distinct time/length scales from those of the constituent flow/structure systems. Obtaining this understanding requires a suite of computational tools capable of predicting and understanding the flow-structure interplay between the targeted complex flows and modern architected structures.

We present some a-la-carte results on these various challenges and opportunities. We discuss some key metamaterial classes promising for certain flow behaviors. We share some ongoing development of high-fidelity and resolvent computational tools within an immersed boundary framework, currently without flow-structure interplay but being designed to enable robust, versatile computations between flows and a wide range of metamaterials. Finally, for simplified flow-metamaterial configurations, we discuss efforts to synthesize appropriate dimensionless parameters, expressed in terms of key intrinsic properties of the separate flow/structure systems, that govern the FMI system's behavior.

*Andres is grateful for funding from AFOSR to perform the presented work.

Andres is an Assistant Professor at UIUC. He uses computational techniques to study flow-structure interaction, particularly when the structure has some heterogeneous properties that make the coupled behavior more complex. He is interested in developing high-fidelity and analysis techniques to simulate and understand these dynamics. He also has two young children that bring fun regular surprises, and enjoys running, cycling, squash, and bouldering.

Wednesday, October 2, 2024

3:30 PM

Science & Engineering Library, Room 202 (SSL 202)

The Zoom webinar is at https://usc.zoom.us/j/96060458816?pwd=8LmoG2q6vBCQubqqWpcizd2F1bxqsH.1

host: Luhar

Printed 3D Microelectronics: Process Development, Materials Science, and Devices Applications

Rahul Panat

Professor
Mechanical Engineering Department
Carnegie Mellon University
Pittsburgh, PA

In this research, we develop a printed microelectronics technique based on droplet-based nanoparticle printing using the Aerosol Jet (AJ) technology. A balance between inertia forces and surface forces for the microdroplets (each containing nanoparticles), along with rapid solvent evaporation are used to create highly complex 3D microarchitectures of metals and polymers without auxiliary support and with near-fully dense truss members. Highly intricate 3-D micro-lattices, pillars, interconnects, and spirals are demonstrated.

We then use these structures to: (i) study fundamental material science, and (ii) demonstrate device applications with extraordinary performance that cannot be achieved by any other method. For (i), a temperature-gradient-driven mass transport is shown as a new mechanism of 4D printing. For (ii), novel 3D geometry of electrodes enables detection of pathogen antibodies and antigens in 10-12 seconds at femtomolar sensitivities - the fastest detection of disease biomarkers yet reported! This technology is validated through human trials. In addition, the 3D microarchitectures in our lab enable fully customizable brain-computer interfaces (BCIs) that record electrical signals between neurons at densities of thousands of electrodes/cm², which is 5-10× the current state-of-the-art BCI technologies. The technology was validated through animal testing via recording of the action potentials from the mouse brain. We also demonstrated the printing of high-capacity Li-ion batteries and thin flexible robotic skins with embedded sensors. Lastly, our ongoing work on creating manufacturing digital twins of the AJ printing process is also discussed.

Rahul Panat is Professor of Mechanical Engineering at Carnegie Mellon University (CMU). He is courtesy faculty in the Materials Science and Engineering and the Robotics Institute at CMU. He is also the Associate Director of Research at the Manufacturing Futures Institute at CMU, which is focused on bringing the latest advances in digital technologies to advanced manufacturing.

Prof. Panat completed his PhD in Theoretical and Applied Mechanics from the University of Illinois at Urbana in 2004. He joined Intel Corporation’s R&D unit in Chandler, AZ, where he worked for 10 years on microprocessor materials and manufacturing R&D - specifically on 3D heterogeneous integration. At Intel, Dr. Panat led a team of engineers that developed the fabrication process for world’s first halogen-free IC chip. He was part of a team that introduced the first Si chip with a billion transistors. He returned to academia in 2014 and joined CMU in fall 2017. His research is focused on microscale 3D printing and its applications to biomedical engineering, stretchable electronics, and Li-ion batteries. He has obtained > $7.5 million in research funding from US Intelligence agencies, US Air Force, US Army, ARPA-H, National Institutes of Health (NIH), Department of Energy (DOE), National Science Foundation (NSF), and industry. Prof. Panat is recipient of several awards, including MRS gold medal, Mavis Memorial Award, an award at Intel for his work on the halogen-free chip, Struminger Teaching Fellowship, and the Russell V. Trader chair professorship at CMU.

Wednesday, October 9, 2024

3:30 PM

Science & Engineering Library, Room 202 (SSL 202)

The Zoom webinar is at https://usc.zoom.us/j/96060458816?pwd=8LmoG2q6vBCQubqqWpcizd2F1bxqsH.1

host: Zhao

Phase Transformations in Multifunctional Materials

Ananya Renuka Balakrishna

Assistant Professor
Mechanical Engineering Department
University of California at Santa Barbara
Santa Barbara, CA

Phase transformation materials are characterized by their ability to rapidly and reversibly switch between distinct properties, such as insulating and conducting, paramagnetic and ferromagnetic, or Li-rich and Li-poor. These transformations, however, are accompanied by abrupt structural changes in the crystal lattices, which can nucleate defects, accumulate strain energy, and accelerate material decay. We investigate these transformations in multifunctional materials from the viewpoint of Ericksen’s multiple energy wells. By doing so, we identify important links between material constants, crystallographic microstructures, and macroscopic properties. This approach to understanding material behavior from the perspective of energy landscapes may suggest new ways to design materials with improved properties and lifespans. In this talk, I will present our findings on phase transformations in battery electrodes (intercalation compounds), photomechanical materials (molecular crystals), and soft magnetic alloys. Most of this work has primarily been conducted by Delin Zhang (PhD candidate at USC/AME) and Devesh Tiwari (MS from USC/AME).

Ananya Renuka Balakrishna is an Assistant Professor in the Materials Department at the University of California Santa Barbara. She received her B.Tech degree in Mechanical Engineering from the National Institute of Technology Karnataka and her Ph.D. in Solid Mechanics and Materials Engineering from the University of Oxford. Before her current appointment, she was a Lindemann Postdoctoral Fellow at MIT and the University of Minnesota and joined the faculty in the Department of Aerospace & Mechanical Engineering at the University of Southern California in 2020. Her research group develops theoretical models to understand the interplay between fundamental material constants and microstructural instabilities, and how they collectively shape the physical response of a material.

Wednesday, October 16, 2024

3:30 PM

Science & Engineering Library, Room 202 (SSL 202)

The Zoom webinar is at https://usc.zoom.us/j/96060458816?pwd=8LmoG2q6vBCQubqqWpcizd2F1bxqsH.1

host: Zhao

Physics-Based AI-Assisted Design and Control of Manufacturing Processes

Jian Cao

Cardiss Collins Professor, Department of Mechanical Engineering
(by courtesy) Department of Materials Science and Engineering
(by courtesy) Department of Civil and Environmental Engineering
Director, Northwestern Initiative on Manufacturing Science and Innovation
Associate Vice President for Research
Norhwestern University
Evanston, IL

Current research efforts at my manufacturing group aim to advance the capability to co-design materials and manufacturing processes using hybrid physics-based and data-driven approaches. In this talk, I will demonstrate our work in the development of differentiable simulation tools, sensing, and process control to achieve effective and efficient predictions and control of a material’s mechanical behavior in metal additive manufacturing processes. Furthermore, I will show how we use machine learning to accelerate the physics-based simulations and to realize active sensing with the goal of effective in-situ local process control. Our solutions particularly target three notoriously challenging aspects of the process: long history-dependent properties, complex geometric features, and the high dimensionality of their design space. The approaches are applicable to other manufacturing processes as well, such as flexible incremental forming.

Cardiss Collins Professor Jian Cao (MIT’Ph.D, MIT’MS, SJTU’BS) specialized in innovative manufacturing processes and systems, particularly in the areas of deformation-based processes and laser additive manufacturing processes. She is the Founding Director of the research center on Manufacturing Science and Innovation at Northwestern, known as NIMSI.

Prof. Cao is an elected member of the National Academy of Engineering (NAE) and of the American Academy of Arts and Sciences (AAA&S). She is a Fellow of American Association for the Advancement of Science (AAAS), ASME, the International Academy for Production Engineering (CIRP) and SME. Her major awards include DoD Vannevar Bush Faculty Fellowship, ASME Ted Belytschko Applied Mechanics Award, the inaugural ASME Devor-Kapoor Manufacturing Medal, Hideo Hanafusa Outstanding Investigator Award for Flexible Automation, ASME Milton C. Shaw Manufacturing Research Medal, Charles Russ Richards Memorial Award from ASME and Pi Tau Sigma, SME Gold Medal, and SME Frederick W. Taylor Research Medal. Cao was the Editor-in-Chief of Journal of Materials Processing Technology.

Prof. Cao now serves as an Associate Vice President for Research at Northwestern, a member of the National Materials and Manufacturing Board of the National Academies, a member of the Defense Materials, Manufacturing and its Infrastructure (DMMI) Standing Committee of the National Academies, Board of Directors of SME, and Board of mHUB – accelerator for hardtech innovation and manufacturing in Chicago.

Wednesday, October 23, 2024

3:30 PM

Science & Engineering Library, Room 202 (SSL 202)

The Zoom webinar is at https://usc.zoom.us/j/96060458816?pwd=8LmoG2q6vBCQubqqWpcizd2F1bxqsH.1

host: Gupta, Zhao

Composable Optimization for Robotic Motion Planning and Control

Zac Manchester

Assistant Professor
The Robotics Institute
Carnegie Mellon University
Pittsburg, PA

Contact interactions are pervasive in key real-world robotic tasks like manipulation and walking. However, the non-smooth dynamics associated with impacts and friction remain challenging to model, and motion planning and control algorithms that can fluently and efficiently reason about contact remain elusive. In this talk, I will share recent work from my research group that takes an “optimization-first” approach to these challenges: collision detection, physics, motion planning, state estimation, and control are all posed as constrained optimization problems. We then build a set of algorithmic and numerical tools that allow us to flexibly compose these optimization sub-problems to solve complex robotics problems involving discontinuous, unplanned, and uncertain contact mechanics.

Zac Manchester is an Assistant Professor of Robotics at Carnegie Mellon University. He holds a Ph.D. in aerospace engineering and a B.S. in applied physics from Cornell University. Zac was a postdoc in the Agile Robotics Lab at Harvard University and previously worked at Stanford, NASA Ames Research Center and Analytical Graphics, Inc. He received a NASA Early Career Faculty Award in 2018 and has led four satellite missions. His research interests include motion planning, control, and numerical optimization, particularly with application to robotic locomotion and spacecraft guidance, navigation, and control.

Wednesday, November 6, 2024

3:30 PM

Science & Engineering Library, Room 202 (SSL 202)

The Zoom webinar is at https://usc.zoom.us/j/96060458816?pwd=8LmoG2q6vBCQubqqWpcizd2F1bxqsH.1

host: Nguyen

Mechanism-Based Mechanical Metamaterials

Robert V. Kohn

Professor Emeritus
Courant Institute of Mathematical Sciences
New York University
New York, NY

The design and analysis of mechanism-based mechanical metamaterials is a relatively new and rapidly growing research area. It studies artificial "materials" that take advantage of "mechanisms" (that is, nontrivial energy-free deformations) to achieve interesting macroscopic behavior. The relevant mechanics is nonlinear, since mechanisms involve large rotations. While there have been insightful studies of specific examples, some fundamental issues remain poorly understood. This talk will address two of them, namely (a) how to analyze a metamaterial's macroscopic behavior, and (b) whether linear elastic calculations can still be of use in the analysis of such systems, despite the fact that their mechanisms involve large rotations? My talk will start with a broad introduction to this area; then I'll discuss some recent work with Xuenan Li, which focuses on a particular (very rich) example — the Kagome metamaterial. This system is interesting because it has infinitely many mechanisms, yet it behaves macroscopically as a nonlinear elastic material whose stress-free states are compressive conformal maps.

Robert V. Kohn is Professor Emeritus of Mathematics at New York University's Courant Institute of Mathematical Sciences. He received his PhD in Mathematics from Princeton in 1979, then held a two-year NSF Postdoctoral Fellowship which took him to the Courant Institute. He joined the faculty of the Courant Institute 1981, becoming Full Professor in 1988 and Silver Professor in 2017 before choosing to retire in 2022. Much of his work has addressed problems from mechanics and physics using methods from the calculus of variations and partial differential equations. He has, in particular, studied many examples of energy-driven pattern formation, in diverse systems ranging from shape-memory materials to thin elastic sheets. Professor Kohn's recognitions include selection as a member of the American Academy of Arts and Sciences, receipt of the American Mathematical Society's 2014 Leroy P. Steele Award, and being selected as both a SIAM Fellow and a Fellow of the American Mathematical Society.

Wednesday, November 13, 2024

3:30 PM

Science & Engineering Library, Room 202 (SSL 202)

The Zoom webinar is at https://usc.zoom.us/j/96060458816?pwd=8LmoG2q6vBCQubqqWpcizd2F1bxqsH.1

host: Newton, Plucinsky

The Art and the Science of Metal 3D Printing

Adrian J. Lew

Professor
Department of Mechanical Engineering
and
Institute for Computational and Mathematical Engineering
Stanford University
Stanford, CA

This is the title of a class I teach at Stanford on metal 3D printing, and it reflects my perspective on where metal 3D printing is today: part art and part science, because of the complexities and multiple physical processes at play. Printing strategies are inspired in science, but when it comes time to print a new alloy or a complex geometry, the art storms in to help bridge the gaps in understanding. A goal in metal 3D printing research is to shift this balance towards science.

In this talk I will first describe the main physical processes involved one of the most widely adopted metal 3D printing technologies, Laser Powder Bed Fusion (LPBF), and then showcase three vignettes of contributions we made: (a) in-situ alloying and printing of tantalum-tungsten alloys, (b) the “surprising” behavior of some martensitic steels under 3D printing conditions, (c) two ways to alter the optical absorptivity of highly-reflective metallic powders to facilitate printing of copper in some standard printers. The art and the science are interweaved in the three contributions.

Adrian J. Lew is a Professor of Mechanical Engineering and the Institute for Computational and Mathematical Engineering at Stanford University. He graduated with the degree of Nuclear Engineer from the Instituto Balseiro in Argentina, and received his master of science and doctoral degrees in Aeronautics from the California Institute of Technology. He is a fellow of the International Association for Computational Mechanics, and has been awarded Young Investigator Award by the International Association for Computational Mechanics, the ONR Young Investigator Award, the NSF Career Award, and the Ferdinand P. Beer & Russel Johnston, Jr., Outstanding New Mechanics Educator Award from the American Society of Engineering Education. He has also received an honorable mention by the Federal Communication Commission for the creation of the Virtual Braille Keyboard. He was the first USACM Technical Thrust Area Lead for Manufacturing, and still serves it as a member. He is currently member of the Technical Advisory Board for Velo 3D, a metal 3D printing start-up located in Campbell, CA, and consultant to other metal 3D printing companies.

Wednesday, November 20, 2024

3:30 PM

Science & Engineering Library, Room 202 (SSL 202)

The Zoom webinar is at https://usc.zoom.us/j/96060458816?pwd=8LmoG2q6vBCQubqqWpcizd2F1bxqsH.1

hosts: Plucinsky, Pantano-Rubino

Shaping up to Explore and Exploit Unsteady Fluid-structure Interactions

Karen Mulleners

Associate Professor
Institute of Mechanical Engineering
École Polytechnique Fédérale de Lausanne (EPFL)
Lausanne, Switzerland

Nature is full of thin, flexible objects that bend, flutter, or flap in the wind or the water such as leaves of trees and bushes, insect wings, and fish fins. A remarkable feature that is common to these objects is their ability to deform when interacting with the air or the water in a way that benefits them. Leaves of trees bend in the wind to reduce their resistance and the loads on their stems. The flexibility of insect wings and fish fins can reduce the effort the animals need to stay aloft or to propel themselves and increases their performance and agility. Leaves, insect wings, and fish fins come in a myriad of different shapes and sizes. Surprisingly, the influence of the shape of thin flexible objects on their fluid structure interactions has not yet received much attention. In our lab, we design unsteady fluid-structure interaction experiments to close that gap and fundamentally study how the shape of flexible structures and their ability to reconfigure changes their fluid dynamic performance and resilience in dynamic fluid environments. I will present recent work including experimental investigations of the fluid-structure interactions of deformable flapping wings, reconfiguring disks, and flapping flags.

Karen Mulleners is an associate professor in the institute of mechanical engineering in the school of engineering at EPFL. She is the head of the unsteady flow diagnostics laboratory (UNFoLD). She is an experimental fluid dynamicist who focuses on unfolding the origin and development of unsteady flow separation and vortex formation. Karen studied physics in Belgium (Hasselt University, previously Limburgs Universitair Centrum) and the Netherlands (TU Eindhoven). She received her PhD in mechanical engineering from the Leibniz Universität Hannover in Germany in 2010 for her work on dynamic stall on pitching airfoils that she conducted as a member of the German aerospace centre (DLR) in Göttingen. Before joining EPFL in 2016, Karen was a (non-tenure track) assistant professor at the Leibniz Universität Hannover in Germany.

Thursday, Novermber 21, 2024

10:00 AM

Laufer Conference Room (OHE 406)

host: Spedding

Lifting Surfaces at Aerodynamically low Reynolds numbers: Recent Advances

Serhiy Yarusevych

Professor
Department of Mechanical and Mechatronics Engineering
University of Waterloo
Waterloo, ON, Canada

Flow development over lifting surfaces in aerodynamically low Reynolds number flows (Re<500,000) is largely governed by boundary layer separation and subsequent separated sear layer development on the suction side. In the time-averaged sense, rapid laminar-to-turbulent transition in the separated shear layer leads to the formation of a closed recirculating flow region referred to as the Laminar Separation Bubble (LSB). However, LSBs feature rich dynamics associated with the formation and evolution of shear layer roll up vortices leading to laminar-to-turbulent transition. Linear stability analysis confirms that there is a continuous stability spectrum spanning laminar boundary later and separated shear layer regions, linking LSB transition and shear layer vortex shedding to upstream amplification of disturbances that originate from free-stream perturbations in the receptivity region. Flow development in the aft portion of the bubble is highly three-dimensional even on nominally two-dimensional geometries. It manifests in progressive deformation of shear layer vortices and subsequent vortex breakdown. On a finite wing, an open LSB forms due to wing tip and root effects. Away from the affected regions, however, LSB topology and dynamics appear to be quasi two-dimensional despite effective angle of attack variation across the span. Changes in operating conditions, including velocity and angle of attack, can lead to significant transient flow developments associated with bubble bursting (i.e., sudden lengthening or full separation without subsequent reattachment) and LSB re-formation, accompanied by substantial changes in aerodynamic loads.

Serhiy Yarusevych is a full professor in the Department of Mechanical and Mechatronics Engineering at the University of Waterloo, Canada. He is directing the Fluid Mechanics Research Laboratory focused on multidisciplinary applications of fluid mechanics in engineering and science, including operation of lifting surfaces at low Reynolds numbers, flows over bluff bodies, free shear flows, flow induced vibrations and noise, and flow control. The associated research involves a combination of experimental, analytical, and numerical tools, with the main emphasis placed on experiments involving particle image velocimetry. His research in Canada was interposed by sabbatical leaves at TU Delft and the University of Bundeswehr Munich, in 2013-2014 and 2019-2020, respectively, involving collaborative research with advanced flow diagnostic tools and volumetric measurements. Dr. Yarusevych is an Alexander von Humboldt Fellow, Mercator Fellow, and Associate Fellow of AIAA. Since 2018, Dr. Yarusevych has been serving as an Editor-in-Chief of Experimental Thermal and Fluid Science, Elsevier.

Friday, November 22, 2024

10:00 AM

Laufer Conference Room (OHE 406)

host: Spedding