Events for September
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AME Seminar
Wed, Sep 04, 2024 @ 03:30 PM - 04:30 PM
Aerospace and Mechanical Engineering
Conferences, Lectures, & Seminars
Speaker: Daniel Chung, University of Melbourne
Talk Title: Modeling drag and heat transfer on riblets and roughness
Abstract: 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.
Biography: Daniel 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.
More Info: https://ame.usc.edu/seminars/
Location: Seaver Science Library (SSL) - 202
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, Sep 11, 2024 @ 03:30 PM - 04:30 PM
Aerospace and Mechanical Engineering
Conferences, Lectures, & Seminars
Speaker: Jian Lin, University of Missouri
Talk Title: Laser Induced Graphene: 2D-to-3D Transformation
Abstract: 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.
Biography: Dr. 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. (https://scholar.google.com/citations?hl=en&user=N9QA8vEAAAAJ&view_op=list_works)
Host: AME Department
More Info: https://ame.usc.edu/seminars/
Webcast: https://usc.zoom.us/j/96060458816?pwd=8LmoG2q6vBCQubqqWpcizd2F1bxqsH.1Location: Seaver Science Library (SSL) - 202
WebCast Link: https://usc.zoom.us/j/96060458816?pwd=8LmoG2q6vBCQubqqWpcizd2F1bxqsH.1
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, Sep 18, 2024 @ 03:30 PM - 04:30 PM
Aerospace and Mechanical Engineering
Conferences, Lectures, & Seminars
Speaker: Agnimitra Dasgupta, USC
Talk Title: Solution of physics-constrained inverse problems using conditional diffusion models
Abstract: 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.
Biography: 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.
Host: AME Department
More Info: https://ame.usc.edu/seminars/
Webcast: https://usc.zoom.us/j/96060458816?pwd=8LmoG2q6vBCQubqqWpcizd2F1bxqsH.1Location: Seaver Science Library (SSL) - 202
WebCast Link: https://usc.zoom.us/j/96060458816?pwd=8LmoG2q6vBCQubqqWpcizd2F1bxqsH.1
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, Sep 25, 2024 @ 03:30 PM - 04:30 PM
Aerospace and Mechanical Engineering
Conferences, Lectures, & Seminars
Speaker: Oliver Schmidt, University of California at San Diego
Talk Title: Modal Decomposition for the Discovery of Nonlinear Flow Physics
Abstract: 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.
Biography: 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.
Host: AME Department
More Info: https://ame.usc.edu/seminars/
Webcast: https://usc.zoom.us/j/96060458816?pwd=8LmoG2q6vBCQubqqWpcizd2F1bxqsH.1Location: Seaver Science Library (SSL) - 202
WebCast Link: https://usc.zoom.us/j/96060458816?pwd=8LmoG2q6vBCQubqqWpcizd2F1bxqsH.1
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.
