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SUMMARY:MICDE/ME Seminar: Krishnan Mahesh\, Professor\, University of Michigan NAME
DESCRIPTION:Bio:  Krishnan Mahesh is a Richard B. Couch Professor of Naval Architecture and Marine Engineering at the University of Michigan. His research focuses on the simulation of complex\, multi-physics turbulent flows. Mahesh received his Bachelor’s degree in Mechanical Engineering from the Indian Institute of Technology (Mumbai)\, and in 1996 obtained his Ph.D. degree in Mechanical Engineering from Stanford University. He is a 2018 Fulbright-Nehru Specialist\, a Fellow of the American Physical Society\, an Associate Fellow of the American Institute of Aeronautics and Astronautics\, and a Fellow of the Minnesota Supercomputing Institute. Mahesh is a recipient of the CAREER Award from the National Science Foundation and the Francois N. Frenkiel Award from the American Physical Society. He has received the Taylor Award for Distinguished Research\, McKnight Presidential Fellowship\, Guillermo E. Borja Award\, and McKnight Land-Grant Professorship from the University of Minnesota. \nLarge Eddy Simulation of Turbulent Cavitating Flows\nCavitation is a complex multi-scale phenomenon that has implications from intense sound production to erosion in engineering applications. This talk will discuss our efforts at developing the large-eddy simulation capability for the simulation of turbulent cavitating flows. LES of cavitation is challenged by phase change modeling\, acoustic stiffness\, sharp multiphase fronts\, strong compressibility effects\, consistent accounting of nuclei\, broadband turbulence and subgrid effects. \nLES of partial cavitation will be discussed under the same conditions as experiments in a sharp wedge configuration.  Physical mechanisms of cavity transition observed in the experiments\, i.e.\, re-entrant jet and bubbly shock waves\, are both captured in the LES over their respective regimes. Vapor volume fraction data obtained from the LES will be quantitatively compared to X-ray densitometry\, and the results will be discussed. Cavitation nuclei are likely to be introduced through the free-stream as well as at solid surfaces. We will present a novel approach based on Gibbs free energy minimization to predict nuclei concentrations. The results from the proposed work will be applied to account for dissolved gas content in CSM measurements and predict several decades of experimentally observed trends in nuclei concentrations. Cavitating flows possess a range of vapor length scales ranging from tiny vapor bubbles to large vapor pockets. We will discuss a compressible hybrid model to capture both sub-grid vapor nuclei and massive sheet cavity dynamics. Finally\, physical aspects of inception due to the interaction of a counter–rotating vortex pair generated behind a pair of hydrofoils will be presented. \n\n  \nThe MICDE Fall 2025 Seminar Series is open to all. University of Michigan faculty and students. \nThis is an in-person event. \nGraduate Certificate in Computational Discovery and Engineering\, please use this form to record your attendance. \nQuestions? Email MICDE-events@umich.edu
URL:https://micde.umich.edu/event/micde-me-seminar-krishnan-mahesh-professor-university-of-michigan-name/
LOCATION:2150 H.H. Dow\, 2300 Hayward St\, Ann Arbor\, 48109\, United States
CATEGORIES:Computational Science,Engineering,Featured Events,Free,Mechanical Engineering,Micde Seminar,MICDE Seminar Series,Michigan Engineering,Naval Architecture and Marine Engineering
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DTSTART;TZID=America/Detroit:20231101T150000
DTEND;TZID=America/Detroit:20231101T160000
DTSTAMP:20231112T191452Z
CREATED:20230913T004822Z
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SUMMARY:MICDE / NERS Seminar:  Larry Aagesen\, Computational Scientist at Idaho National Laboratory
DESCRIPTION:Bio: Dr. Larry Aagesen is a Computational Scientist at Idaho National Laboratory (INL)\, and is the leader of the Computational Microstructure Science group there. He is a member of the development team for Marmot\, INL’s application for simulating microstructural evolution in nuclear fuels and reactor structural materials\, which is based on MOOSE\, INL’s framework for solving partial differential equations using the finite element method. His primary area of expertise is in the phase-field method\, having developed phase-field models for a variety of physical phenomena\, including fission gas bubble evolution\, solid-state precipitation\, solidification and coarsening in metallic alloys and ceramics\, and semiconductor growth. He received his undergraduate degree in Physics at the University of California\, Berkeley in 1997\, followed by service in the U. S. Navy’s nuclear propulsion program and work in industry. He then returned to graduate school\, completing his Ph.D. in Materials Science and Engineering at Northwestern University in 2010. This was followed by appointment as a postdoctoral researcher and Assistant Research Scientist in the Department of Materials Science and Engineering at the University of Michigan from 2010 to 2015\, after which he joined INL. \nMulti-scale modeling of the evolution of structure and properties in materials for nuclear energy applications\nNuclear energy is an important component of an overall strategy to address climate change. Idaho National Laboratory (INL) is the U.S. Department of Energy’s primary facility for research and development in nuclear science and technology for energy generation\, supporting the improvement and life extension of the existing reactor fleet and the development and licensing of new reactor designs. Computational modeling is an important component of these activities\, particularly in the area of materials for nuclear applications\, where experimental data can be very challenging and expensive to acquire\, and where data is especially scarce for new reactor designs. INL has used multi-scale modeling – linking atomistic\, mesoscale\, and engineering scales – to improve the ability to predict the performance of materials for nuclear energy applications. These modeling efforts make extensive of MOOSE (Multiphysics Object-Oriented Simulation Environment)\, a general-purpose open source finite element framework developed at INL. In this talk\, I will give an overview of the approach and tools used\, and several examples of application\, including performance of nuclear fuels\, understanding radiation-driven formation of nanoscale void and gas bubble superlattices\, and powder densification through electric field assisted sintering. \n  \n\n  \nThe MICDE Fall 2023 Seminar Series is open to all. University of Michigan faculty and students interested in predicting and explaining the properties of materials using computer simulation are encouraged to attend. \nThis seminar is cohosted by the Michigan Institute for Computational Discovery & Engineering (MICDE) and the Department of Nuclear Engineering and Radiological Sciences\, (NERS). Dr. Aagesen will be hosted by Dr. Kevin Field\, Associate Professor of Nuclear Engineering. \nThis is an in-person event. \nGraduate Certificate in Computational Discovery and Engineering\, and MICDE fellows\, please use this form to record your attendance. \n 
URL:https://micde.umich.edu/event/larry-aagesen-computational-scientist-idaho-national-laboratory-inl/
LOCATION:2150 H.H. Dow\, 2300 Hayward St\, Ann Arbor\, 48109\, United States
CATEGORIES:Featured Events,Micde Seminar,MICDE Seminar Series
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DTSTART;TZID=America/Detroit:20171211T100000
DTEND;TZID=America/Detroit:20171211T110000
DTSTAMP:20230905T171416Z
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SUMMARY:ME Faculty Candidate Seminar Series:  Xiu Yang\, Computational Mathematics Scientist\, Pacific Northwest National Laboratory
DESCRIPTION:Bio: Dr. Xiu Yang received his B.S. and M.Sc. in computational mathematics from Peking University\, Beijing\, China and Ph.D. in applied mathematics from Brown University\, Providence\, RI. He is currently a research scientist in computational mathematics group at Pacific Northwest National Laboratory\, Richland\, WA. His research interests include uncertainty quantification\, multi-scale modeling\, multi-fidelity data fusion and inverse problem.\n  \n Uncertainty Quantification for Complex Systems Using Limited Data\nRealistic analysis and design of complex engineering systems require not only a fine understanding of the underlying physics\, but also a significant recognition of uncertainties and their influences on the quantities of interest. Intrinsic variabilities and lack of knowledge about system parameters or governing physical models often considerably affect quantities of interest and decision-making processes. For complex systems\, the available data for quantifying uncertainties or analyzing sensitivities are usually limited because the cost of conducting a large number of experiments or running many large-scale simulations can be prohibitive. Efficient approaches of representing uncertainties using limited data are critical for such problems. I will talk about three methods for uncertainty quantification by constructing surrogate model of the quantity of interest. The first method is the adaptive functional ANOVA method\, which constructs the surrogate model hierarchically by analyzing the sensitivities of individual parameters. The second method is the sparse regression based on identification of low-dimensional structure\, which exploits low-dimensional structures in the parameter space and solves an optimization problem to construct the surrogate model. The third one is the multi-fidelity information fusion via Gaussian process regression\, which integrates limited high-fidelity data with a large number of low-fidelity data. I will demonstrate the efficiency of these methods in applications including perturbation of drag and lift in aerodynamics\, solvation energy computing in chemical biology\, stability analysis of power grid system and optimizing Li-O2 battery design. \n  \n\nThis is a talk of potential interest to the MICDE community. The speakers in this seminar series are Faculty Candidates in the department of Mechanical Engineering for a Computational Science search that is being carried out with the active engagement of MICDE. We expect that the successful candidate will be a highly engaged affiliate of MICDE.
URL:https://micde.umich.edu/event/me-faculty-candidate-seminar-series-xiu-yang-computational-mathematics-scientist-pacific-northwest-national-laboratory/
LOCATION:2150 H.H. Dow\, 2300 Hayward St\, Ann Arbor\, 48109\, United States
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