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DTSTART;TZID=America/Detroit:20210625T120000
DTEND;TZID=America/Detroit:20210625T130000
DTSTAMP:20230217T195710Z
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SUMMARY:ME Special Webinar: Aditya Nair\, Mechanical Engineering\, University of Nevada
DESCRIPTION:Bio: Aditya G. Nair joined the Department of Mechanical Engineering at U. Nevada\, Reno in August 2020. His research interests are in the areas of computational fluid dynamics\, unsteady aerodynamics\, high-performance computing\, data science and control theory focused on modeling and control of high-dimensional fluid flow physics. He received his Ph.D. in mechanical engineering from the Florida State U. in 2018. Prior to this\, he completed his M.S. in mechanical engineering from U. Michigan in 2013 and B.E in mechanical engineering from University of Mumbai in 2011. Following his Ph.D.\, he served as a post-doctoral research associate in the mechanical engineering department at U. of Washington till July 2020. \nPhysics-based and data-driven strategies for modeling and control of unsteady fluid flows\nData and network science offer novel perspectives to describe complex dynamics over a web of interactions. The last few years have seen an emergence of such techniques to characterize\, model\, and control fluid flows. The ability of these approaches to characterize vortical and modal interactions in unsteady fluid flows is essential to understand its complex behavior. Using a simple description of the flow physics on a mathematical graph with weights based on vortical interactions\, modal interactions and probabilistic interactions\, we can design feedback control techniques to alter flow behavior. We discuss some of the successes and challenges associated with applying this formalism for fluid dynamics. \nZoom information\nlink: umich.zoom.us/j/92859783250\npasscode: 833499 \nProf. Nair is being hosted by Prof. Johnsen (Mechanical Engineering). \n 
URL:https://micde.umich.edu/event/me-special-webinar-aditya-nair-mechanical-engineering-university-of-nevada/
LOCATION:Zoom Event\, MI\, United States
ATTACH;FMTTYPE=image/jpeg:https://micde.umich.edu/wp-content/uploads/2023/02/aditya-nair-nevada.jpeg
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BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20210216T150000
DTEND;TZID=America/Detroit:20210216T160000
DTSTAMP:20230905T171258Z
CREATED:20230905T171258Z
LAST-MODIFIED:20230905T171258Z
UID:10000428-1613487600-1613491200@micde.umich.edu
SUMMARY:MICDE Seminar: Emma Lejeune\, Assistant Professor\, Mechanical Engineering\, Boston University
DESCRIPTION:Bio: Emma Lejeune is an Assistant Professor in the Mechanical Engineering Department at Boston University. She received her PhD from Stanford University in September 2018\, and was a Peter O’Donnell\, Jr. postdoctoral research fellow at the Oden Institute at the University of Texas at Austin until 2020 when she joined the faculty at BU. At BU\, Emma has received the David R. Dalton Career Development Professorship\, a Computational Science and Engineering Junior Faculty Fellowship\, and the Haythornthwaite Research Initiation Grant from the ASME Applied Mechanics Division. Current areas of research involve integrating data-driven and physics based computational models\, and characterizing and predicting the mechanical behavior of heterogeneous materials and biological systems. \nMODELING HETEROGENEOUS MATERIALS: BENCHMARK DATASETS\, METAMODELS\, AND EXPERIMENTAL CHARACTERIZATION: \nBiological systems are spatially heterogeneous across scales. To effectively model biological materials we need new tools to quantify and capture this heterogeneity. In this talk\, we will first discuss our recent work on simulating spatially heterogeneous materials. Specifically\, we will discuss our recent work in developing and exploring benchmark datasets of spatially heterogeneous materials simulated with the finite element method. These datasets are useful primarily for constructing metamodels\, or computationally cheap models of models\, that map defined model inputs to defined model outputs. By nature\, a given metamodel will be tailored to a specific dataset. However\, the most pragmatic metamodel type and structure will often be general to larger classes of problems. At present\, the most pragmatic metamodel selection for predicting the mechanical behavior of spatially heterogeneous materials — specifically simulations of heterogenous materials — has not been thoroughly explored. Drawing inspiration from the benchmark datasets available to the computer vision research community\, we introduce a benchmark data set (Mechanical MNIST https://open.bu.edu/handle/2144/39371) for constructing metamodels of heterogeneous material undergoing large deformation. We then show a few examples of problems that we have explored thus far with this dataset. Looking forward\, we anticipate that disseminating benchmark datasets will enable the broader community of researchers to develop improved metamodeling techniques for capturing the behavior of spatially heterogeneous materials that will surpass the baseline performance that we show here. Finally\, to conclude the talk\, we will change gears and briefly discuss some of our recent work on creating new tools for characterizing cell behavior using concepts from kinematics and spatial statistics. Looking forward\, we are interested in the natural synergy between advances in methods for both simulating and characterizing heterogeneous materials. \n\nThe MICDE Winter 2021 Seminar Series is open to all. University of Michigan faculty and students interested in computational and data sciences are encouraged to attend. \nDr. Lejeune will be hosted by Professor Krishna Garikipati\, MICDE Director. \nWatch the full webinar. \nQuestions? Email MICDE-events@umich.edu
URL:https://micde.umich.edu/event/micde-seminar-emma-lejeune-assistant-professor-mechanical-engineering-boston-university/
LOCATION:Zoom Event
CATEGORIES:Featured Events,MICDE Seminar Series
ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2020/11/Emma-Lejeune.png
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BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20210209T160000
DTEND;TZID=America/Detroit:20210209T170000
DTSTAMP:20230905T171258Z
CREATED:20230905T171258Z
LAST-MODIFIED:20230905T171258Z
UID:10000408-1612886400-1612890000@micde.umich.edu
SUMMARY:MICDE / Mechanical Engineering Seminar: Ceila Reina\, Assistant Professor\, Mechanical Engineering and Applied Mechanics\, University of Pennsylvania
DESCRIPTION:Bio:  Celia Reina is the William K. Gemmill Term Assistant Professor in Mechanical Engineering and Applied Mechanics at the University of Pennsylvania. She joined in 2014 after holding the Lawrence Postdoctoral Fellowship at Lawrence Livermore National Laboratory and the HCM postdoctoral Fellowship at the Hausdorff Center of Mathematics in Bonn\, Germany. Dr. Reina received her PhD from the California Institute of Technology in Aerospace Engineering in 2011\, under the supervision of Prof. Michael Ortiz\, following a B.S. in Mechanical Engineering from the University of Seville in Spain\, and a Master in Structural Dynamics from Ecole Centrale Paris in France. She is the 2017 recipient of the Eshelby Mechanics Award for Young Faculty\, she is a member of the TTA on Nanotechnology and Lower Scale Phenomena at the USACM\, and she currently serves as the recording secretary for the Applied Mechanics Division of the ASME. \nCONTINUUM MECHANICS OF NON-EQUILIBRIUM PHENOMENA: A JOURNEY THROUGH SPACE AND TIME SCALES:  The fascinating diversity of material behavior at the macroscopic scale can only emerge from the underlying atomistic or particle behavior. Yet\, the direct connection between these two scales remains an extremely challenging quest\, particularly in the context of non-equilibrium phenomena. In this talk\, we will discuss several advances in this direction\, in the context of plasticity\, thermoelasticity\, diffusion and viscous dissipation. In all these cases\, the importance of fluctuations in the effective response will become apparent. More precisely\, these will provide crucial information for the material description and evolution at the continuum scale\, where the behavior is modeled as deterministic and free of fluctuations. \n\nThe MICDE Winter 2021 Seminar Series is open to all. University of Michigan faculty and students interested in computational and data sciences are encouraged to attend. \nThis event will be a joint seminar with the University of Michigan College of Engineering’s Mechanical Engineering department. \nQuestions? Email MICDE-events@umich.edu
URL:https://micde.umich.edu/event/micde-mechanical-engineering-seminar-ceila-reina-assistant-professor-mechanical-engineering-and-applied-mechanics-university-of-pennsylvania/
LOCATION:Zoom Event
CATEGORIES:Featured Events,MICDE Seminar Series
ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2020/09/Celia-Reina.png
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BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20200929T140000
DTEND;TZID=America/Detroit:20200929T150000
DTSTAMP:20230905T171252Z
CREATED:20230905T171252Z
LAST-MODIFIED:20230905T171252Z
UID:10000409-1601388000-1601391600@micde.umich.edu
SUMMARY:MICDE / Mechanical Engineering Seminar: Sophia Haussener\, Associate Professor\, Laboratory of Renewable Energy Science and Engineering\, EPFL\, Lausanne\, Switzerland
DESCRIPTION:View webinar recording. \nBio: Sophia Haussener is an Associate Professor heading the Laboratory of Renewable Energy Science and Engineering at the Ecole Polytechnique Fédérale de Lausanne (EPFL). Her current research is focused on providing design guidelines for thermal\, thermochemical\, and photoelectrochemical energy conversion reactors through multi-physics modelling. Her research interests include: thermal sciences\, fluid dynamics\, charge transfer\, electro-magnetism\, and thermo/electro/photochemistry in complex multi-phase media on multiple scales. She received her MSc (2007) and PhD (2010) in Mechanical Engineering from ETH Zurich. Between 2011 and 2012\, she was a postdoctoral researcher at the Joint Center of Artificial Photosynthesis (JCAP) and the Energy Environmental Technology Division of the Lawrence Berkeley National Laboratory (LBNL). She has published over 70 articles in peer-reviewed journals and conference proceedings\, and 2 books. She has been awarded the ETH medal (2011)\, the Dimitris N. Chorafas Foundation award (2011)\, the ABB Forschungspreis (2012)\, the Prix Zonta (2015)\, the Global Change Award (2017)\, and the Viskanta Award (2019)\, and is a recipient of a Starting Grant of the Swiss National Science Foundation (2014). She is a deputy leader in the Swiss Competence Center for Energy Research (SCCER) on energy storage and acts as a Member of the Scientific Advisory Council of the Helmholtz Zentrum. \nModelling\, experimentation and scaling of photo-electrochemical fuel processing devices\nThe development of a sustainable energy economy based on renewable\, carbon-neutral energy is a necessary and urgent task. Photo-electrochemical approaches for solar fuels and materials are interesting\, provided they can be efficiently\, stably\, scalably\, and sustainably implemented. The functionality of such devices relies on complicated and coupled multi-physics processes\, occurring at multiple temporal and spatial scales. Device modelling can actively and efficiently support the choice of the most promising – in terms of efficiency\, cost\, robustness\, scalability\, and practicability – conceptual design pathways\, material choices\, and operating approaches. \nFirst\, I focus on cost competitive photo-electrochemical (PEC) devices identified through quasi-transient techno-economic modelling [1]. I will describe the conceptual idea of thermal integration in the context of PEC [2]\, provide results of maximum theoretical efficiency calculations to quantify the benefits\, and review the modelling framework that enabled the design of a feasible device [3]. I will illustrate how we have used our models to design and implement a PEC device with a solar-to-fuel efficiency of 17%\, and discuss ongoing approaches to scale up by our lab in order to bridge the gap between research and practical applications. \nSecond\, I will discuss detailed multi-dimensional mesoscale models that allow to assess the transport in complex (photo)electrodes. Specifically\, we use direct pore-level simulations for the coupled transport characterization of mesostructured (photo)electrodes utilizing nano-tomography techniques to obtain the exact mesostructure that is utilized in direct numerical simulations [4]. I will extend these investigations to ordered structures for the assessment of the transport in mesostructured electrodes for the electorchemical reduction of CO2 and discuss the effect of the mass transport on selectivity and activity [5]. I will then present possibilities to simplify these involved multi-dimensional numerical models into rapid screening models based on semi-analytical correlations. I will discuss analysis results for a large range of semiconductor materials [6\,7]. I will end with an outlook on research challenges and gaps in the field of (photo)electrochemical water and CO2 splitting. \n\nThis seminar is co-hosted by the Michigan Institute for Computational Discovery & Engineering\, and the Mechanical Engineering department within the University of Michigan College of Engineering. Dr. Haussener will be hosted by Rohini Bala Chandran\, Assistant Professor of Mechanical Engineering. \nThe MICDE Fall 2020 and Winter 2021 Seminar Series is open to the general public. University of Michigan faculty and students interested in computational and data sciences are encouraged to attend.  \nQuestions? Email MICDE-events@umich.edu \n\nReferences: \n[1] M. Dumortier\, S. Tembhurne\, S. Haussener\, Energy Environ. Sci. \, 8:3614–3628\, 2015\n[2] S. Tembhurne\, F. Nandjou\, S. Haussener\, Nature Energy\, 10.1038/s41560-019-0373-7\, 2019\n[3] S. Tembhurne\, S. Haussener\, Journal of The Electrochemical Society \, 163:H1008-H1018\, 2016\n[4] S. Suter\, M. Catoni\, Y. Gaudy\, S. Pokrant\, S. Haussener\, Linking Morphology and Multi-Physical Transport in\nStructured Photoelectrodes\, Sustainable Energy & Fuels \, doi: 10.1039/C8SE00215K\, 2018.\n[5] S. Suter\, S. Haussener\, Energy Environmental Science \, doi: 10.1039/C9EE00656G\, 2019.\n[6] Y. Gaudy\, S. Haussener\, Rapid Performance Optimization Method for Photoelectrodes\, Journal of Physical Chemistry\nC\, doi: 10.1021/acs.jpcc.9b04102\, 2019.\n[7] Y. Gaudy\, Z. Gacevic\, Haussener\, Theoretical maximum photogeneration efficiency and performance characterization\nof InxGa1-xN/Si tandem water-splitting photoelectrodes\, APL Materials\, accepted\, 2020.
URL:https://micde.umich.edu/event/micde-mechanical-engineering-seminar-sophia-haussener-associate-professor-laboratory-of-renewable-energy-science-and-engineering-swiss-federal-institute-of-technology-lausanne/
LOCATION:Zoom Event
CATEGORIES:Featured Events,MICDE Seminar Series
ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2020/09/Sophia-Haussener.png
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DTSTART;TZID=America/Detroit:20191028T160000
DTEND;TZID=America/Detroit:20191028T160000
DTSTAMP:20230905T171336Z
CREATED:20230905T171336Z
LAST-MODIFIED:20230905T171336Z
UID:10000297-1572278400-1572278400@micde.umich.edu
SUMMARY:Mid West Mechanics Seminar: Jacqueline H. Chen\, Senior Scientist\, Sandia National Laboratories
DESCRIPTION:Bio: Jacqueline H. Chen is a Senior Scientist at the Combustion Research Facility at Sandia National Laboratories. She has contributed broadly to research in turbulent combustion elucidating turbulence-chemistry interactions in combustion through direct numerical simulations. To achieve scalable performance of DNS on heterogeneous computer architectures she leads an interdisciplinary team of computer scientists\, applied mathematicians and computational scientists to develop an exascale direct numerical simulation capability for turbulent combustion with complex chemistry and multi- physics. She is a member of the National Academy of Engineering and a Fellow of the Combustion Institute and the Americal Physical Society. She received the Combustion Institute’s Bernard Lewis Gold Medal Award in 2018 and the Society of Women Engineers Achievement Award in 2018. \nTowards Exascale Simulation of Turbulent Combustion in Complex Flows Relevant to Efficient Clean Engines\nDirect numerical simulation (DNS) methodology and computing power have progressed to the point where it is feasible to perform DNS in mildly complex geometries representative of flow configurations encountered in practical combustors. These complex flows encompass effects of mean shear\, flow recirculation\, and wall boundary layers together with turbulent fluctuations which affect entrainment\, mixing and combustion. Recent DNS studies with complex flows relevant to efficient low emissions gas turbine and internal combustion engines will be presented. Through application co-design with computer scientists a data centric asynchronous programming system has been used to refactor the DNS code\, S3D\, resulting in improved time-to-solution and overall performance on heterogeneous architectures. The programming system also provides more efficient and effective composition of in situ analytics and machine learning techniques. \nContact Melissa McGeorge (mcgeorge@umich.edu) for more details.
URL:https://micde.umich.edu/event/mid-west-mechanics-seminar-jacqueline-h-chen-sandia-national-laboratories/
LOCATION:107 Gorguze Family Laboratory\, 2609 Draper Dr\, Ann Arbor\, MI\, 48109\, United States
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://micde.umich.edu/wp-content/uploads/2023/02/JacquelineChen.jpg
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BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20171211T100000
DTEND;TZID=America/Detroit:20171211T110000
DTSTAMP:20230905T171416Z
CREATED:20230905T171416Z
LAST-MODIFIED:20230905T171416Z
UID:10000119-1512986400-1512990000@micde.umich.edu
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
ATTACH;FMTTYPE=image/jpeg:https://micde.umich.edu/wp-content/uploads/2023/02/XiuYang.jpg
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20171206T120000
DTEND;TZID=America/Detroit:20171206T130000
DTSTAMP:20230905T171416Z
CREATED:20230905T171416Z
LAST-MODIFIED:20230905T171416Z
UID:10000118-1512561600-1512565200@micde.umich.edu
SUMMARY:ME Faculty Candidate Seminar Series: Xun (Ryan) Huan\, Combustion Research Facility at Sandia National Laboratories
DESCRIPTION:Bio: Xun (Ryan) Huan is a postdoctoral researcher in the Combustion Research Facility at Sandia National Laboratories. He received a Ph.D. in Computational Science and Engineering from MIT Department of Aeronautics and Astronautics. He also has a master’s degree from MIT and a bachelor’s degree from the University of Toronto\, both in Aerospace Engineering. Xun’s research interests broadly revolve around uncertainty quantification\, decision-making under uncertainty\, data-driven modeling\, and optimization for engineering applications. Outside work\, he is an ice hockey player and a pilot. \nFinding the Most Informative Data Using Model-based Optimal Experimental Design\nExperimental data play a crucial role in developing and refining models of physical systems. However\, some experiments produce more useful data than others\, and well-chosen experiments can provide substantial resource savings. Optimal experimental design (OED) thus seeks to systematically quantify and maximize the value of experiments. We introduce general mathematical frameworks and algorithmic approaches for OED with nonlinear models. The formalism employs Bayesian statistics and an information-theoretic objective\, and rigorously defines the conditions under which batch experiments (experiments chosen simultaneously) and sequential experiments (forward-looking designs with data feedback) are truly optimal. Finding these optimal designs using conventional means is generally intractable. We develop practical numerical methods for OED by advancing computational techniques on several fronts\, including stochastic optimization\, polynomial chaos surrogate modeling\, approximate dynamic programming\, and transport maps. Using the overall algorithm\, we design combustion experiments for optimal learning of Arrhenius kinetic parameters\, and sequential sensor placement for contaminant source inversion.\n\n* Lunch won’t be provided but you are welcome to bring your own\n\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-xun-ryan-huan/
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://micde.umich.edu/wp-content/uploads/2023/02/XunRyanHuan-e1583777526832.jpg
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BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20171204T100000
DTEND;TZID=America/Detroit:20171204T110000
DTSTAMP:20230905T171416Z
CREATED:20230905T171416Z
LAST-MODIFIED:20230905T171416Z
UID:10000117-1512381600-1512385200@micde.umich.edu
SUMMARY:ME Faculty Candidate Seminar Series: Maziar Raissi\, Brown University
DESCRIPTION:Bio: Maziar Raissi is an Assistant Professor of Applied Mathematics (research) in the Division of Applied Mathematics at Brown University. He received his Ph.D. in Applied Mathematics & Statistics\, and Scientific Computations from University of Maryland — College Park in December 2016. Raissi’s expertise lies at the intersection of Probabilistic Machine Leaning\, Deep Learning\, and Data Drive Scientific Computing. \nHidden Physics Models: Machine Learning of Non-linear Partial Differential Equations\n\nA grand challenge with great opportunities is to develop a coherent framework that enables blending conservation laws\, physical principles\, and/or phenomenological behaviours expressed by differential equations with the vast data sets available in many fields of engineering\, science\, and technology. At the intersection of probabilistic machine learning\, deep learning\, and scientific computations\, this work is pursuing the overall vision to establish promising new directions for harnessing the long-standing developments of classical methods in applied mathematics and mathematical physics to design learning machines with the ability to operate in complex domains without requiring large quantities of data. To materialize this vision\, this work is exploring two complimentary directions: (1) designing data-efficient learning machines capable of leveraging the underlying laws of physics\, expressed by time dependent and non-linear differential equations\, to extract patterns from high-dimensional data generated from experiments\, and (2) designing novel numerical algorithms that can seamlessly blend equations and noisy multi-fidelity data\, infer latent quantities of interest (e.g.\, the solution to a differential equation)\, and naturally quantify uncertainty in computations. The latter is aligned in spirit with the emerging field of probabilistic numerics. \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-maziar-raissi-brown-university/
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://micde.umich.edu/wp-content/uploads/2023/02/MaziarRaissi.jpg
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BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20171019T120000
DTEND;TZID=America/Detroit:20171019T133000
DTSTAMP:20230905T171405Z
CREATED:20230905T171405Z
LAST-MODIFIED:20230905T171405Z
UID:10000115-1508414400-1508419800@micde.umich.edu
SUMMARY:Mechanical Engineering Seminar: Mark Owkes\, Department of Mechanical and Industrial Engineering\, Montana State University
DESCRIPTION:Bio: Mark Owkes is an Assistant Professor in the department of Mechanical and Industrial Engineering at Montana State University. He earned a BS in Mechanical Engineering from Clarkson University in 2008. He subsequently attained an MS in Mechanical Engineering from the University of Colorado at Boulder in 2011 under the direction of Prof. Olivier Desjardins. He continued his work under Prof. Desjardins at Cornell University where he earned his Ph.D. in May 2014. Mark’s research interests include the development of numerical methods for capturing gas-liquid interfaces in multiphase flow simulations. His simulations of primary atomization provide insight into the physical phenomena important in the break-up of a liquid jet into droplets. Notably\, he has developed both a level set and a volume-of-fluid interface capturing schemes and multiple approaches to compute the curvature of a gas-liquid interface which is important for accurate surface tension forces. \nGas-Liquid Flows: Numerical Methods through Simulations on Supercomputers\nGas-liquid flows exist within many engineering devices including fuel injectors\, wave energy extraction devices\, fire suppression systems\, and PEM fuel cells. Many of these flows are challenging to explore experimentally and computational fluid dynamics (CFD) simulations offer an alternative and useful approach to advance our understanding. For example\, the breakup of liquid fuel into droplets via atomization has a direct effect on combustion efficiency and pollutant formation\, yet a fundamental understanding of the complex process is absent. Laboratory experiments are inherently difficult to conduct because atomizing jets produce a large number of opaque droplets that hinder optical access to the breakup dynamics. With increasing computational resources and advancements in numerical methods\, computational fluid dynamics (CFD) has emerged as a promising tool to investigate the fundamental nature of atomization. In this presentation\, I will present an overview of difficulties arising due to the discontinuities that exist at the gas-liquid interface and recent advances in numerical methods that overcome these challenges. Then I will discuss efforts to improve the the usefulness of the very large data-sets that result from CFD simulations. Details on computing the curvature of a gas-liquid interface\, implementing contact line dynamics\, performing physics extraction\, and coupling gas-liquid flow calculations with uncertainty quantification we be discussed.
URL:https://micde.umich.edu/event/mechanical-engineering-seminar-mark-owkes-department-of-mechanical-and-industrial-engineering-montana-state-university/
LOCATION:1012 EECS\, 1301 Beal Ave\, Ann Arbor\, MI\, 48104\, United States
CATEGORIES:Seminar
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