CFD: Data-driven Methods

Presentations

• Towards Generalizable Data-driven Turbulence Model Augmentations

  ORAL

  Nov. 24, 2019, 3:48 PM – Nov. 24, 2019, 4:01 PM

  Authors

  • Vishal Srivastava

    • University of Michigan, Ann Arbor

  • Karthik Duraisamy

    • University of Michigan
    • University of Michigan, Ann Arbor

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• A data-driven approach to modeling turbulent decay at non-asymptotic Reynolds numbers

  ORAL

  Nov. 24, 2019, 4:01 PM – Nov. 24, 2019, 4:14 PM

  Authors

  • Mateus Dias Ribeiro

    • German Research Center for Artificial Intelligence
    • German Research Center for Artificial Intelligence (DFKI)

  • Gavin Portwood

    • Los Alamos National Laboratory
    • Los Alamos National Laboratory, Los Alamos

  • Peetak Mitra

    • University of Massachusetts Amherst
    • University of Massachusetts, Amherst

  • Tan Mihn Nyugen

    • NVIDIA Corporation, Santa Clara

  • Balasubramanya Nadiga

    • Los Alamos National Lab
    • Los Alamos National Laboratory, Los Alamos
    • Los Alamos National Laboratory

  • Misha Chertkov

    • University of Arizona
    • Los Alamos National Laboratory, Los Alamos

  • Anima Anandkumar

    • NVIDIA Corporation, Santa Clara

  • David P Schmidt

    • University of Massachusetts Amherst
    • University of Massachusetts, Amherst

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• A data-driven approach to modeling turbulent flows in an engine environment

  ORAL

  Nov. 24, 2019, 4:14 PM – Nov. 24, 2019, 4:27 PM

  Authors

  • Peetak Mitra

    • University of Massachusetts Amherst
    • University of Massachusetts, Amherst

  • Mateus Dias Ribeiro

    • German Research Center for Artificial Intelligence
    • German Research Center for Artificial Intelligence (DFKI)

  • David P Schmidt

    • University of Massachusetts Amherst
    • University of Massachusetts, Amherst

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• Toward data-driven stochastically forced turbulence closure models

  ORAL

  Nov. 24, 2019, 4:27 PM – Nov. 24, 2019, 4:40 PM

  Authors

  • Armin Zare

    • The University of Texas at Dallas
    • University of Texas at Dallas

  • Anubhav Dwivedi

    • University of Minnesota

  • Mihailo Jovanovic

    • University of Southern California
    • USC
    • Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California

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• A data-driven approach to simulate turbulent bubbly flows using machine learning for modeling bubble size.

  ORAL

  Nov. 24, 2019, 4:40 PM – Nov. 24, 2019, 4:53 PM

  Authors

  • Hokyo Jung

    • Dept. of Mechanical Engineering, Sogang University

  • Youngjae Kim

    • Dept. of Mechanical Engineering, Sogang University

  • Serin Yoon

    • Dept. of Mechanical Engineering, Sogang University

  • Gangwoo Ha

    • Dept. of Mechanical Engineering, Sogang University

  • Jun Ho Lee

    • Dept. of Mechanical and Aerospace Engineering, Seoul National University

  • Hyungmin Park

    • Seoul National University
    • Dept. of Mechanical and Aerospace Engineering, Seoul National University
    • Seoul national university

  • Dongjoo Kim

    • Dept. of Mechanical Engineering, Kumoh National Institute of Technology

  • Jungwoo Kim

    • Dept. of Mechanical System Design Engineering, Seoul National University of Science and Technology

  • Seongwon Kang

    • Dept. of Mechanical Engineering, Sogang University

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• Deep Neural Networks for Data-Driven Turbulence Models

  ORAL

  Nov. 24, 2019, 4:53 PM – Nov. 24, 2019, 5:06 PM

  Authors

  • Andrea Beck

    • University of Stuttgart

  • David Flad

    • University of Stuttgart

  • Claus-Dieter Munz

    • University of Stuttgart

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• Generalized Non-Linear Eddy Viscosity Models for Data-Assisted Reynolds Stress Closure

  ORAL

  Nov. 24, 2019, 5:06 PM – Nov. 24, 2019, 5:19 PM

  Authors

  • Basu Parmar

    • University of Colorado Boulder

  • Eric Peters

    • Ball Aerospace

  • Kenneth Jansen

    • University of Colorado Boulder

  • Alireza Doostan

    • University of Colorado, Boulder
    • University of Colorado Boulder

  • John Evans

    • University of Colorado Boulder
    • University of Colorado, Boulder

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• An S-frame Discrepancy Correction for Data-Driven Reynolds Stress Closure

  ORAL

  Nov. 24, 2019, 5:19 PM – Nov. 24, 2019, 5:32 PM

  Authors

  • Aviral Prakash

    • University of Colorado, Boulder

  • Eric Peters

    • Ball Aerospace

  • Riccardo Balin

    • University of Colorado Boulder
    • University of Colorado, Boulder

  • Kenneth Jansen

    • U. of Colorado-Boulder
    • University of Colorado Boulder
    • University of Colorado, Boulder

  • Alireza Doostan

    • University of Colorado, Boulder
    • University of Colorado Boulder

  • John Evans

    • University of Colorado Boulder
    • University of Colorado, Boulder

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