Computational Fluid Dynamics: Uncertainty Quantification (5:00pm - 5:45pm CST)

Presentations

• Two-Stage Ensemble Kalman Filter Approach to Estimate Fracture Parameters in Sub-Surface Formations

  POSTER

  Authors

  • Michael Liem

    • ETH Zurich

  • Patrick Jenny

    • ETH Zurich

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• Physics-constrained multi-fidelity convolutional neural networks for surrogate fluid modeling

  POSTER

  Authors

  • Luning Sun

    • University of Notre Dame

  • Jian-Xun Wang

    • University of Notre Dame

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• Uncertainty Quantification in Complex Flows for Aeronautical {\&} Mechanical Engineering Applications

  POSTER

  Authors

  • Francisco-Javier Granados-Ortiz

    • University of M\'{a}laga

  • Joaquin Ortega-Casanova

    • University of M\'{a}laga

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• Influence of Reynolds Number and Flow Configuration on Turbulence Model Form Errors

  POSTER

  Authors

  • Kerry S. Klemmer

    • Princeton University

  • Michael E Mueller

    • Princeton University

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• Quantifying the uncertainties of density self-correlation in RANS simulations for variable density flows

  POSTER

  Authors

  • Jan Felix Heyse

    • Center for Turbulence Research, Stanford University

  • Zhu Huang

    • Center for Turbulence Research, Stanford University

  • Aashwin Mishra

    • Center for Turbulence Research, Stanford University

  • Gianluca Iaccarino

    • Center for Turbulence Research, Stanford University

  • Timothy Clarke Wallstrom

    • Los Alamos National Laboratory

  • David Sharp

    • Los Alamos National Laboratory

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• Epistemic uncertainty quantification of Reynolds stress models

  POSTER

  Authors

  • Xinyi Huang

    • Pennsylvania State University

  • Naman Jain

    • Penn State University, Mechanical Engineering
    • Pennsylvania State University

  • Robert Kunz

    • Penn State University, Mechanical Engineering
    • Pennsylvania State University

  • Xiang Yang

    • Pennsylvania State University
    • Penn State University, Mechanical Engineering

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• Evaluation of a polynomial-chaos based multi-fidelity simulation framework for predicting wind pressure loads on buildings.

  POSTER

  Authors

  • Themistoklis Vargiemezis

    • Stanford University

  • Catherine Gorlé

    • Stanford Univ
    • Stanford University
    • Wind Engineering Laboratory, Department of Civil and Environmental Engineering, Stanford University

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• Machine-learning quasilinear Gaussian moment closures for uncertainty quantification of turbulent fluid flows

  POSTER

  Authors

  • Alexis-Tzianni Charalampopoulos

    • Massachusetts Institute of Technology MIT

  • Themistoklis Sapsis

    • Massachusetts Institute of Technology MIT
    • MIT

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• Exploring turbulence model uncertainties in turbomachinery applications

  POSTER

  Authors

  • Marcel Matha

    • German Aerospace Center (DLR)

  • Christian Morsbach

    • German Aerospace Center (DLR)

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• Full-scale validation of natural ventilation models using uncertainty quantification

  POSTER

  Authors

  • Chen Chen

    • Stanford Univ

  • Catherine Gorlé

    • Stanford Univ
    • Stanford University
    • Wind Engineering Laboratory, Department of Civil and Environmental Engineering, Stanford University

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• Field Sensitivity Analysis for Wind Energy Modeling

  POSTER

  Authors

  • Julian Quick

    • CU Boulder

  • Ryan King

    • National Renewable Energy Laboratory

  • Marc Henry de Frahan

    • National Renewable Energy Laboratory

  • Shreyas Ananthan

    • National Renewable Energy Laboratory

  • Michael Sprague

    • National Renewable Energy Laboratory

  • Peter Hamlington

    • CU Boulder

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