Computational Fluid Dynamics: Uncertainty Quantification
ORAL · Q34 ·
Presentations
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Non-intrusive uncertainty quantification of computational fluid dynamics simulations: notes on the accuracy and efficiency
ORAL
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Authors
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Malgorzata Zimon
- IBM Research UK
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Robert Sawko
- IBM Research UK
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David Emerson
- STFC Daresbury Laboratory
- Scientific Computing Department, STFC Daresbury laboratory, Warrington WA4 4AD, United Kingdom
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Christopher Thompson
- IBM Research UK
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Uncertainty Quantification in Multi-scale Simulations of Coronary Artery Bypass Grafts
ORAL
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Authors
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Justin Tran
- Stanford Univ
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Daniele Schiavazzi
- University of Notre Dame
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Abhay Ramachandra
- Yale University
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Andrew Kahn
- University of California, San Diego
- UCSD Medical Center
- UCSD
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Alison Marsden
- Stanford University
- Bioengineering, Pediatric Cardiology - Stanford Univ
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Quantifying Structural Uncertainties for Turbulent Passive Scalar Transport
ORAL
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Authors
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Zengrong Hao
- Stanford University
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Catherine Gorle
- Stanford University
- Stanford Univ
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Eigenspace perturbations for structural uncertainty estimation of turbulence closure models
ORAL
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Authors
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Lluis Jofre
- Stanford University
- Center for Turbulence Research, Stanford University
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Aashwin Mishra
- Stanford University
- Center for Turbulence Research, Stanford University
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Gianluca Iaccarino
- Stanford University
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Uncertainty Quantification for Combined Polynomial Chaos Kriging Surrogate Models
ORAL
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Authors
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Justin Weinmeister
- Colorado State Univ
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Xinfeng Gao
- Colorado State Univ
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Aditi Krishna Prasad
- Colorado State Univ
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Sourajeet Roy
- Colorado State Univ
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Inadequacy representation of flamelet-based RANS model for turbulent non-premixed flame
ORAL
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Authors
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M.K. Lee
- The University of Texas at Austin
- Institute for Computational Engineering and Sciences, UT-Austin
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Todd Oliver
- The University of Texas at Austin
- Univ of Texas, Austin
- Institute for Computational Engineering and Sciences, UT-Austin
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Robert Moser
- The University of Texas at Austin
- University of Texas at Austin
- Institute for Computational Engineering and Sciences, The University of Texas at Austin
- Univ of Texas, Austin
- Institute for Computational Engineering and Sciences, UT-Austin
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Uncertainty quantification in Eulerian-Lagrangian models for particle-laden flows
ORAL
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Authors
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Vasileios Fountoulakis
- San Diego State University
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Gustaaf Jacobs
- San Diego State University
- Department of Aerospace Engineering, Sandiego State Univeristy
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H. S. Udaykumar
- University of Iowa
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Simulation of noisy dynamical system by Deep Learning
ORAL
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Authors
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Kyongmin Yeo
- IBM T J Watson Res Ctr
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Machine Learning Algorithms for prediction of regions of high Reynolds Averaged Navier Stokes Uncertainty
ORAL
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Authors
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Aashwin Mishra
- Stanford University
- Center for Turbulence Research, Stanford University
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Gianluca Iaccarino
- Center for Turbulence Research, Stanford University
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Estimating the State of Aerodynamic Flows in the Presence of Modeling Errors
ORAL
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Authors
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Andre F. C. da Silva
- California Institute of Technology
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Tim Colonius
- California Institute of Technology
- Caltech
- Department of Mechanical and Civil Engineering, California Institute of Technology
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Data-free and data-driven spectral perturbations for RANS UQ
POSTER
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Authors
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Wouter Edeling
- Stanford University
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Aashwin Mishra
- Stanford University
- Center for Turbulence Research, Stanford University
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Gianluca Iaccarino
- Stanford University
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Vortex-based flow estimation with an ensemble Kalman filter
ORAL
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Authors
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Darwin Darakananda
- University of California, Los Angeles
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Jeff D. Eldredge
- University of California, Los Angeles
- Mechanical & Aerospace Engineering, University of California, Los Angeles
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Andre F. C. da Silva
- California Institute of Technology
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Tim Colonius
- California Institute of Technology
- Caltech
- Department of Mechanical and Civil Engineering, California Institute of Technology
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