Physics-Informed, Data-Driven Framework for Model-Form Uncertainty Estimation and Reduction in RANS Simulations

Physics-Informed, Data-Driven Framework for Model-Form Uncertainty Estimation and Reduction in RANS Simulations

Computational fluid dynamics (CFD) has been widely used to simulate turbulent flows. Although an increased availability of computational resources has enabled high-fidelity simulations (e.g. large eddy simulation and direct numerical simulation) of turbulent flows, the Reynolds-Averaged Navier-Stoke...

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Bibliographic Details
Main Author: Wang, Jianxun
Other Authors: Aerospace and Ocean Engineering
Format: Others
Published: Virginia Tech 2017
Subjects:
Uncertainty quantification
Data-driven
RANS
Turbulence modeling
Machine learning
Random matrix
Online Access:http://hdl.handle.net/10919/77035

Internet

http://hdl.handle.net/10919/77035

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