Predictive Turbulence Modeling with Bayesian Inference and Physics-Informed Machine Learning

Predictive Turbulence Modeling with Bayesian Inference and Physics-Informed Machine Learning

Reynolds-Averaged Navier-Stokes (RANS) simulations are widely used for engineering design and analysis involving turbulent flows. In RANS simulations, the Reynolds stress needs closure models and the existing models have large model-form uncertainties. Therefore, the RANS simulations are known to be...

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Bibliographic Details
Main Author: Wu, Jinlong
Other Authors: Aerospace and Ocean Engineering
Format: Others
Published: Virginia Tech 2018
Subjects:
Turbulence modeling
RANS
Model-form uncertainty
Data-driven
Uncertainty quantification
Bayesian Inference
Machine learning
Online Access:http://hdl.handle.net/10919/85129

Internet

http://hdl.handle.net/10919/85129

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