RANS turbulence model development using CFD-driven machine learning

Author:

Zhao YaominORCID,Akolekar Harshal D.,Weatheritt JackORCID,Michelassi Vittorio,Sandberg Richard D.

Funder

DOE

Swiss National Supercomputing Centre

Australian Government

Government of Western Australia

Publisher

Elsevier BV

Subject

Computer Science Applications,Physics and Astronomy (miscellaneous),Applied Mathematics,Computational Mathematics,Modelling and Simulation,Numerical Analysis

Reference33 articles.

1. Guidelines and criteria for the use of turbulence models in complex flows;Hunt,2005

2. Turbulence modeling in the age of data;Duraisamy;Annu. Rev. Fluid Mech.,2019

3. Bayesian estimates of parameter variability in the k–ε turbulence model;Edeling;J. Comput. Phys.,2014

4. Machine learning methods for data-driven turbulence modeling;Zhang,2015

5. A paradigm for data-driven predictive modeling using field inversion and machine learning;Parish;J. Comput. Phys.,2016

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