## Prediction of Reynolds Stresses in High-Mach-Number Turbulent Boundary Layers using Physics-Informed Machine Learning

- Jian-Xun Wang, Junji Huang, Lian Duan, Heng Xiao
- ArXiv
- 2019

Highly Influenced

13 Excerpts

@inproceedings{Wang2017PhysicsinformedML, title={Physics-informed machine learning approach for reconstructing Reynolds stress modeling discrepancies based on DNS data}, author={Jian-Xun Wang and Jin-Long Wu and Heng Xiao}, year={2017} }

- Published 2017
DOI:10.1103/PhysRevFluids.2.034603

Turbulence modeling is a critical component in numerical simulations of industrial flows based on Reynolds-averaged Navier-Stokes (RANS) equations. However, after decades of efforts in the turbulence modeling community, universally applicable RANS models with predictive capabilities are still lacking. Large discrepancies in the RANS-modeled Reynolds stresses are the main source that limits the predictive accuracy of RANS models. Identifying these discrepancies is of significance to possibly… CONTINUE READING