# Physics-informed machine learning approach for reconstructing Reynolds stress modeling discrepancies based on DNS data

@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

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