# Prediction of Reynolds stresses in high-Mach-number turbulent boundary layers using physics-informed machine learning

@article{Wang2018PredictionOR, title={Prediction of Reynolds stresses in high-Mach-number turbulent boundary layers using physics-informed machine learning}, author={Jian-Xun Wang and Junji Huang and Lian Duan and Heng Xiao}, journal={Theoretical and Computational Fluid Dynamics}, year={2018}, volume={33}, pages={1-19} }

- Published in ArXiv 2018
DOI:10.1007/s00162-018-0480-2

Modeled Reynolds stress is a major source of model-form uncertainties in Reynolds-averaged Navier–Stokes (RANS) simulations. Recently, a physics-informed machine learning (PIML) approach has been proposed for reconstructing the discrepancies in RANS-modeled Reynolds stresses. The merits of the PIML framework have been demonstrated in several canonical incompressible flows. However, its performance on high-Mach-number flows is still not clear. In this work, we use the PIML approach to predict… CONTINUE READING

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