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}
}
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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