Towards high-accuracy deep learning inference of compressible turbulent flows over aerofoils. (arXiv:2109.02183v2 [physics.flu-dyn] UPDATED)

The present study investigates the accurate inference of Reynolds-averaged
Navier-Stokes solutions for the compressible flow over aerofoils in two
dimensions with a deep neural network. Our approach yields networks that learn
to generate precise flow fields for varying body-fitted, structured grids by
providing them with an encoding of the corresponding mapping to a canonical
space for the solutions. We apply the deep neural network model to a benchmark
case of incompressible flow at randomly given angles of attack and Reynolds …

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