Data-driven wall models for Reynolds Averaged Navier–Stokes simulations
نویسندگان
چکیده
This article presents a data-based methodology to build Reynolds-Averaged Navier–Stokes (RANS) wall models for aerodynamic simulations at low Mach numbers. Like classical approaches, the model is based on nondimensional local quantities derived from friction velocity uτ, viscosity μw, and density ρw. A fully-connected neural network approximates relation u+=f(y+,p+). We consider reference data (obtained with RANS fine meshes up wall) of attached turbulent flows various Reynolds numbers over different geometries bumps, covering range pressure gradients. After training networks subset data, paper assesses their ability accurately recover unseen conditions that have been trimmed an interface height where learned law applied. The network’s interpolation extrapolation capabilities are quantified carefully examined. Overall, when tested within its capabilities, shows good robustness accuracy. global error skin coefficient few percent behaves consistently all considered test cases.
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ژورنال
عنوان ژورنال: International Journal of Heat and Fluid Flow
سال: 2023
ISSN: ['1879-2278', '0142-727X']
DOI: https://doi.org/10.1016/j.ijheatfluidflow.2022.109097