Uncertainty Quantification of RANS Data-Driven Turbulence Modeling
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Updated
Jan 11, 2021 - C++
Uncertainty Quantification of RANS Data-Driven Turbulence Modeling
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OpenFOAM case files for simulating the atmospheric boundary layer (ABL) using Reynolds-Averaged Navier-Stokes (RANS) turbulence models. Includes shear stress-driven, pressure-driven, and body force-driven ABL configurations.
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Predicting turbulent eddy viscosity and Reynolds stress using MLP neural networks trained on DNS data
rANS entropy coding with bits-back primitives
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