TBNN
Reynolds averaged turbulence modelling using deep neural networks with embedded invariance
Paper title Reynolds averaged turbulence modelling using deep neural networks with embedded invariance
Paper doi.org
Learns the Reynolds stress anisotropy tensor of a RANS model from high-fidelity simulations. The network takes five invariants of the mean strain-rate and rotation-rate tensors and outputs the coefficients of ten basis tensors (Pope's integrity basis); a final multiplicative layer combines them, so the predicted tensor is Galilean invariant by construction.
- Published
- Journal of Fluid Mechanics
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