A PINN-derived analytical correction to the dissipation-production coefficient C_ε0 in a k-ε-b RANS model captures the Rayleigh-Taylor transition to turbulence by coupling dissipation to the Froude number and directed-to-total kinetic energy ratio.
As can be seen, the calibrated coefficients obtained from the different approaches—regression, Bayesian calibration, and PINN-C 0D or 1D— are in good agreement
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Learning Turbulence Closures with Physics-Informed Neural Networks for the Rayleigh-Taylor Transition to Turbulence
A PINN-derived analytical correction to the dissipation-production coefficient C_ε0 in a k-ε-b RANS model captures the Rayleigh-Taylor transition to turbulence by coupling dissipation to the Froude number and directed-to-total kinetic energy ratio.