Orateur
Description
Reynolds-averaged Navier–Stokes (RANS) turbulence models are known to perform poorly in predicting the dynamics of Rayleigh–Taylor mixing when turbulence is not fully developed, particularly during the transition from an initially perturbed interface. We investigate the use of data-driven strategies to enhance a simple k–epsilon–S model for this transitional regime. The turbulence model is first embedded within a surrogate physics-informed neural network (PINN), which enables the identification of corrective terms accounting for both parametric errors arising from model calibration and structural errors associated with missing physical processes. The learned corrections are then projected onto the model state variables and relevant flow indicators, leading to explicit analytical modifications of the closure. The resulting fully interpretable corrected model is assessed against an extensive database of direct numerical simulations (DNS) of Rayleigh–Taylor flows. This framework enables improved predictions of the mixing-layer growth during the transition to turbulence and provides a systematic quantification of model uncertainties in the description of Rayleigh–Taylor mixing.