A probabilistic, data-driven closure model for RANS simulations with aleatoric, model uncertainty

arXiv:2307.02432v2 Announce Type: replace-cross
Abstract: We propose a data-driven, closure model for Reynolds-averaged Navier-Stokes (RANS) simulations that incorporates aleatoric, model uncertainty. The proposed closure consists of two parts. A parametric one, which utilizes previously proposed, neural-network-based tensor basis functions dependent on the rate of strain and rotation tensor invariants. This is complemented by latent, random variables which account for aleatoric model errors. A fully Bayesian formulation is proposed, combined with a sparsity-inducing prior in order to identify regions in the …

abstract arxiv cs.lg data data-driven functions network parametric physics.comp-ph physics.flu-dyn rate rotation simulations stat.ml tensor type uncertainty