Quantifying model form uncertainty in Reynolds-averaged turbulence models with Bayesian deep neural networks

Understand

Data-driven methods for improving turbulence modeling in Reynolds-Averaged Navier-Stokes (RANS) simulations have gained significant interest in the computational fluid dynamics community.

• Modern machine learning algorithms have opened up a new area of black-box turbulence models allowing for the tuning of RANS simulations to increase their predictive accuracy.
• While several data-driven turbulence models have been reported, the quantification of the uncertainties introduced has mostly been neglected.
• Uncertainty quantification for such data-driven models is essential since their predictive capability rapidly declines as they are tested for flow physics that deviate from that in the training data.