Paper ID: 2306.13370

Physics-constrained Random Forests for Turbulence Model Uncertainty Estimation

Marcel Matha, Christian Morsbach

To achieve virtual certification for industrial design, quantifying the uncertainties in simulation-driven processes is crucial. We discuss a physics-constrained approach to account for epistemic uncertainty of turbulence models. In order to eliminate user input, we incorporate a data-driven machine learning strategy. In addition to it, our study focuses on developing an a priori estimation of prediction confidence when accurate data is scarce.

Submitted: Jun 23, 2023