Physics-constrained Random Forests for Turbulence Model Uncertainty Estimation

• URL: http://arxiv.org/abs/2306.13370v2
• Date: Tue, 11 Jul 2023 16:15:40 GMT
• Title: Physics-constrained Random Forests for Turbulence Model Uncertainty Estimation
• Authors: Marcel Matha and Christian Morsbach
• Abstract summary: We discuss a physics-constrained approach to account for uncertainty of turbulence models. In order to eliminate user input, we incorporate a data-driven machine learning strategy.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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