Exploring Machine Learning Strategies for RANS Uncertainty Quantification

This work builds the understanding of the critical machine learning considerations when predicting the uncertainty bounds for a Reynolds Averaged Naiver-Stokes (RANS) model. These bounds are created with eigenvalue perturbations to the Reynolds stress anisotropy tensor, predicted by a random forest trained on high-fidelity data that exhibits similar flow characteristics to those being studied with the low-fidelity approach. The first part of this work explores the hyperparameters of the random forest, the utilization of weak learners, and the optimal strategy for training applied to an asymmetric diffuser. This exploration provided insight into the random forests' sensitivity to different variables and dataset divisions, and the overall accuracy of eigenvalue perturbations. These insights led to restructuring the machine learning approach to two separate objectives summarized as a classification and regression problem. We seek to classify different areas of the flow field into universal regimes and then determine how much to perturb the corresponding fields from the classified insights and other flow characteristics. The second part of this work introduces the classification aspect with primary results applied to an asymmetric diffuser, jet-data, and other compressible flows.