Uncertainty and Error Quantification for Data-Driven Reynolds-Averaged Turbulence Modelling with Mean-Variance Estimation Networks

Abstract

Amid growing interest in machine learning, numerous data-driven models have recently been developed for Reynolds-averaged turbulence modelling. However, their results generally show that they do not give accurate predictions for test cases that have different flow phenomena to the training cases. As these models have begun being applied to practical cases typically seen in industry such as in cooling and nuclear, improving or incorporating metrics to measure their reliability has become an important matter. To this end, a novel data-driven approach that uses mean-variance estimation networks (MVENs) is proposed in the present work. MVENs enable efficient computation as a key advantage over other uncertainty quantification (UQ) methods - during model training with maximum likelihood estimation, and UQ with a single forward propagation. Furthermore, the predicted standard deviation is also shown to be an appropriate proxy variable for the error in the mean predictions, thereby providing error quantification (EQ) capabilities. The new tensor-basis neural network with MVEN integration was compared with its popular underlying data-driven model by evaluating them on two test cases: a separated flow and a secondary flow. In both cases, the proposed approach preserved the predictive accuracy of the underlying data-driven model, while efficiently providing reliability metrics in the form of UQ and EQ. For the purposes of turbulence modelling, this work demonstrates that the UQ and EQ mechanisms in MVENs enable risk-informed predictions to be made and therefore can be insightful reliability measures in more complex cases, such as those found in industry.