Assessment of Neural Network Augmented Reynolds Averaged Navier Stokes Turbulence Model in Extrapolation Modes

Abstract

A machine-learned (ML) model is developed to enhance the accuracy of turbulence transport equations of Reynolds Averaged Navier Stokes (RANS) solver and applied for periodic hill test case, which involves complex flow regimes, such as attached boundary layer, shear-layer, and separation and reattachment. The accuracy of the model is investigated in extrapolation modes, i.e., the test case has much larger separation bubble and higher turbulence than the training cases. A parametric study is also performed to understand the effect of network hyperparameters on training and model accuracy and to quantify the uncertainty in model accuracy due to the non-deterministic nature of the neural network training. The study revealed that, for any network, less than optimal mini-batch size results in overfitting, and larger than optimal batch size reduces accuracy. Data clustering is found to be an efficient approach to prevent the machine-learned model from over-training on more prevalent flow regimes, and results in a model with similar accuracy using almost one-third of the training dataset. Feature importance analysis reveals that turbulence production is correlated with shear strain in the free-shear region, with shear strain and wall-distance and local velocity-based Reynolds number in the boundary layer regime, and with streamwise velocity gradient in the accelerating flow regime. The flow direction is found to be key in identifying flow separation and reattachment regime. Machine-learned models perform poorly in extrapolation mode, wherein the prediction shows less than 10% correlation with Direct Numerical Simulation (DNS). A priori tests reveal that model predictability improves significantly as the hill dataset is partially added during training in a partial extrapolation model, e.g., with the addition of only 5% of the hill data increases correlation with DNS to 80%.