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Intelligent Prediction of Turbulent Flow over Backward-Facing Step using Direct Numerical Simulation Data

Abstract In turbulent flows, there are very complicated nonlinear behaviors that are influenced by many indeterminate factors. Currently, Direct Numerical Simulation (DNS) plays a significant role in turbulent flow simulation modeling due to the improvement of computer technology, but it can still be too expensive and time-consuming. In this study, a multivariate Levenberg-Marquardt (LM) neural network is developed to predict the turbulent flow over backward-facing step using information from a DNS. A multi-task learning technique is used to improve generalization performance and the prediction accuracy of flow field properties with low computational cost. A novel approach is applied to determine the optimum number of hidden units in order to prevent overfitting. Radial Basis Function (RBF) networking is employed to evaluate the effectiveness of the proposed approach in predicting hidden units. The excellent performance of the Levenberg-Marquardt Artificial Neural Network is shown based on the estimation of the flow fields with a large amount of data, such as in the case of the backward-facing step flow. The results of the LM neural network demonstrate that the proposed method provides a practical guideline for the estimation of the number of hidden units essentially needed for Artificial Neural network (ANN) simulations.