A sub-grid scale model for Burgers turbulence based on the artificial neural network method

Abstract

The present study proposes a sub-grid scale (SGS) model for the one-dimensional Burgers turbulence based on the neural network and deep learning method. The filtered data of the direct numerical simulation is used to establish the training data set, the validation data set, and the test data set. The artificial neural network (ANN) method and Back Propagation method are employed to train parameters in the ANN. The developed ANN is applied to construct the sub-grid scale model for the large eddy simulation (LES) of the Burgers turbulence in the one-dimensional space. The proposed model well predicts the time correlation and the space correlation of the Burgers turbulence.