A lower-dimensional approximation model of turbulent flame stretch and its related quantities with machine learning approaches

Abstract

Flame stretch and its related quantities are three-dimensional (3D), while most planar imaging techniques, widely used in turbulent combustion, can only provide lower-dimensional information of these quantities. In the present work, based on a direct numerical simulation (DNS) database, artificial neural network (ANN) and random forest (RF) models were developed to predict the 3D flame stretch and its related quantities such as the tangential strain rate, displacement velocity, and curvature from lower-dimensional information that can be accessed experimentally. It was found that the performance of the RF model is better than that of the ANN model. In the RF model, the correlation coefficients between the modeled and actual values are more than 0.97, and the determination coefficients are over 0.95. The model performance deteriorates with increasing turbulent intensity. The probability density functions of various quantities predicted by the RF model are in good agreement with those of the DNS. Compromising the model performance and the computational cost, a simplified RF model was proposed by using a few optimal input features. It was found that the discrepancies between the modeled and actual values mainly occur in highly curved regions, which explains the observation that the prediction errors increase with increasing turbulent intensity. Overall, the predictions of the simplified RF model agree well with the actual values.