Efficient modeling of the filtered density function in turbulent sprays using ensemble learning

Abstract

An efficient ensemble learning approach is used for modeling the filtered density function (FDF) of mixture fraction in turbulent evaporating sprays. This is achieved by implementing the state-of-the-art eXtreme Gradient Boosting (XGBoost) and Light Gradient Boosting Machine (LightGBM) algorithms. The results show that ensemble learning models achieve a very high accuracy that is comparable to a deep neural network. Computational requirements are, however, much reduced and of the order of those needed for the computation of a conventional β-FDF. Ensemble learning thus provides a suitable alternative to model FDF statistics and corresponding software for training and a C++ model library are provided.