Machine learning-based prediction of species mass fraction and flame characteristics in partially premixed turbulent jet flame

TL;DR

This paper integrates machine learning with large eddy simulation to accurately and efficiently predict species mass fractions and flame characteristics in turbulent jet flames, demonstrating significant speed improvements over traditional methods.

Contribution

The study introduces neural network models trained on LES data to enhance real-time prediction of combustion properties, outperforming traditional LES in speed and accuracy.

Findings

Neural Network model achieved highest accuracy among tested ML models.

NN model was approximately 17.25 times faster than traditional LES simulations.

ML models showed promise despite limitations with large dataset fluctuations.

Abstract

This study explores the integration of machine learning (ML) techniques with large eddy simulation (LES) for predicting species mass fraction and flame characteristics in partially premixed turbulent jet flames. The LES simulations, conducted using STAR-CCM+ software, employed the Flamelet Generated Manifold (FGM) approach to effectively capture the interactions between the turbulence and chemical reactions, providing high-fidelity data on flame behaviour and pollutant formation. The simulation was based on the Sandia Flame D specification, utilizing a detailed mesh to accurately represent flow features and flame dynamics. To enhance real-time prediction capabilities, three ML models, Neural Networks (NN), Linear Regression (LR), and Decision Tree Regression (DTR), were trained on the LES data. Comparative analysis using metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), Pearson Coefficient (PC), and R-squared (R2) identified the NN model as the most effective one. The NN model demonstrated high accuracy in predicting species mass fractions and flame patterns, significantly outperforming traditional LES solvers in terms of computational efficiency. The study also highlighted the considerable computational speedup achieved by the NN model, making it approximately 17.25 times faster than traditional LES solvers. Despite some limitations, such as handling large dataset fluctuations, the ML models have shown promise for future applications in combustion simulations.