Development of Machine Learning Models for Studying the Premixed Turbulent Combustion of Gas-To-Liquids (GTL) Fuel Blends

Abstract

Studying the spatial and temporal evolution in turbulent flames represents one of the most challenging problems in the combustion community. Based on previous 3D numerical analyses, this study aims to develop data-driven machine learning (ML) models for predicting the flame radius evolution and turbulent flame speeds for diesel, gas-to-liquids (GTL), and their 50/50 blend (by volumetric composition) under different thermodynamic and turbulence operating conditions. Two ML models were developed in this study. Model 1 predicts the variations of the flame radius with time, equivalence ratio, and turbulence intensity, whereas model 2 predicts the variations of the turbulence flame speed with the operating parameters. The k-fold cross-validation technique is used for model training, and the developed neural network-based model is used to investigate the effects of operating parameters on the premixed turbulent flames. In addition, the possible minimum and maximum values of responses at the corresponding operating parameters are found using a genetic algorithm (GA) approach. Model 1 could capture the computational fluid dynamics (CFD) outputs with high precision at different flame radiuses and time instants with a maximum absolute error percentage of 5.46%. For model 2, the maximum absolute error percentage was 6.58%. Overall, this study demonstrates the applicability and promising performance of the proposed ML models, which will be used in subsequent research to analyze turbulent flames a posteriori.