Studying the spatial and temporal evolution in turbulent fl ames 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 fl ame radius evolution and turbulent fl ame speeds for diesel, gas-to-liquids (GTL), and their

50/50 blend (by volumetric composition) under diff erent thermodynamic and turbulence operating conditions. Two ML

models were developed in this study. Model 1 predicts the variations of the fl ame radius with time, equivalence ratio, and

turbulence intensity, whereas model 2 predicts the variations of the turbulence fl ame 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 eff ects of operating parameters on the premixed turbulent fl ames. 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 fl uid dynamics (CFD) outputs with high precision at diff erent fl ame 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 fl ames a posteriori.