Prediction of Hydrogen–Oxygen Combustion Ignition Delay Time Based on Gaussian Process Regression

To address the insufficient prediction accuracy of traditional Arrhenius-type empirical correlations for hydrogen–oxygen combustion ignition delay time (IDT) over wide operating conditions, this paper proposes a data-driven prediction method based on Gaussian Process Regression (GPR). Based on 413 sets of shock tube and rapid compression machine experimental data, with temperature, pressure, and equivalence ratio as input features, an IDT prediction model was constructed. The GPR model adopts the Matérn 3/2 kernel function combined with the Automatic Relevance Determination (ARD) strategy for hyperparameter optimization. As a benchmark comparison, LASSO regression was employed for feature selection and parameter estimation of the Arrhenius empirical correlation. On a stratified sampling test set, the GPR model achieved a coefficient of determination R2 of 0.8587 (Rln2 = 0.9271 on logarithmic scale), significantly outperforming the Arrhenius model’s R2 of 0.5422 (Rln2 = 0.7027). In 50 random-split stability tests, GPR yielded a mean R2 of 0.9180 with a standard deviation of only 0.0333, demonstrating excellent robustness. This study provides a valuable reference for ignition performance prediction of hydrogen–oxygen combustion systems.