Thermodynamics-based data-driven combustion modelling for modern spark-ignition engines

Combustion modelling is complicated, computationally expensive, and crucial for the development of modern spark-ignition (SI) engines. This study introduces a novel data-driven approach to improve the predictability of phenomenological SI engine models. First, a physics-based model is used to generate Mass Fraction Burned (MFB) profiles for 1258 precisely controlled knock-limited combustion experiments. To predict these MFB profiles based on the operating conditions, Artificial Neural Networks (ANN), Multiple Output Support Vector Regression (MOSVR), and Multivariate Gaussian Process (MGP) are then applied. Among these, MGP demonstrates superior performance due to the Gaussian-like distribution of the outputs. Further sensitivity analysis using MGP identifies critical inputs that are not engine specific to develop a thermodynamics-based data-driven model. The model demonstrates high accuracy, uses normalised inputs that are independent of engine geometry, and consistently performs well with small datasets. When applied to a different but similarly sized engine, the model accurately predicts the knock-limited spark timing and captures the MFB profile relatively well, showing strong generalisability. This study not only improves the predictability of engine combustion simulations but also establishes a valuable dataset for further development of data-driven models in different engines.