Development of machine learning models for the prediction of laminar flame speeds of hydrocarbon and oxygenated fuels

Laminar flame speed (LFS) is a key physicochemical property of a premixed fuel/oxidizer mixture, and is critical in the description of complex combustion phenomena. Accurate experimental measurements of LFSs for various fuels have been performed to develop and validate detailed kinetic mechanisms, which in turn are used to predict LFSs under various combustion conditions. However, such procedure is inefficient, especially in large-scale turbulent combustion modeling studies. Based on previous experimental studies of LFSs for various fuels, this work aims to develop a data-driven machine learning (ML) model for the prediction of LFSs of hydrocarbon and oxygenated fuels. Descriptors computed from semi-empirical quantum chemistry methods are used as input in ML models due to the simplicity and computational-efficiency. Pearson correlation analysis is used to select important features, and 5 descriptors are screened as the input features for ML model development. The accuracies and interpretabilities of existing 16 ML algorithms in the prediction of LFSs are compared through systematically evaluated the errors based on the differences between experimental data and model prediction. These ML models include regression trees, support vector machine regression, gaussian process regression, and ensemble trees. An efficient ML model for predicting LFSs of hydrocarbon and oxygenated fuels based on gaussian process regression algorithm is proposed, which exhibits good accuracy in predicting of LFSs for variable pressure, temperature, and equivalence ratio. The dependency of LFSs on the descriptors are also analysed. The developed ML model is fast enough for integration into large-scale computational fluid dynamics for combustion studies.