Integrated analysis of CFD simulation data with K-means clustering algorithm for soot formation under varied combustion conditions

Abstract Computational fluid dynamics (CFD) modelling is a scientific tool to provide fluid dynamics and chemical simulation that facilitates understanding of the complex combustion phenomenon in engine studies. With the advance of Machine Learning (ML) technology, the big data from CFD results can be intelligently recognized and classified, thus ease the data post-processing. This study proposed an integrated analysis that uses CFD simulation results of scalar distributions and K-means clustering algorithm to optimally partition engine combustion chamber into different zones. Therefore, the space of combustion chamber was automatically divided into light soot zones and heavy soot zones based on the clustering results on local equivalence ratio (ER) and temperature. Consequently, the surveys of soot mitigation by Reactivity Controlled Compression Ignition (RCCI) engines combustion mode were carried out as well as corresponding sooting tendency by CFD numerical study. The localized soot depositions in each zone under varied combustion boundaries were compared, hence improving the development of control strategy with numerical modellings and machine learning techniques.