Data-Driven Identification of Reduced-State Mechanisms for Ethanol Diffusion Flames Using PCA–ANN

This study introduces PCREM, a principal-component analysis/artificial neural network (PCA-ANN) framework for identifying reduced ethanol-combustion mechanism topologies from a two-dimensional OpenFOAM ethanol pool-fire analogue simulated using the 2016-12-14 San Diego Mechanism. For PCA and species ranking, 17 species are selected from the San Diego-generated fields according to the modified Millán-Merino retained-species set, with temperature included as an additional thermochemical scalar. PCA reduces the 18-dimensional thermochemical state to 10 principal components capturing 99.939% of the cumulative variance, while ANN models reconstruct temperature, species mass fractions, principal-component source terms, and diffusion information. A unity-weighted sensitivity analysis is subsequently used to rank the influence of the original thermochemical scalars on the selected quantities of interest. Based on this ranking, three candidate reduced-state mechanisms are proposed: PCREM-16, removing CH₃ CH₂O; PCREM-15, additionally removing CH₃CHOH; and PCREM-13, further removing HO₂ and C₂ H₂. Reaction-consistency analysis distinguishes directly closed pathways from those requiring ANN-based retrieval. The results establish an a priori, data-informed framework for reduced ethanol pool-fire chemistry and provide the basis for subsequent a posteriori OpenFOAM validation and computational-performance assessment.