A HyChem Model of Polyoxymethylene Dimethyl Ether 5 Optimized with a Differential Evolution Algorithm.

ABSTRACT: This study develops a machine learning-optimized combustion model for the ether-based fuel PODE₅ based on the HyChem approach, which decouples fuel combustion steps into lumped pyrolysis and detailed oxidation. Due to its main-chain oxygen and high oxygen-to-carbon ratio, PODE fuels differ from conventional hydrocarbon and methyl ester fuels that HyChem has been applied to, requiring special consideration for oxygenated fuel decomposition intermediates such as CH₂O, CH₃OH, and HOCHO. During the model development, the differential evolution algorithm was used to adjust the reaction rate parameters of the HyChem model based on the ignition delay time. By defining optimization objectives as ignition delay times predicted by a reference model, the use of the di

SUBMITTER: Li G 

PROVIDER: S-EPMC12004197 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature