Combustion system optimization for the integration of e-fuels (Oxymethylene Ether) in compression ignition engines

•CFD-guided optimization of an engine combustion system using Oxymethylene Ether.•Obtention of a combustion system configuration adapted to OME properties.•Optimized system maximized efficiency and decreased NOx for the low-sooting fuel.•A neural network model was trained from the obtained data for sensitivity analysis. In this study, a numerical methodology for the optimization of the combustion chamber in compression ignited engines using OME as fuel is presented. The objective is to obtain a dedicated combustion system for an engine that is fueled with this alternative fuel improving the efficiency and reducing the emissions of NOx. This article proposes the integration between the optimization algorithm and CFD codes to evaluate the behavior of an engine fuelled with the low sooting fuel OME. Based on a diesel model validated against experimental data, a further model for OME fuel was implemented for evaluating the performance of the engine. The particle swarm algorithm (PSO) was modified based on the Novelty Search concepts and used as optimization algorithm. Several tools are coupled in order to create each CFD case where all the tools and optimization algorithm are coupled in a routine that automates the entire process. The result is an optimized combustion system that provides an increase of the efficiency (about 2.2%) and a NOx reduction (35.7%) in comparison with the baseline engine with conventional fuel. In addition, a neuronal network was trained with all the results of all simulations performed during the optimization process, studying the influence of each parameter on the emissions and efficiency. From this analysis it was concluded that the EGR rate and injection pressure affects the NOx emissions with a range of variability of 63% and 38% respectively.