Genetic algorithm-assisted multi-objective optimization for developing a Multi-Wiebe Combustion model in ammonia-diesel dual fuel engines

Abstract

Direction Injection Dual-Fuel (DIDF) engines fueled with ammonia and diesel are identified as a promising solution for decarbonizing large-scale Compression Ignition (CI) engines. This study addresses the research gap of missing a parametric model for simulating the combustion process in DIDF CI engines using ammonia and diesel. Multi-objective optimization and genetic algorithms are applied to generate a parametric Multi-Wiebe Combustion (MWC) model based on experimental results from a NH₃-diesel DIDF CI engine. The innovative approach supports one-dimensional engine modeling with NH₃-diesel combustion in GT-Power, enhancing the understanding of direct injection timings, fuel interactions, and combustion dynamics. Key findings include the impact of dual-fuel injection timings and fuel ratios on ignition delay, individual combustion phase durations, and heat release rate, providing a quantitative description of combustion behavior under varying conditions. The validation results show that with injection timing variations from −17.5 to −10 CAD aTDC and NH₃ energy ratios ranging from 40 % to 60 %, relative errors remain below 5 % for key performance indicators such as pressure and efficiency. This study proposes a methodology to generate an accurate combustion model – the MWC model - for one-dimensional dual-fuel engine simulation, aiding in calibrating scaled-up DIDF CI engines and guiding further engine designs.