Combustion Chamber Screening and Multi-Objective Optimization of Combustion System Parameters for a Locomotive Diesel Engine

Locomotive diesel engines remain necessary for non-electrified railway services, shunting, and emergency operations, motivating efforts to reduce fuel use and exhaust emissions. This study combined combustion chamber selection with injection and intake optimization for a locomotive diesel engine. Three bowl designs were evaluated using a three-dimensional CFD model in AVL FIRE at 1200 r/min under high load; the model was validated against global pressure, heat release, and emission measurements for the baseline configuration at this same operating condition. The deep ω-shaped chamber provided the best overall predicted performance and was retained for subsequent optimization. A Latin hypercube design generated 240 cases spanning nozzle hole diameter and count, spray included angle, injection timing, intake pressure, and EGR rate. CFD responses were used to train a backpropagation neural network, after which NSGA-II and TOPSIS were applied under a 23 MPa peak-pressure constraint. At this single operating point, CFD re-evaluation of the selected compromise predicted reductions of 1.92%, 68.28%, and 29.27% in BSFC, NOx, and soot, respectively, relative to the baseline, with a peak pressure of 22.54 MPa. The model-resolved fields further suggested smaller fuel-rich regions and reduced late-combustion high-temperature and pollutant-accumulation regions. These spatial observations are model-dependent because spatially resolved validation is unavailable and the RANS/ECFM-3Z framework does not resolve the detailed near-wall flame and quenching structure. The reported improvements therefore apply only to the investigated operating point and design space.