Abstract This study developed a deep learning architecture to predict combustion characteristics and exhaust emissions using a prior Soot-NOx coupled mechanism. Three-dimensional combustion simulations quantified the effects of operating conditions (ambient temperature, ambient pressure, exhaust gas recirculation rate, fuel injection timing, and fuel injection quantity) on combustion and emissions. Increased ambient temperature, ambient pressure, fuel injection quantity, and advanced fuel injection timing advanced combustion phasing, raising in-cylinder pressure and rate of heat release while reducing ignition delay. Conversely, a higher exhaust gas recirculation rate prolonged ignition delay and weakened combustion intensity due to dilution and thermal effects. NOx emissions depended heavily on peak temperature, pressure, and residence time, while soot was sensitive to local oxygen and mixture quality. The characteristic soot-NOx trade-off was confirmed. Based on 0D analysis, a Long Short-Term Memory model was selected for its superior temporal learning capabilities. Applied to the Three-dimensional dataset, the Long Short-Term Memory model achieved an R2 0.96 for combustion characteristics and R2 0.98 for emission formation (NOx, soot) and oxidation processes. These results indicate the model effectively captures the non-linear relationships within the data.
Shin et al. (Fri,) studied this question.