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This study presents an optimization method for internal combustion engines using an artificial neural network (ANN) combined with a genetic algorithm (GA) to enhance fuel efficiency and reduce emissions in a novel direct dual fuel stratification (DDFS) strategy. The ANN-GA method significantly outperforms traditional computational fluid dynamics (CFD)-GA approaches by reducing computational time by over 75% while improving accuracy and flexibility in optimizing engine performance. The research highlights th

Author

hb21010

Language

EN