Machine learning assisted analysis of heat transfer characteristics of a heavy duty natural gas engine
2022-01-0572
03/29/2022
- Event
- Content
- Heat transfer affects engine performance, efficiency, and emissions. Hence, it was essential to investigate the heat transfer characteristics after converting a compression ignition engine to a spark ignition and natural gas. The heat transfer for each engine speed and load was calculated based on Woschni’s correlation. As the number of experiments that provided engine response was limited, an artificial neural network approach was proposed to predict the engine heat transfer characteristics and aid in the analysis. The good agreement between the experimental results and data predicted by the machine learning algorithm validated the predictive capability of the artificial neural network model. The results of the data-driven analyses suggested that the heat transfer increased as the spark timing advanced. Moreover, increasing the engine speed reduced the heat flux form the combustion chamber to the engine coolant. Further, leaning the mixture lowered the heat transfer rate to the boundary compared to stoichiometric operation, which was beneficial for engine efficiency. But most important, the total heat transfer of this modified engine was higher than that expected from a conventional spark ignition engine, due to the greater intensity of in-cylinder turbulence and the larger surface to volume ratio of the combustion chamber.
- Citation
- Liu, J., Dumitrescu, C., and Ulishney, C., "Machine learning assisted analysis of heat transfer characteristics of a heavy duty natural gas engine," SAE Technical Paper 2022-01-0572, 2022, .