On the use of artificial neural networks to model the performance and emissions of a heavy-duty natural gas spark ignition engine

The use of computational models for internal combustion engine development is ubiquitous. Numerical simulations using simpler to complex physical models can predict engine's performance and emissions, but they require large computational capabilities. By comparison, statistical methodologies are more economical tools in terms of time and resources. This paper investigated the use of an artificial neural network algorithm to simulate the nonlinear combustion process inside the cylinder. Three engine control variables (i.e. spark timing, mixture equivalence ratio, and engine speed) were set as the model inputs. Outputs included peak cylinder pressure and its location, maximum pressure rise rate, indicated mean effective pressure, ignition lag, combustion phasing, burn duration, exhaust temperature, and engine-out emissions (i.e. nitrogen oxides, carbon monoxide, and unburned hydrocarbons). Eighty percent of the experimental data from a heavy-duty natural gas spark ignition engine were utilized to train the model. The perceptions accurately learned the combustion characteristics and predicted engine responses with acceptable errors, evidenced by close-to-unity coefficient of determination and close-to-zero root-mean-square error. Moreover, the regressors captured the effect of key operating variables on the engine response, suggesting the well-trained models successfully identified the complex relationships and can help assist engine analysis. Overall, the neural network algorithm was appropriate for the application investigated in this study.

Automated summary

The paper investigated the use of an artificial neural network algorithm to simulate the nonlinear combustion process inside the cylinder of an internal combustion engine. The neural network accurately learned the combustion characteristics and predicted engine responses with acceptable errors. The well-trained models successfully identified the complex relationships between key operating variables and engine responses, indicating the neural network algorithm's appropriateness for the application studied.