Machine Learning Integration With Combustion Physics to Develop a Composite Predictive Model for Reactivity Controlled Compression Ignition Engine

Abstract

Abstract Phasing of combustion metrics close to the optimum values across operation range is necessary to avail benefits of reactivity controlled compression ignition (RCCI) engines. Parameters like start of combustion occurrence crank angle (CA) (θsoc), occurrence of burn rate fraction reaching 50% (θ50), mean effective pressure from indicator diagram (IMEP), etc. are described as combustion metrics. These metrics act as markers for the macroscopic state of combustion. Control of these metrics in RCCI engine is relatively complex due to the nature of ignition. As direct combustion control is challenging, alternative methods like combustion physics-derived models are a subject of research interest. In this work, a composite predictive model was proposed by integrating trained random forest (RF) machine learning and artificial neural networks (ANNs) to combustion physics-derived modified Livengood–Wu integral, parametrized double-Wiebe function, autoignition front propagation speed-based correlations, and residual gas fraction model. The RF machine learning established a correlative relationship between physics-based model coefficients and engine operating condition. The ANN developed a similar correlation between residual gas fraction parameters and engine operating condition. The composite model was deployed for the predictions of θsoc, θ50, and IMEP as RCCI engine combustion metrics. Experimental validation showed an error standard deviation (σ68.3,err) of 0.67°CA, 1.19°CA, 0.223 bar and symmetric mean absolute percentage error of 6.92%, 7.87%, and 4.01% for the predictions of θsoc, θ50, and IMEP, respectively, on cycle to cycle basis. Wide range applicability, lesser experiments for model calibration, low computational costs, and utility for control applications were the benefits of the proposed predictive model.