Co-optimization of miller degree and geometric compression ratio of a large-bore natural gas generator engine with novel Knock models and machine learning

Abstract

Large-bore natural gas reciprocating generator engines will play crucial roles in future low-carbon energy systems. Knock, as an abnormal combustion phenomenon, is a bottleneck for long time to restrict the engine performance improvement. Development and application of the reliable knock model can contribute the predictive engine control and optimization to further improve the engine efficiency and performance. The biggest challenge of developing the predictive knock model is the stochastic nature of the knock phenomenon. To bridge the technological gap, this study aims to develop a novel predictive knock model for a large-bore natural gas engine and showcase its application for co-optimization of the engine design and control parameters, together with the machine learning. The knock model, taking the effects of hot spots heat release rate and energy density into account, is calibrated by the statistical phenomenological knock factor. The 1-D engine simulation model embedded with the knock model is built and calibrated. The data-driven model based on machine learning is developed and used to predict the relation between the key parameters and the engine performance. The genetic algorithms are employed to achieve the multi-objectives global optimization of geometric compression ratio, Miller degree and other control parameters of the engine to reduce the exhaust gas temperature and improve the engine performance. The co-optimized results show that the engine exhaust gas temperature can be remarkably lowered and the indicated thermal efficiency is increased by 1–3% depending on the operational conditions, without degrading the engine power output and increasing the exhaust NOx emissions.