摘要
The performance and particulate emission of a diesel engine are affected by the consumption of lubricating oil. Most studies on oil consumption mechanism of the cylinder have been done by using the experimental method, however they are very costly. Therefore, it is very necessary to study oil consumption mechanism of the cylinder and obtain the accurate results by the calculation method. Firstly, four main modes of lubricating oil consumption in cylinder are analyzed and then the oil consumption rate under common working conditions are calculated for the four modes based on an engine. Then, the factors that affect the lubricating oil consumption such as working conditions, the second ring closed gap, the elastic force of the piston rings are also investigated for the four modes. The calculation results show that most of the lubricating oil is consumed by evaporation on the liner surface. Besides, there are three other findings： （1） The oil evaporation from the liner is determined by the working condition of an engine; （2） The increase of the ring closed gap reduces the oil blow through the top ring end gap but increases blow-by; （3） With the increase of the elastic force of the ring, both the left oil film thickness and the oil throw-off at the top ring decrease. The oil scraping of the piston top edge is consequently reduced while the friction loss between the rings and the liner increases. A neural network prediction model of the lubricating oil consumption in cylinder is established based on the BP neural network theory, and then the model is trained and validated. The main piston rings parameters which affect the oil consumption are optimized by using the BP neural network prediction model and the prediction accuracy of this BP neural network is within 8%, which is acceptable for normal engineering applications. The oil consumption is also measured experimentally. The relative errors of the calculated and experimental values are less than 10%, verifying the validity of the simulation results. Applying the established simulation model and the validated BP network model is able to generate numerical results with sufficient accuracy, which significantly reduces experimental work and provides guidance for the optimal design of the piston rings diesel engines.
The performance and particulate emission of a diesel engine are affected by the consumption of lubricating oil. Most studies on oil consumption mechanism of the cylinder have been done by using the experimental method, however they are very costly. Therefore, it is very necessary to study oil consumption mechanism of the cylinder and obtain the accurate results by the calculation method. Firstly, four main modes of lubricating oil consumption in cylinder are analyzed and then the oil consumption rate under common working conditions are calculated for the four modes based on an engine. Then, the factors that affect the lubricating oil consumption such as working conditions, the second ring closed gap, the elastic force of the piston rings are also investigated for the four modes. The calculation results show that most of the lubricating oil is consumed by evaporation on the liner surface. Besides, there are three other findings： （1） The oil evaporation from the liner is determined by the working condition of an engine; （2） The increase of the ring closed gap reduces the oil blow through the top ring end gap but increases blow-by; （3） With the increase of the elastic force of the ring, both the left oil film thickness and the oil throw-off at the top ring decrease. The oil scraping of the piston top edge is consequently reduced while the friction loss between the rings and the liner increases. A neural network prediction model of the lubricating oil consumption in cylinder is established based on the BP neural network theory, and then the model is trained and validated. The main piston rings parameters which affect the oil consumption are optimized by using the BP neural network prediction model and the prediction accuracy of this BP neural network is within 8%, which is acceptable for normal engineering applications. The oil consumption is also measured experimentally. The relative errors of the calculated and experimental values are less than 10%, verifying the validity of the simulation results. Applying the established simulation model and the validated BP network model is able to generate numerical results with sufficient accuracy, which significantly reduces experimental work and provides guidance for the optimal design of the piston rings diesel engines.
基金
supported by National Natural Science Foundation of China (Grant No. 50975192)
Specialized Research Foundation for the Doctoral Program of Higher Education of China (Grant No.20090032110001)
