农用柴油机活塞环组机油消耗和窜气的灰色关联分析与预测

Grey relation analysis and prediction of lube oil consumption and crankcase blow-by in piston ring pack for agricultural diesel engine

活塞环组的润滑和密封性能直接影响柴油机的动力性、经济性和排放特性,机油消耗量(lube oil consumption,LOC)和窜气量(crankcase blow-by,CB)是评估活塞环组润滑和密封性能优劣的主要指标。为探究影响农用柴油机活塞环组LOC和CB的影响因素及LOC和CB对影响因素的敏感程度,该研究提出农用柴油机活塞环组LOC和CB的多影响因素系统分析及预测方法。以YNF40农用柴油机为对象,首先搭建柴油机LOC和CB测试台架,建立活塞环组运动学与动力学仿真模型并验证模型的精确度和可靠度,然后采用灰色关联分析(grey relation analysis,GRA)得到不同影响因素的灰色关联度(grey relational grade,GRG),并基于此进行影响因素敏感性分析,最后基于BP神经网络建立不同转速工况下LOC和CB瞬态变化的时间序列预测模型。多因素GRG分析结果表明:对活塞环组LOC和CB影响最敏感和最不敏感的因素分别是活塞第一环槽下边缘倒角和活塞第三环槽背隙,其对应的GRG分别为0.89279和0.58361,得到目标与影响因素敏感程度的降序排列为:第一环槽下边缘倒角、顶环开口间隙、第一环槽下侧环岸间隙、第二环槽侧隙、第二环槽下边缘倒角、第二环开口间隙、第三环槽上边缘倒角、油环开口间隙、第二环槽上边缘倒角、第一环槽背隙、顶环切向弹力、第三环槽上侧环岸间隙、顶环环槽下侧轮廓倾角、第二环槽下侧环岸间隙、第一环槽上侧环岸间隙、配缸间隙、第三环槽侧隙、第一环槽上边缘倒角、顶环环槽上侧轮廓倾角、第一环槽侧隙、第二环槽上侧环岸间隙、第三环槽下边缘倒角、第二环槽背隙、第三环槽下侧环岸间隙、顶环上端缩减量、第三环槽背隙。LOC和CB随活塞第一环槽下边缘倒角和活塞第三环槽背隙变化的平均标准差分别为2.81、3.90和0.003、0.209。各影响因素与LOC和CB的GRG越大,LOC和CB随影响因素变化的平均标准差越大,对影响因素越敏感。BP神经网络时间序列预测结果表明:预测值与真实值的变化趋势较一致且都分布在95%置信带以内,模型具有较高的预测精度。研究结果可为农用柴油机提高热效率、减少和控制碳排放提供有效指导。

The piston ring pack is one of the most important components to affect the power,economy and emission of a diesel engine.Among them,lube oil consumption(LOC)and crankcase blow-by(CB)are the main indicators to evaluate the lubrication and sealing performance of the piston ring pack.In this study,the multi-factor analysis was implemented to predict the influencing factors on the LOC,CB and the sensitivity between them in the piston ring pack for an agricultural diesel engine.A YNF40 diesel engine was taken as the research object.Firstly,the test bench of LOC and CB was built for the diesel engine.The kinematics and dynamics models were established using hydrodynamic lubrication and gas flow theoretical of piston pack friction subsets,technical and structural parameters.A series of experimental tests were also carried out to verify the accuracy and reliability of the simulation.Secondly,the grey relational grade(GRG)of different influencing factors was obtained by grey relation analysis(GRA).The sensitivity analysis between the influencing factors and LOC-CB was then carried out using GRG calculation and ranking.Finally,the time-series prediction models were established for the transient variations of LOC and CB under different speed conditions using a back propagation(BP)neural network.The multi-factor GRG analysis indicated that the most sensitive influencing factor on the LOC and CB in the piston ring pack for a diesel engine was the chamfer of the bottom edge of the 1st ring groove of the piston,whereas,the most insensitive influencing factor was the back clearance of the 3rd ring groove of piston.The maximum GRG between LOC and CB and the chamfer of the bottom edge of the 1st ring groove of the piston was 0.89279,whereas,the minimum GRG between LOC and CB and the back clearance of the 3rd ring groove of the piston was 0.58361.The descending order of sensitivity was obtained between the target and influencing factors.The LOC and CB gradually increased in the piston ring pack for a diesel engine,as the chamfer of the bottom edge of the 1st ring groove of the piston increased.The back clearance of the 3rd ring groove of the piston shared no significant effect.The average standard deviation of the variation of LOC and CB with the chamfer of the bottom edge of the 1st ring groove of the piston were 2.81,and 3.90,respectively.By contrast,the average standard deviation of the variation of LOC and CB with the back clearance of the 3rd ring groove of the piston were 0.003 and 0.209,respectively.Therefore,the larger the GRG between LOC and CB,and each influencing factor were,the larger the mean standard deviation of LOC and CB with the influencing factors were,and the more sensitive the objectives were to the influencing factors.The time-series prediction showed that there was a better consistent trend of the predicted and actual values on the transient variations of LOC and CB.The prediction accuracy and reliability of the time series prediction model were higher than before,which were distributed within the 95%confidence band.The research findings can provide effective guidance to improve the thermal efficiency with the less carbon emissions of agricultural diesel engines.