计入油膜力作用的矿用发动机活塞-轴系系统性能研究  

Investigation of Performance of Mine Engine Piston-crankshaft System under Oil Film Forces

作　　者：魏勇刚[1] 孟凡明[2] 

机构地区：[1]煤炭科学研究总院太原分院,山西太原030006 [2]清华大学摩擦学国家重点实验室,北京100084

出　　处：《润滑与密封》2006年第6期42-44,共3页Lubrication Engineering

摘　　要：利用径向基神经网络(RBFNN)对油膜力作用下的矿用发动机活塞-轴系系统性能进行了研究。首先,对发动机活塞-轴系系统进行分解和重构,然后通过RBFNN对现有数据库中由试验测得的燃气压力和由计算得到的活塞组件的油膜力进行训练,并将训练的结果纳入到活塞-轴系的动力学建模中,从而获得了活塞-轴系的系统性能。结果表明,当计入油膜力作用时,在完整工作循环内,活塞-轴系中存在着极限环等非线性动力学现象。通过对一矿用发动机仿真结果的验证,证实了该方法的有效性。When considering oil film force for piston assemble, piston-crankshaft system performances of an combustion engine used in a coal mine was investigated by the use of radial base function neural network （RBFNN）. The system＇ s decomposition and reconstruction were firstly discussed. Then, RBFNN was designed to reconstruct the data taken from a special database, which was constituted of the cylinder gas pressure measured and frictional force and load capacity of oil film force calculated by lubrication equation. After the well-trained RBFNN was achieved, train results were combined with the dynamic equations in above system,and further system performances were predicted, validity of RBFNN was demonstrated by simulating an engine used in a coal mine. Result shows there is nonlinear dynamics in the piston - crankshaft system when considering oil film force during a work cycle.

关 键 词：预测 活塞-轴系系统 油膜力 RBF神经网络 有效性 

分 类 号：TK413.33[动力工程及工程热物理—动力机械及工程]