基于多压力变温实验的缸套-活塞环摩擦因数预测研究  

Prediction of Cylinder Liner-Piston Ring Friction Coefficient Based on Multi-Pressure Variable Temperature Experiments

作　　者：马轩 王哲 刘晓日[1] MA Xuan;WANG Zhe;LIU Xiaori(School of Energy and Environmental Engineering,Hebei University of Technology,Tianjin 300401,China;Tianjin Research Institute for Advanced Equipment,Tsinghua University,Tianjin 300300,China)

机构地区：[1]河北工业大学能源与环境工程学院,天津300401 [2]清华大学天津高端装备研究院,天津300300

出　　处：《润滑与密封》2023年第10期68-73,85,共7页Lubrication Engineering

基　　金：国家自然科学基金青年基金项目(52005149);河北省自然科学基金面上项目(E2022202026)。

摘　　要：为探究内燃机中缸套-活塞环摩擦副在复杂工况下的摩擦因数变化规律,根据实际工况下的温度和压力,使用SRV高温摩擦试验机模拟内燃机系统缸套-活塞环之间的往复式摩擦运动,获得不同温度和压力下的摩擦因数,并测试不同温度下润滑油的黏度。以摩擦接触面温度、载荷和润滑油黏度为输入,摩擦因数为输出,构建BP神经网络预测模型。结果表明,BP神经网络具有较高的预测精度,测试集误差值稳定在1%以内,满足工程精度要求。In order to investigate the friction coefficient variation law of the cylinder liner-piston ring friction pair in the internal combustion engine under complex working conditions,the reciprocating friction motion between the cylinder liner-piston ring of the internal combustion engine system was simulated using the SRV high-temperature tribotester according to the temperature and pressure of actual working conditions.The friction coefficient of the cylinder liner-piston ring friction pair under different temperature and pressure was obtained,and the viscosity of lubricating oil at different temperatures was tested.A BP neural network prediction model was constructed with friction contact surface temperature,load and lubricant viscosity as input and friction coefficient as output.The results demonstrate that the BP neural network has a high prediction accuracy,and the error value of the test set is stable within 1%,which meets the engineering accuracy requirements.

关 键 词：缸套-活塞 内燃机 摩擦因数 神经网络 润滑性能 

分 类 号：TH117.1[机械工程—机械设计及理论]