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首页> 外文会议>American Society of Mechanical Engineers(ASME)/Society of Tribologists and Lubrication Engineers(STLE) International Joint Tribology Conference 2004(IJTC 2004) vol.1 pt.A; 20041024-27; Long Beach,CA(US) >PROBABILISTIC MAIN BEARING PERFORMANCE FOR AN INTERNAL COMBUSTION ENGINE
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PROBABILISTIC MAIN BEARING PERFORMANCE FOR AN INTERNAL COMBUSTION ENGINE

机译：内燃发动机的概率主要轴承性能

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摘要

A probabilistic analysis is presented for studying the variation effects on the main bearing performance of an I.C. engine system, under structural dynamic conditions. For computational efficiency, the probabilistic analysis is based on surrogate models (metamodels), which are developed using the kriging method. An Optimum Symmetric Latin Hypercube (OSLH) algorithm is used for efficient "space-filling" sampling of the design spase. The metamodels provide an efficient and accurate substitute to the actual engine bearing simulation models. The bearing performance is based on a comprehensive engine system dynamic analysis which couples the flexible crankshaft and block dynamics with a detailed main bearing elastohydrodynamic analysis. The clearance of all main bearings and the oil viscosity comprise the random variables in the probabilistic analysis. The maximum oil pressure and the percentage of time within each cycle that a bearing operates with oil film thickness below a threshold value of 0.27 μm at each main bearing constitute the system performance measures. Probabilistic analyses are first performed to calculate the mean, standard deviation and probability density function of the bearing performance measures. Subsequently, a probabilistic sensitivity analysis is described for identifying the important random variables. Finally, a Reliability-Based Design Optimization (RBDO) study is conducted for optimizing the main bearing performance under uncertainty. Results from a V6 engine are presented.
机译：提出了概率分析来研究I.C.对主轴承性能的变化影响。发动机系统，在结构动态条件下。为了提高计算效率，概率分析基于使用克里金法开发的代理模型（元模型）。最佳对称拉丁超立方体（OSLH）算法用于对设计空间进行有效的“空间填充”采样。元模型为实际的发动机轴承仿真模型提供了有效而准确的替代方法。轴承性能基于全面的发动机系统动力学分析，该分析将挠性曲轴和缸体动力学与详细的主轴承弹性流体动力学分析相结合。所有主轴承的游隙和油的粘度都是概率分析中的随机变量。每个主轴承的最大油压和轴承在油膜厚度低于0.27μm阈值的情况下运行的每个周期内的时间百分比构成了系统性能指标。首先进行概率分析，以计算轴承性能度量的平均值，标准差和概率密度函数。随后，描述了概率敏感性分析以识别重要的随机变量。最后，进行了基于可靠性的设计优化（RBDO）研究，以优化不确定性下的主轴承性能。显示了V6引擎的结果。

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