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Machine learning-based performance analysis of two-axial-groove hydrodynamic journal bearings
Proceedings of the Institution of Mechanical Engineers, Part J: Journal of Engineering Tribology ( IF 1.6 ) Pub Date : 2021-02-09 , DOI: 10.1177/1350650121992895
Biswajit Roy 1 , Sudip Dey 1
Affiliation  

The precise prediction of a rotor against instability is needed for avoiding the degradation or failure of the system’s performance due to the parametric variabilities of a bearing system. In general, the design of the journal bearing is framed based on the deterministic theoretical analysis. To map the precise prediction of hydrodynamic performance, it is needed to include the uncertain effect of input parameters on the output behavior of the journal bearing. This paper presents the uncertain hydrodynamic analysis of a two-axial-groove journal bearing including randomness in bearing oil viscosity and supply pressure. To simulate the uncertainty in the input parameters, the Monte Carlo simulation is carried out. A support vector machine is employed as a metamodel to increase the computational efficiency. Both individual and compound effects of uncertainties in the input parameters are studied to quantify their effect on the steady-state and dynamic characteristics of the bearing.



中文翻译:

基于机器学习的两轴槽动压滑动轴承性能分析

为了避免由于轴承系统的参数变化而导致的系统性能下降或故障,需要针对转子的不稳定性进行精确的预测。通常,轴颈轴承的设计是基于确定性理论分析的框架。要绘制流体动力性能的精确预测,需要包括输入参数对轴颈轴承输出行为的不确定影响。本文介绍了两轴沟槽轴颈轴承的不确定流体力学分析,包括轴承油粘度和供应压力的随机性。为了模拟输入参数的不确定性,进行了蒙特卡洛模拟。支持向量机被用作元模型以提高计算效率。

更新日期:2021-02-09
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