非支配排序遗传算法的动压轴承形状多目标优化

庞晓平; 陈进

doi:10.3969/j.issn.0258-2724.2012.04.017

非支配排序遗传算法的动压轴承形状多目标优化

doi: 10.3969/j.issn.0258-2724.2012.04.017

庞晓平,
陈进

基金项目:

国家自然科学基金资助项目(51005257)

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- 文章访问数: 1289
- HTML全文浏览量: 94
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- 被引次数: 0
出版历程
- 收稿日期: 2011-01-19
- 刊出日期: 2012-08-25

Multi-objective Shape Optimization of Hydrodynamic Journal Bearings Using Non-dorminated Sorting Genetic Algorithm Ⅱ

摘要

摘要: 为了克服以偏心率为初始参数的轴承优化模型优化结果局限于原始形状的缺点,提出用傅里叶级数表示通用膜厚方程,建立了多目标形状优化设计数学模型.应用基于非支配排序遗传算法,以最小功耗和最小侧漏流速为目标、最小油膜厚度和最小承载力为限制条件,以通用膜厚方程系数为设计变量,进行了轴承形状的多目标优化设计,并用Matlab偏微分方程工具箱求解基于通用膜厚的控制方程.实例分析结果表明:基于通用膜厚方程的多目标优化后的轴承形状不受固有型线的限制;在保证最大承载力的基础上,优化后的非圆轴承与仅以最大承载力为单目标优化的结果相比,最小功耗下降了80.8%,最小侧漏流速比优化前下降了3个数量级,并得出了Pareto最优解集.
- 动压径向轴承 /
- 形状优化 /
- NSGA-Ⅱ /
- 多目标优化 /
- Pareto最优解集
Abstract: In order to overcome the shortcoming caused by taking the eccentricity as the initial design variables in the shape optimization model, i.e., the optimized shape was localized in the original shape, a new mathematic model of multi-objective shape optimization for hydrodynamic journal bearings was built through the general film thickness equation based on the Fourier series. In the model, the objective function is minimization of oil leakage and power loss, which is subjected to the minimum oil film thickness and the minimum bearing capacity, and the coefficients of general oil film thickness are design variables. The multi-objective optimization for the profile design of a hydrodynamic journal bearing is accomplished using a modified non-dominated sorting genetic algorithm II (NSGA-Ⅱ). The governing equations based on the general film thickness were solved by Matlab PDE (partial differential equation) toolbox. The results of a case study show that the bearing shape obtained by multi-objective optimization based on the general film thickness equation is not limited by the original shape. In addition,a Pareto-optimal set is obtained, where, for one of the optimized non-circle bearings with the maximum load capacity, the power loss and leakage rate are decreased by 80.8% and three orders of magnitude, respectively, compared with that just taking the load capacity as the objective function.
- hydrodynamic journal bearing /
- shape optimization /
- NSGA-Ⅱ /
- multi-objective optimization /
- Pareto optimal set

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参考文献(15)

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文章访问数: 1289
HTML全文浏览量: 94
PDF下载量: 428
被引次数: 0

非支配排序遗传算法的动压轴承形状多目标优化

doi: 10.3969/j.issn.0258-2724.2012.04.017

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出版历程

Multi-objective Shape Optimization of Hydrodynamic Journal Bearings Using Non-dorminated Sorting Genetic Algorithm Ⅱ

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