| 引用本文: | 张泽斌,张鹏飞,郭红,李永.Kriging序贯设计方法在滑动轴承优化中的应用[J].哈尔滨工业大学学报,2019,51(7):178.DOI:10.11918/j.issn.0367-6234.201810147 |
| ZHANG Zebin,ZHANG Pengfei,GUO Hong,LI Yong.Implementation of Kriging model based sequential design on the optimization of sliding bearing[J].Journal of Harbin Institute of Technology,2019,51(7):178.DOI:10.11918/j.issn.0367-6234.201810147 |
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| 摘要: |
| 利用 Kriging代理模型提供目标函数无偏预测值和理论置信区间的优势,比较传统试验设计方法和基于Kriging模型的序贯加点方法对模型的影响, 结合设计空间的全局搜索和最优解临近区间的局部搜索,引入并行加点准则及相应的收敛条件,得到精度、效率高的代理模型,用2个经典优化测试函数进行验证和评估. 结果表明,与传统试验设计方法相比,基于Kriging的序贯加点方法得到的模型全局精度更高且能更快地收敛到优化问题的真实最优解. 最后,以动静压滑动轴承为优化设计对象,单位承载力下摩擦功耗为目标函数,考虑几何结构及工况等约束条件,采用传统试验设计方案和Kriging序贯加点方案分别建立目标函数的模型,分别进行优化设计,并同传统的复合形优化结果进行对比. 3种方案对比结果显示,Kriging加点方案在有限迭代步数下,对于降低单位承载力下的摩擦功耗效果最为显著,验证了该方法快速收敛的特性. |
| 关键词: Kriging 代理模型 序贯设计 动静压轴承 优化设计 |
| DOI：10.11918/j.issn.0367-6234.201810147 |
| 分类号:TH122TH117.2 |
| 文献标识码:A |
| 基金项目:国家自然科学基金资助项目(51575498) |
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| Implementation of Kriging model based sequential design on the optimization of sliding bearing |
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ZHANG Zebin,ZHANG Pengfei,GUO Hong,LI Yong
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(School of Mechanical Engineering,Zhengzhou University,Zhengzhou 450001,China)
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| Abstract: |
| A built-in prime benefit of Kriging surrogate model resides in its unbiased prediction and the associated confidence intervals, comparisons have been done between the classical DoE methods and Kriging model based sequential design. This latter considers both the design space global exploration and optimum-neighborhood exploitation. A parallel point-adding training strategy and its corresponding convergence criterion were introduced to improve the model precision. The approach had been illustrated with two classical optimization test functions. Results show not only a more accurate model but also a possibility to reduce the number of sampling points. Finally, the training strategy was experimented for the optimal design of a sliding bearing. Friction power loss per unit load capacity was taken as the objective function to be modeled. Two surrogate model based optimizations had been per-formed based on classical DoE such as Orthogonal Latin Hypercube and Kriging sequential strategy respectively. These two optimization results are compared with a previously optimization using Complex-optimization method. Within a limited number of iterations, the Kriging model based training strategy showed the best convergence to the global optimum among those 3 methods. |
| Key words: Kriging Surrogate model Sequential Design Hybrid Bearing Optimization |
