| 基于代理模型全局优化的自适应参数化方法 |
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| 引用本文： | 张伟,高正红,周琳,夏露. 基于代理模型全局优化的自适应参数化方法[J]. 航空学报, 2020, 41(10): 123815-123815. DOI: 10.7527/S1000-6893.2020.23815 |
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| 作者姓名： | 张伟 高正红 周琳 夏露 |
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| 作者单位： | 西北工业大学 航空学院, 西安 710072 |
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| 基金项目： | 西北工业大学翼型、叶栅空气动力学重点实验室稳定支持 |
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| 摘 要： | 对于翼型气动隐身设计问题，设计变量的配置对设计结果影响很大，而简单地增加设计变量不能保证得到理想的结果。提出一种适用于代理模型全局优化的自适应参数化方法：利用全局敏感性分析方法——基本效应法，得到设计空间关于目标函数的敏感区域信息，并以此为根据增加设计变量；利用节点插入算法将低维样本在高维空间内进行重构，避免了重新取样的工作量。相对于传统固定设计空间维度方法，自适应参数化方法在设计空间的敏感区域扩展维度，能够更加精准地描述外形并反映目标的变化趋势。通过飞翼布局翼型的气动隐身优化算例，证实自适应参数化方法可以大幅提高优化设计质量和效率。
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| 关 键 词： | 气动隐身设计 代理模型全局优化 自适应参数化方法 全局敏感性分析 节点插入算法 |
| 收稿时间： | 2020-01-09 |
| 修稿时间： | 2020-04-03 |
Adaptive parameterization method for surrogate-based global optimization |
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| ZHANG Wei,GAO Zhenghong,ZHOU Lin,XIA Lu. Adaptive parameterization method for surrogate-based global optimization[J]. Acta Aeronautica et Astronautica Sinica, 2020, 41(10): 123815-123815. DOI: 10.7527/S1000-6893.2020.23815 |
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| Authors: | ZHANG Wei GAO Zhenghong ZHOU Lin XIA Lu |
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| Affiliation: | School of Aeronautics, Northwestern Polytechnical University, Xi'an 710072, China |
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| Abstract: | The results of the airfoil aerodynamic/stealth design with different numbers of design variables are compared and analyzed, revealing considerable impact of the design variable configuration on the results. Simple increase in the design variables cannot guarantee ideal results. This paper proposes an adaptive parameterization method for surrogate-based optimization. Using the global sensitivity analysis method and the element effect method, it obtains the sensitive information about the objectives in the design space to add design variables. The knot insertion algorithm is adopted to reconstruct samples in the high-dimensional space, avoiding the computational cost of resampling. Compared with the traditional fixed-dimensional method, the adaptive parameterization method expands the dimension in the sensitive area of the design space. The expanded design space can more accurately describe the shape and reflect the changing trend of the objective function. Therefore, the proposed method can significantly improve the quality and efficiency of optimization. |
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| Keywords: | aerodynamic/stealth design surrogate-based global optimization adaptive parameterization method global sensitivity analysis knot insertion algorithm |
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