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An efficient geometric constraint handling method for surrogate-based aerodynamic shape optimization
Engineering Applications of Computational Fluid Mechanics ( IF 6.1 ) Pub Date : 2023-01-06 , DOI: 10.1080/19942060.2022.2153173
Kai Wang, Zhong-Hua Han, Ke-Shi Zhang, Wen-Ping Song

Handling a large number of geometric constraints brings a big challenge to the surrogate-based aerodynamic shape optimization (ASO) driven by computational fluid dynamics (CFD). It is not feasible to calculate the geometric constraint functions directly during the sub-optimization of a surrogate-based optimization, as the geometric constraint functions are to be evaluated thousands of times for each updating cycle and the total cost of a number of cycles can be prohibitive. This article proposes an efficient method of handling geometric constraints within the framework of a surrogate-based optimization to address this problem. The core idea is to use the Kreisselmeier-Steinhauser (KS) method to aggregate all geometric constraints into one that can be approximated by a cheap surrogate model, in order to avoid the large computational cost associated with tremendous calculation of geometric constraint values. The proposed method is verified by an analytical test case. Then, the proposed method is demonstrated and compared with the methods of building surrogate models of all geometric constraints and calculating all geometric constraints directly during each sub-optimization by drag minimizations of NACA0012 air foil and ONERA M6 wing in transonic flows. To investigate the ability of the proposed method for handling various geometric constraints, drag minimization of CRM wing in viscous transonic flow driven by CFD is performed. Results show that the proposed method can dramatically improve the optimization efficiency of ASO with the number of geometric constraints ranging from 15 to 1429 and the number of types of geometric constraints up to 3, which offers great potential for handling a larger number and more types of geometric constraints.



中文翻译:

基于代理的气动形状优化的有效几何约束处理方法

处理大量几何约束给由计算流体动力学 (CFD) 驱动的基于代理的空气动力学形状优化 (ASO) 带来了巨大挑战。在基于代理的优化的子优化过程中直接计算几何约束函数是不可行的,因为几何约束函数要在每个更新周期中被评估数千次,并且多个周期的总成本可以是令人望而却步。本文提出了一种在基于代理的优化框架内处理几何约束的有效方法,以解决此问题。核心思想是使用 Kreisselmeier-Steinhauser (KS) 方法将所有几何约束聚合为一个可以通过廉价代理模型近似的几何约束,为了避免与几何约束值的大量计算相关的大量计算成本。所提出的方法通过分析测试用例进行了验证。然后,对所提出的方法进行了论证,并与建立所有几何约束的代理模型并在每次子优化期间直接计算所有几何约束的方法进行了比较,方法是通过跨音速流动中的 NACA0012 机翼和 ONERA M6 机翼的阻力最小化。为了研究所提出的方法处理各种几何约束的能力,在 CFD 驱动的粘性跨音速流中执行 CRM 机翼的阻力最小化。

更新日期:2023-01-06
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