Efficient aerodynamic shape optimization of transonic wings using a parallel infilling strategy and surrogate models
Efficient aerodynamic shape optimization of transonic wings using a parallel infilling strategy and surrogate models
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DOI：
10.1007/s00158-016-1546-7
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发表时间：
2017-03-01
影响因子：
3.9
通讯作者：
Zhang, Y.
中科院分区：
文献类型：
--
作者：
Liu, J.;Song, W. -P.;Zhang, Y.
在气动外形数值优化设计中，通常采用高精度、高成本的计算流体动力学（CFD）方法，使用代理模型可以显著提高设计效率。传统的自适应方法是在初始代理模型建立后，在每个更新周期中只选择一个采样点来更新代理模型。为了在每个更新周期选择多个新样本，近年来已经开发了一些并行填充策略，以减少优化的挂钟时间。本文提出了一种基于代理约束优化的并行填充策略，并以跨音速机翼气动外形优化为例进行了验证。与现有的采用单一填充准则选取多个样本点的方法不同，本文采用多个填充准则的组合，每个准则选取不同的样本点。ONERA-M6和DLR-F4机翼的约束阻力最小化被用来验证所提出的方法，包括低维（6个设计变量）和高维问题（多达48个设计变量）。结果表明，对于基于代理的跨音速机翼优化，当初始样本点数在Nv到8 Nv（Nv表示设计变量的数量）范围内时，该方法比现有的并行填充策略更有效。每个案例重复50次，以消除我们结果中的随机性影响。
Surrogate models are used to dramatically improve the design efficiency of numerical aerodynamic shape optimization, where high-fidelity, expensive computational fluid dynamics (CFD) is often employed. Traditionally, in adaptation, only one single sample point is chosen to update the surrogate model during each updating cycle, after the initial surrogate model is built. To enable the selection of multiple new samples at each updating cycle, a few parallel infilling strategies have been developed in recent years, in order to reduce the optimization wall clock time. In this article, an alternative parallel infilling strategy for surrogate-based constrained optimization is presented and demonstrated by the aerodynamic shape optimization of transonic wings. Different from existing methods in which multiple sample points are chosen by a single infill criterion, this article uses a combination of multiple infill criteria, with each criterion choosing a different sample point. Constrained drag minimizations of the ONERA-M6 and DLR-F4 wings are exercised to demonstrate the proposed method, including low-dimensional (6 design variables) and higher-dimensional problems (up to 48 design variables). The results show that, for surrogate-based optimization of transonic wings, the proposed method is more effective than the existing parallel infilling strategies, when the number of initial sample points are in the range from Nv to 8Nv (Nv here denotes the number of design variables). Each case is repeated 50 times to eliminate the effect of randomness in our results.