Efficient aerodynamic shape optimization using variable-fidelity surrogate models and multilevel computational grids
Efficient aerodynamic shape optimization using variable-fidelity surrogate models and multilevel computational grids
复制标题
使用可变保真度代理模型和多级计算网格进行高效的空气动力学形状优化
DOI：
10.1016/j.cja.2019.05.001
复制
发表时间：
2020-01-01
影响因子：
5.7
通讯作者：
Song, Wenping
中科院分区：
文献类型：
--
作者：
Han, Zhonghua;Xu, Chenzhou;Song, Wenping
变保真方法可以显著提高基于高保真和昂贵的数值模拟的设计优化的效率，并辅之以低保真和廉价的模拟(S)。然而，现有的大多数作品只包含两个保真度级别，因此效率的提高非常有限。为了尽可能减少高保真模拟的数量，迫切需要将其扩展到三个或更多保真度。本文提出了一种新的变保真优化方法，并将其应用于气动设计。它的关键部分是多层分层克立格(MHK)的理论和算法，该模型被称为代理模型，可以结合任意保真度的模拟数据。高保真模型被定义为使用精细网格的CFD模拟，而低保真模型被定义为相同的CFD模型，但具有较粗的网格，这是通过网格收敛研究确定的。首先，通过实验设计技术(DoE)为每个保真度级别选择采样形状。然后，进行CFD模拟，并使用不同保真度的输出数据来建立目标(例如CD)和约束(例如CL、Cm)函数的初始MHK模型。然后，通过填充抽样准则选择新样本，并重复更新代理模型，直到找到全局最优解。通过算例分析验证了该方法的有效性，并应用于NACA0012翼型和ONERA M6机翼跨声速气动外形优化。结果表明，该方法能显著提高优化效率，明显优于已有的单保真或两级保真方法。(三)2019年中国航空航天学会。爱思唯尔有限公司制作和主办。
A variable-fidelity method can remarkably improve the efficiency of a design optimization based on a high-fidelity and expensive numerical simulation, with assistance of lower-fidelity and cheaper simulation(s). However, most existing works only incorporate "two" levels of fidelity, and thus efficiency improvement is very limited. In order to reduce the number of high-fidelity simulations as many as possible, there is a strong need to extend it to three or more fidelities. This article proposes a novel variable-fidelity optimization approach with application to aerodynamic design. Its key ingredient is the theory and algorithm of a Multi-level Hierarchical Kriging (MHK), which is referred to as a surrogate model that can incorporate simulation data with arbitrary levels of fidelity. The high-fidelity model is defined as a CFD simulation using a fine grid and the lower-fidelity models are defined as the same CFD model but with coarser grids, which are determined through a grid convergence study. First, sampling shapes are selected for each level of fidelity via technique of Design of Experiments (DoE). Then, CFD simulations are conducted and the output data of varying fidelity is used to build initial MHK models for objective (e.g. CD) and constraint (e.g. CL, Cm) functions. Next, new samples are selected through infill-sampling criteria and the surrogate models are repetitively updated until a global optimum is found. The proposed method is validated by analytical test cases and applied to aerodynamic shape optimization of a NACA0012 airfoil and an ONERA M6 wing in transonic flows. The results confirm that the proposed method can significantly improve the optimization efficiency and apparently outperforms the existing single-fidelity or two-level-fidelity method. (C) 2019 Chinese Society of Aeronautics and Astronautics. Production and hosting by Elsevier Ltd.