Improving variable-fidelity surrogate modeling via gradient-enhanced kriging and a generalized hybrid bridge function
Improving variable-fidelity surrogate modeling via gradient-enhanced kriging and a generalized hybrid bridge function
复制标题
通过梯度增强克里金法和广义混合桥函数改进可变保真度代理建模
DOI：
10.1016/j.ast.2012.01.006
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发表时间：
2013-03-01
影响因子：
5.6
通讯作者：
Zimmermann, Ralf
中科院分区：
文献类型：
--
作者：
Han, Zhong-Hua;Goertz, Stefan;Zimmermann, Ralf
可变保真度代理建模提供了一种有效的方法，可以基于一组具有不同保真度和计算费用的 CFD 方法来生成用于空气载荷预测的空气动力学数据。在本文中，将直接梯度增强克里金法（GEK）和新开发的广义混合桥函数（GHBF）相结合，以提高现有可变保真度建模（VFM）方法的效率和准确性。新的算法和功能针对分析功能进行了演示和评估，随后用于构建 RAE 2822 翼型的空气动力系数和阻力极的全局替代模型。结果表明，本文提出的梯度增强 GHBF 非常有前景，可用于显着提高航空载荷预测中 VFM 的效率、准确性和鲁棒性。 (C) 2012 Elsevier Masson SAS。版权所有。
Variable-fidelity surrogate modeling offers an efficient way to generate aerodynamic data for aero-loads prediction based on a set of CFD methods with varying degree of fidelity and computational expense. In this paper, direct Gradient-Enhanced Kriging (GEK) and a newly developed Generalized Hybrid Bridge Function (GHBF) have been combined in order to improve the efficiency and accuracy of the existing Variable-Fidelity Modeling (VFM) approach. The new algorithms and features are demonstrated and evaluated for analytical functions and are subsequently used to construct a global surrogate model for the aerodynamic coefficients and drag polar of an RAE 2822 airfoil. It is shown that the gradient-enhanced GHBF proposed in this paper is very promising and can be used to significantly improve the efficiency, accuracy and robustness of VFM in the context of aero-loads prediction. (C) 2012 Elsevier Masson SAS. All rights reserved.