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Gradient-Enhanced Hierarchical Kriging Model for Aerodynamic Design Optimization

机译:空气动力学设计优化的梯度增强分层克里格模型

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摘要

A cokriging model incorporating gradient information and the function value of sample points can reduce the computational cost with a given level of accuracy. In this paper, the hierarchical kriging, a recently proposed cokriging method is employed, and a new method called gradient-enhanced hierarchical kriging (GEHK) is developed. First of all, a low-fidelity kriging model is built using derived samples, which are obtained by Taylor approximation using gradients and selected step sizes. Then a high-fidelity model is built by adjusting the low-fidelity kriging model with initial sample points. The GEHK model is more efficient than the traditional gradient-based cokriging model in the aerodynamic optimization, and could get a better optimum value. Taking the advantage of the modeling strategy, the global accuracy of the GEHK is not sensitive to step sizes, and the accuracy of prediction is enhanced evidently. The GEHK method is able to overcome limitations of traditional gradient-based cokriging models, and the prediction accuracy of the model is improved globally. (C) 2017 American Society of Civil Engineers.
机译:结合了梯度信息和样本点函数值的协同克里格模型可以在给定的精度水平下降低计算成本。本文采用分层克里金法,采用了最近提出的协同克里金法,并开发了一种新的方法,称为梯度增强分层克里金法(GEHK)。首先,使用派生样本构建低保真克里金模型,该样本通过使用梯度和选定步长的泰勒近似法获得。然后,通过使用初始采样点调整低保真克里金模型来构建高保真模型。在空气动力学优化中,GEHK模型比传统的基于梯度的协同克里金模型更有效,并且可以获得更好的最优值。利用建模策略的优势,GEHK的全局精度对步长不敏感,预测精度明显提高。 GEHK方法能够克服传统基于梯度的协同克里金模型的局限性,并且该模型的预测准确性在全球范围内得到了提高。 (C)2017年美国土木工程师学会。

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  • 来源
    《Journal of aerospace engineering》 |2017年第6期|04017072.1-04017072.14|共14页
  • 作者单位

    Northwestern Polytech Univ, Natl Key Lab Sci & Technol Aerodynam Design & Res, 127 West Youyi Rd, Xian 710072, Shaanxi, Peoples R China;

    Northwestern Polytech Univ, Natl Key Lab Sci & Technol Aerodynam Design & Res, 127 West Youyi Rd, Xian 710072, Shaanxi, Peoples R China;

    Northwestern Polytech Univ, Natl Key Lab Sci & Technol Aerodynam Design & Res, 127 West Youyi Rd, Xian 710072, Shaanxi, Peoples R China;

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