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A faster optimization method based on support vector regression for aerodynamic problems

机译：基于支持向量回归的空气动力学问题快速优化方法

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摘要

In this paper, a new strategy for optimal design of complex aerodynamic configuration with a reasonable low computational effort is proposed. In order to solve the formulated aerodynamic optimization problem with heavy computation complexity, two steps are taken: (1) a sequential approximation method based on support vector regression (SVR) and hybrid cross validation strategy, is proposed to predict aerodynamic coefficients, and thus approximates the objective function and constraint conditions of the originally formulated optimization problem with given limited sample points; (2) a sequential optimization algorithm is proposed to ensure the obtained optimal solution by solving the approximation optimization problem in step (1) is very close to the optimal solution of the originally formulated optimization problem. In the end, we adopt a complex aerodynamic design problem, that is optimal aerodynamic design of a flight vehicle with grid fins, to demonstrate our proposed optimization methods, and numerical results show that better results can be obtained with a significantly lower computational effort than using classical optimization techniques.
机译：本文提出了一种合理的低计算量的复杂空气动力学配置优化设计的新策略。为了解决复杂的公式化的空气动力学优化问题，采取了两个步骤：（1）提出了一种基于支持向量回归和混合交叉验证策略的逐次逼近方法，以预测空气动力学系数，从而进行近似计算。在给定有限采样点的情况下，最初制定的优化问题的目标函数和约束条件； （2）提出了一种顺序优化算法，以通过求解步骤（1）中的近似优化问题来确保获得的最优解与原始制定的优化问题的最优解非常接近。最后，我们采用了一个复杂的空气动力学设计问题，即带有翼片的飞行器的最佳空气动力学设计，以证明我们提出的优化方法，数值结果表明，与使用FPS相比，用较低的计算量可以获得更好的结果。经典优化技术。

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