Hypersonic vehicle has gained increasing attention due to its high cruise speed and long voyage. In this paper, an enhanced Sequential Approximate Optimization method is proposed for aerodynamic optimization of a hypersonic vehicle. In this method, a constrained design of experiment method is adopted to generate the initial sampling set with adequate number of feasible points. A field metamodel is proposed to surrogate the aerodynamic parameters distributions in the flow field obtained by the aerodynamic simulation. A hybrid metamodel combing radial basis functions and polynomial chaos expansion is applied to construct the field metamodel with good approximate performance. A robust mesh morphing scheme based on radial basis functions is developed to generate high-quality meshes for the sequential optimization scheme. The hypersonic vehicle aerodynamic optimization problem is performed using the proposed optimization framework and satisfactory results are obtained with limited computational budgets. Results show that the proposed field metamodel-enhanced Sequential Approximate Optimization method possesses powerful optimization performance and promising prospects in the field of hypersonic vehicle optimization design.

中文翻译：

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利用场元模型增强的顺序近似优化进行超音速飞行器空气动力学优化

高超音速飞行器因其高巡航速度和长航程而受到越来越多的关注。本文提出了一种改进的顺序近似优化方法，用于高超声速飞行器的空气动力学优化。在这种方法中，采用实验方法的约束设计来生成具有足够数量可行点的初始采样集。提出了一种现场元模型来替代通过空气动力学模拟获得的流场中的空气动力学参数分布。将径向基函数和多项式混沌展开相结合的混合元模型用于构造具有良好近似性能的场元模型。开发了一种基于径向基函数的鲁棒网格变形方案，以生成用于顺序优化方案的高质量网格。使用所提出的优化框架来执行高超声速飞行器的空气动力学优化问题，并在有限的计算预算下获得令人满意的结果。结果表明，所提出的场元模型增强的顺序近似优化方法在超音速飞行器优化设计领域具有强大的优化性能，并具有广阔的应用前景。