基 于 自 适 应 采 样 的 高 超 声 速 飞 行 器 气 动 热全 局 快 速 预 示

High-fidelity aerothermodynamics analysis models in the thermal protection system of the hypersonic vehicles significantly increase the computational budget of engineering design，drawing extensive attention on data-driven based rapid prediction methods. This paper proposes a batch adaptive sampling method based on fuzzy clustering to improve the global prediction accuracy with the limited computational budget of high fidelity models. The sampling influence domain is constructed by clustering and hypersphere segmentation with the distribution characteristics of the prediction error，considering both the key sampling domain with larger errors and global exploration. The sampling refused domain is developed by the local error scoring coefficient weighted to reduce the redundancy of newly added samples. The method adds new samples in the comprehensively determined key sampling space to improve the sampling quality based on the maxmin criterion，thereby rapidly improving the global accuracy of the prediction models. The comparison results show that the proposed method outperforms One-Shot，APSFC and CV-Voronoi in terms of reducing the sampling scale required and accelerating prediction accuracy improvement. The rapid prediction results of the HTV-2 typed vehicle aerothermodynamics demonstrate the practicality and effectiveness of the proposed method in engineering practices.