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A surrogate modeling method for large-scale flow field prediction based on locally optimal shape parameters

Chenlu Wang Junfeng Li Zeping Wu Jianhong Sun Shuaichao Ma Yi Zhao

Aerospace Traffic and Safety2025，Vol.2Issue(1)：P.1-9,9.

Aerospace Traffic and Safety2025，Vol.2Issue(1)：P.1-9,9.DOI:10.1016/j.aets.2025.03.001

A surrogate modeling method for large-scale flow field prediction based on locally optimal shape parameters

Chenlu Wang ¹Junfeng Li ²Zeping Wu ²Jianhong Sun ³Shuaichao Ma ²Yi Zhao²

作者信息

1. Key Laboratory of Aircraft Environment Control and Life Support,MIIT,Nanjing University of Aeronautics and Astronautics,Nanjing 210016,China College of Aerospace Science and Engineering,National University of Defense Technology,Changsha 410073,China
2. College of Aerospace Science and Engineering,National University of Defense Technology,Changsha 410073,China
3. Key Laboratory of Aircraft Environment Control and Life Support,MIIT,Nanjing University of Aeronautics and Astronautics,Nanjing 210016,China
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摘要

关键词

Flow field prediction/Enhanced radial basis function/Global and local surrogate

分类

数理科学

引用本文复制引用

Chenlu Wang,Junfeng Li,Zeping Wu,Jianhong Sun,Shuaichao Ma,Yi Zhao..A surrogate modeling method for large-scale flow field prediction based on locally optimal shape parameters[J].Aerospace Traffic and Safety,2025,2(1):P.1-9,9.

基金项目

supported by the science and technology innovation Program of Hunan Province(Grant No.2024RC3142) （Grant No.2024RC3142）

National Natural Science Foundation of China(Grant No.52375278). （Grant No.52375278）

Aerospace Traffic and Safety

ISSN：2950-3388

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