A surrogate modeling method for large-scale flow field prediction based on locally optimal shape parametersOA
To address the high computational cost caused by extensive matrix operations in constructing surrogate models for large-scale flow field prediction,this paper proposes an enhanced radial basis function(RBF)surrogate modeling method based on local shape parameter optimization.Given that far-field physical parameters exhibit simpler relationships and regions with significant prediction errors are predominantly concentrated near geometric bodies,an optimization strategy using local maximum prediction error points has been introduced.By capturing key points in high-error regions,a local surrogate model is constructed to iteratively optimize shape parameters,replacing global flow field shape parameters with locally optimal ones.Experimental results demonstrate that compared to traditional computational fluid dynamics(CFD)methods,flow field prediction based on surrogate model reduces computational time by over 99%.The proposed local error-driven parameter optimization strategy reduces computational costs by over 90%compared to traditional global optimization methods,while maintaining the average prediction error below 2%.
Chenlu Wang;Junfeng Li;Zeping Wu;Jianhong Sun;Shuaichao Ma;Yi Zhao
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,ChinaCollege of Aerospace Science and Engineering,National University of Defense Technology,Changsha 410073,ChinaCollege of Aerospace Science and Engineering,National University of Defense Technology,Changsha 410073,ChinaKey Laboratory of Aircraft Environment Control and Life Support,MIIT,Nanjing University of Aeronautics and Astronautics,Nanjing 210016,ChinaCollege of Aerospace Science and Engineering,National University of Defense Technology,Changsha 410073,ChinaCollege of Aerospace Science and Engineering,National University of Defense Technology,Changsha 410073,China
数理科学
Flow field predictionEnhanced radial basis functionGlobal and local surrogate
《Aerospace Traffic and Safety》 2025 (1)
P.1-9,9
supported by the science and technology innovation Program of Hunan Province(Grant No.2024RC3142)National Natural Science Foundation of China(Grant No.52375278).
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