空气动力学学报2026，Vol.44Issue(3)：22-34,13.DOI:10.7638/kqdlxxb-2024.0192
基于GPR模型的多保真气动力建模方法
Multi-fidelity aerodynamic modeling method based on GPR model
摘要
Abstract
Accurate aerodynamic characterization is crucial for optimizing aircraft design and enhancing flight performance.Multi-fidelity modeling approaches improve aerodynamic prediction accuracy and computational efficiency by integrating data from various fidelity levels.To better handle the complex mixed linear and nonlinear correlations coexisting between high-and low-fidelity data,this paper proposes a new multi-fidelity Gaussian process regression(MFGPR)model based on the nonlinear autoregressive Gaussian process(NARGP)framework.By integrating linear and nonlinear kernel functions,the proposed model extends the capabilities of NARGP,enabling it to simultaneously capture complex nonlinear relationships and linear dependencies within multi-fidelity data.To validate the effectiveness of the MFGPR method,two classes of classic analytical functions were selected for numerical testing,and a comparative analysis was performed against three traditional multi-fidelity methods:Cokriging,NARGP,and MFDNN.The results indicate that in handling linear correlations,the prediction performance of MFGPR is consistent with that of CoKriging.Conversely,in modeling nonlinear correlations,MFGPR demonstrates higher prediction accuracy than the other three methods,while offering a clear advantage in modeling efficiency.Furthermore,MFGPR was applied to predict the pressure distribution of the ONERA M6 wing and the drag coefficient of the NACA2414 airfoil,verifying its potential application and superior performance in aerodynamic modeling.关键词
多保真气动力建模/气动特性/高斯过程回归/线性核函数/建模效率Key words
multi-fidelity aerodynamic modeling/aerodynamic characteristics/Gaussian process regression/linear kernel fuctions/modeling efficiency分类
航空航天引用本文复制引用
罗希,黄俊,唐磊,王庆凤..基于GPR模型的多保真气动力建模方法[J].空气动力学学报,2026,44(3):22-34,13.基金项目
飞行器流体物理全国重点实验室开放课题(2024-APF-KFZD-06) （2024-APF-KFZD-06）
