高效率采样的数据关联融合气动力建模方法  

Data association and fusion aerodynamic modeling method based on efficient sampling

作　　者：宁晨伽 王旭[1] 王文正 张伟伟[1] NING Chenjia;WANG Xu;WANG Wenzheng;ZHANG Weiwei(School of Aeronautics,Northwestern Polytechnical University,Xi'an 710072,China;School of Aeronautics and Astronautics,University of Electronic Science and Technology of China,Chengdu 611731,China;Aircraft Swarm Intelligent Sensing and Cooperative Control Key Laboratory of Sichuan Province,Chengdu 611731,China)

机构地区：[1]西北工业大学航空学院,西安710072 [2]电子科技大学航空航天学院,成都611731 [3]飞行器集群智能感知与协同控制四川省重点实验室,成都611731

出　　处：《空气动力学学报》2022年第5期39-49,共11页Acta Aerodynamica Sinica

基　　金：国家自然科学基金面上项目(12072282);国家自然科学基金集成项目(92152301)。

摘　　要：飞行器设计阶段的气动分析需要大量的高保真度气动力数据以提高设计性能,但其获取成本十分高昂。为了缓解建模成本与模型精度之间的矛盾,构建了关联不同保真度数据的多保真度气动数据融合模型,并提出了最优关联点选取方法和均匀性增强序贯采样方法,以此实现co-Kriging变可信度模型的高效初始化与最速收敛。作为验证,选用标准数值算例开展建模研究,并结合统计结果对方法精度优劣进行了对比。最后将该建模框架成功应用于NACA0012翼型跨声速气动力工程算例当中。结果表明,与传统模型相比,在仅有的少量高保真度样本下,所采用的方法可以大幅提升变可信度模型收敛精度和建模效率,有效降低了采样成本;相较于高保真度单精度元模型,误差可降低50%以上。Aerodynamic analysis of aircraft design often requires a large amount of high-fidelity(HF)aerodynamic data to improve the performance of aircraft design. However, the acquisition cost is very high. In order to alleviate the contradiction between modeling cost and accuracy, this paper constructs a multi-fidelity aerodynamic data fusion model by associating data with different fidelity. Furthermore, an optimal correlation point selection method and a uniformly enhanced sequential sampling method are proposed to achieve the efficient initialization and fastest convergence of variable-fidelity models based on co-Kriging. As a validation, standard numerical examples are selected to carry out modeling study, and the accuracy of the method is checked by comparing the statistical variables. Finally, the framework is successfully applied in the transonic aerodynamic engineering case of the NACA0012 airfoil. The results show that compared with the traditional model, the proposed method can greatly improve the convergence accuracy and modeling efficiency of the variable-fidelity model with only a small number of high-fidelity samples, which effectively reduces the sampling cost. Compared to the highfidelity single precision sequence modeling, the error can be reduced by more than a half.

关 键 词：数据关联融合 变可信度模型 样本初始化 CO-KRIGING 序贯采样 

分 类 号：O354[理学—流体力学] V211.5[理学—力学]