空气动力学学报2024，Vol.42Issue(10)：69-83,15.DOI:10.7638/kqdlxxb-2024.0109
面向飞行试验认知不确定性的气动数据融合方法
Aerodynamic data fusion method under epistemic uncertainty in flight tests
摘要
Abstract
In the field of aircraft design,various methods for obtaining aerodynamic data have their own advantages and disadvantages,making it a challenge to accurately predict an aircraft's aerodynamic characteristics using a single approach.Therefore,in practical engineering applications,it is often necessary to fuse data from multiple sources to achieve a more accurate and comprehensive description of aerodynamic characteristics.In response to this need,this study takes the typical jet aircraft as an example and employs real flight data,simulated flight data,and Computational Fluid Dynamics(CFD)data.By combining these with deep neural networks,we propose a dual-level deep evidential fusion algorithm for aerodynamic data under epistemic uncertainty.In this algorithm,two standard confidence distribution methods are introduced,and the output of deep neural networks is combined with variational Dirichlet distribution parameters to express and quantify epistemic uncertainty during the model fusion process.Utilizing the Dempster-Shafer theory,this algorithm effectively fuses data from different sources and their associated uncertainties.The results indicate that this algorithm successfully fuses multi-source aerodynamic data,producing outcomes that not only conform to physical laws but also provide more accurate and comprehensive aerodynamic data,which demonstrate significant advantages over single-source data methods.关键词
双层深度证据融合/多源气动数据/认知不确定性/飞行试验/CFD仿真Key words
dual-level deep evidential fusion/multi-source aerodynamic data/epistemic uncertainty/flight test/CFD simulation分类
航空航天引用本文复制引用
仇静轩,司海青,高昕睿,曹九发,吴晓军,赵炜,张培红..面向飞行试验认知不确定性的气动数据融合方法[J].空气动力学学报,2024,42(10):69-83,15.基金项目
航空航天结构力学及控制全国重点实验室青年学生项目(MCAS-S-0224G03) （MCAS-S-0224G03）
国家自然科学基金委员会-中国民用航空局联合基金项目(U2033202) （U2033202）
工信部民机专项科研项目(MJZ1-8N22) （MJZ1-8N22）
江苏省研究生科研创新计划(SJCX24-0141) （SJCX24-0141）
