| Flush Air Data Sensing Based on Dimensionless Input and Output Neural Networks With Less Data | |
Liu Y(刘洋) ; Zhang CA(张陈安); Yan, Xunshi; Liu W(刘文)
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| Source Publication | IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS
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| 2023-04 | |
| Volume | 59Issue:2Pages:1411-1425 |
| ISSN | 0018-9251 |
| Abstract | Neural networks have the ability to deal with the flush air data sensing (FADS) system of various vehicles. However, the demand for large quantities of training data limits its application. To overcome the problem, this article develops a FADS algorithm called dimensionless input and output neural networks FADS (DIO-NNFADS) to estimate air data states. The DIO-NNFADS is utilized to approximate the aerodynamicmodel defined by dimensional analysis, which decouples the freestream static pressure. Thus, trained by less data from a single flight profile, the DIO-NNFADS can achieve good accuracy in the entire flight envelope, effectively reducing the training data for neural networks. The Mach number, angle of attack, angle of sideslip, and the pressure coefficients are directly output by the DIO-NNFADS. And the static pressure and dynamic pressure are solved by the equations composed of the measured pressures and pressure coefficients. The proposed FADS algorithm is verified on a simplified supersonic model through numerical simulation. Results show that the algorithm can estimate the Mach number within the relative error of 2.9%, static pressure and dynamic pressure within the relative error of 6.2%, and the angle of incidence within the absolute error of 0.4. in the entire flight envelope. Besides, the optimal size of the training data set for the DIO-NNFADS is discussed. Furthermore, the influence of port layout and selection is analyzed, and the algorithm also shows good performance for a port layout without stagnation point. |
| DOI | 10.1109/TAES.2022.3201813 |
| Indexed By | SCI ; EI |
| Language | 英语 |
| WOS ID | WOS:000974895700051 |
| Funding Organization | Strategic Priority Research Program of Chinese Academy of Sciences [XDA17030100] ; National Science and Technology Major Project of China [ZX069] ; National Natural Science Foundation of China [11902324] |
| Classification | 一类 |
| Ranking | 1 |
| Contributor | Yan, XS |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://dspace.imech.ac.cn/handle/311007/92213 |
| Collection | 高温气体动力学国家重点实验室 |
| Affiliation | 1.(Liu Yang, Zhang Chen-an, Liu Wen) Chinese Acad Sci Inst Mech State Key Lab High Temp Gas Dynam Beijing 100190 Peoples R China 2.(Liu Yang) Univ Chinese Acad Sci Sch Engn Sci Beijing 100049 Peoples R China 3.(Yan Xunshi) Tsinghua Univ Inst Nucl & New Energy Technol Beijing 100084 Peoples R China |
| Recommended Citation GB/T 7714 | Liu Y,Zhang CA,Yan, Xunshi,et al. Flush Air Data Sensing Based on Dimensionless Input and Output Neural Networks With Less Data[J]. IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS,2023,59,2,:1411-1425.Rp_Au:Yan, XS |
| APA | 刘洋,张陈安,Yan, Xunshi,&刘文.(2023).Flush Air Data Sensing Based on Dimensionless Input and Output Neural Networks With Less Data.IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS,59(2),1411-1425. |
| MLA | 刘洋,et al."Flush Air Data Sensing Based on Dimensionless Input and Output Neural Networks With Less Data".IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 59.2(2023):1411-1425. |
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| Jp2023Fa340.pdf（4708KB） | 期刊论文 | 出版稿 | 开放获取 | CC BY-NC-SA | View Download | |
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