| Citation: | ZHOU Y J，WAN Q，XU Y Z，et al. Redundancy design of a FADS system on a complex leading-edge vehicle using neural network approach[J]. Journal of Beijing University of Aeronautics and Astronautics，2024，50（3）：757-764 （in Chinese） doi: 10.13700/j.bh.1001-5965.2022.0341
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In order to gather airspeed and aerodynamic orientation for hypersonic vehicles, flush air data sensing (FADS) systems are used in place of pitot tubes. This eliminates the issue of intense hypersonic heating caused by the poked-out pitot tube and concurrently enhances the vehicle's stealth performance. At present, there is less analysis and research work on neural network methods and FADS systems for complex profile leading-edge hypersonic vehicles. The impacts of thin leading edge and inlet components were taken into consideration during the redundant design and verification of the FADS systems with complicated profile leading edge, which was aimed at the subsonic/transonic conditions of autonomous return hypersonic vehicles in the landing stage. In the present study, the FADS systems on a typical complex profile sharp-nosed hypersonic vehicles with 15 integrated pressure orifices have been investigated numerically and experimentally. The pressure database has been set up by numerical simulations, with typical conditions verified through wind tunnel experiments. For complex leading-edge vehicle, four sets of FADS algorithms were constructed based on the neural network approach and redundancy design research was carried out, including 1 nine-orifice algorithm and 3 redundant algorithms. Results showed relatively high accuracy in the nine-orifice algorithm, where the estimation error is within 0.07° for an angle of attack, 0.3° for an angle of sideslip, 0.0012 for Mach number, and 1.5% for far-field static and dynamic pressure. Further, a fault management scheme has been proposed, where failure in individual orifice does not lead to fatal degradation in system performance.
| [1] |
陈广强, 豆国辉, 魏昊功, 等. 火星探测器大气数据测量方法[J]. 航空学报, 2022, 43(3): 626619.
CHEN G Q, DOU G H, WEI H G, et al. Air data sensing technology of Mars probe[J]. Acta Aeronautica et Astronautica Sinica, 2022, 43(3): 626619(in Chinese).
|
| [2] |
WHITMORE S, COBLEIGH B, HAERING E. Design and calibration of the X-33 flush airdata sensing (FADS) system[C]// Proceedings of the 36th AIAA Aerospace Sciences Meeting and Exhibit. Reston: AIAA, 1998: 201.
|
| [3] |
ROHLOFF T J, WHITMORE S A, CATTON I. Air data sensing from surface pressure measurements using a neural network method[J]. AIAA Journal, 1998, 36: 2094-2101. doi: 10.2514/2.312
|
| [4] |
ROHLOFF T J, WHITMORE S A, CATTON I. Fault-tolerant neural network algorithm for flush air data sensing[J]. Journal of Aircraft, 1999, 36(3): 541-549. doi: 10.2514/2.2489
|
| [5] |
WHITMORE S A, MOES T R. Measurement uncertainty and feasibility study of a flush airdata system for a hypersonic flight experiment: NASA TM-4627[R]. California: NASA, 1994: 3-15.
|
| [6] |
BAUMANN E, PAHLE J W, DAVIS M C, et al. X-43A flush airdata sensing system flight-test results[J]. Journal of Spacecraft and Rockets, 2010, 47(1): 48-61. doi: 10.2514/1.41163
|
| [7] |
PRUETT C D, WOLF H, HECK M L, et al. Innovative air data system for the Space Shuttle Orbiter[J]. Journal of Spacecraft and Rockets, 1983, 20(1): 61-69. doi: 10.2514/3.28357
|
| [8] |
KELLY G M, FINDLAY J T, COMPTON H R. Shuttle subsonic horizontal wind estimation[J]. Journal of Spacecraft and Rockets, 1983, 20(4): 390-397. doi: 10.2514/3.25612
|
| [9] |
KARLGAARD C D, KUTTY P, SCHOENENBERGER M. Coupled inertial navigation and flush air data sensing algorithm for atmosphere estimation[J]. Journal of Spacecraft and Rockets, 2017, 54(1): 128-140. doi: 10.2514/1.A33331
|
| [10] |
王鹏. 尖楔前体飞行器FADS系统的径向基网络建模及验证[J]. 气体物理, 2019, 4(3): 23-33.
WANG P. RBF neural network modeling and validation for FADS system applied to the vehicle with sharp wedged fore-bodies[J]. Physics of Gases, 2019, 4(3): 23-33(in Chinese).
|
| [11] |
王鹏, 胡远思, 金鑫, 等. 尖楔前体飞行器FADS系统的神经网络算法[J]. 空气动力学学报, 2017, 35(6): 777-780.
WANG P, HU Y S, JIN X, et al. Neural network algorithm for FADS system applied to the vehicles with sharp wedged fore-bodies[J]. Acta Aerodynamica Sinica, 2017, 35(6): 777-780(in Chinese).
|
| [12] |
王鹏, 金鑫. 尖锥前体飞行器FADS系统的人工神经网络建模及风洞试验研究[J]. 实验流体力学, 2019, 33(5): 57-63.
WANG P, JIN X. Study on artificial neural network modeling and wind tunnel test for the FADS system applied to the vehicle with sharp nosed fore-bodies[J]. Journal of Experiments in Fluid Mechanics, 2019, 33(5): 57-63(in Chinese).
|
| [13] |
王鹏, 胡远思, 金鑫. 尖楔前体飞行器FADS系统驻点压力对神经网络算法精度的影响[J]. 宇航学报, 2016, 37(9): 1072-1079.
WANG P, HU Y S, JIN X. Effect of stagnation pressure on the neural network algorithm accuracy for FADS system applied to the vehicle with sharp wedged fore-bodies[J]. Journal of Astronautics, 2016, 37(9): 1072-1079(in Chinese).
|
| [14] |
王鹏, 金鑫. 尖楔前体飞行器FADS系统测压孔故障对算法精度的影响[J]. 力学与实践, 2021, 43(6): 833-841.
WANG P, JIN X. Numerical effect of pressure ports fault in the fads system applied to flight vehicle with sharp wedged fore-bodies[J]. Mechanics in Engineering, 2021, 43(6): 833-841(in Chinese).
|
| [15] |
王鹏, 李秋红, 胡远思, 等. 尖楔前体飞行器FADS-α的求解精度研究[J]. 中国科学: 物理学 力学 天文学, 2015, 45(12): 98-108.
WANG P, LI Q H, HU Y S, et al. Research on solving accuracy for FADS-α applied to the vehicle with sharp wedged fore-bodies[J]. Scientia Sinica (Physica, Mechanica & Astronomica), 2015, 45(12): 98-108(in Chinese).
|
| [16] |
张斌, 于盛林. 嵌入式飞行参数传感系统的神经网络算法[J]. 航空学报, 2006, 27(2): 294-298.
ZHANG B, YU S L. Neural network algorithm for flush airdata sensing system[J]. Acta Aeronautica et Astronautica Sinica, 2006, 27(2): 294-298(in Chinese).
|
| [17] |
贾乾磊, 章卫国, 史静平, 等. FADS系统故障诊断方法研究[J]. 西北工业大学学报, 2020, 38(6): 1210-1217. doi: 10.1051/jnwpu/20203861210
JIA Q L, ZHANG W G, SHI J P, et al. Research on fault detection method of FADS system[J]. Journal of Northwestern Polytechnical University, 2020, 38(6): 1210-1217(in Chinese). doi: 10.1051/jnwpu/20203861210
|
| [18] |
丁智坚, 周欢, 吴东升, 等. 嵌入式大气数据测量系统技术研究进展[J]. 宇航学报, 2019, 40(3): 247-257.
DING Z J, ZHOU H, WU D S, et al. Review of flush air data sensing system[J]. Journal of Astronautics, 2019, 40(3): 247-257(in Chinese).
|
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