Research on FADS technology of diamond-nosed aircraft without stagnation pressure
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摘要: 现代战斗机受头部火控雷达等设备限制，无法在机头驻点附近开设测压孔，缺乏驻点压力会导致常规嵌入式大气数据传感系统测试精度大大下降。针对典型战斗机常用的菱形前体外形，对无驻点压力FADS（Flush Air Data Sensing, FADS）系统解算算法及精度进行研究。通过亚跨声速风洞校准试验，获得了测压孔压力分布特性，基于卡尔曼滤波算法构建了无驻点压力FADS技术。通过引入差压数据改进了算法，改进算法实现了部分解耦，提高了解算精度且迭代次数较少，计算量较小。风洞试验结果表明：无驻点压力解算算法可以在外插车次下较好地解算大气参数，其中迎角测量精度为0.33°，侧滑角测量精度为0.30°，静压测量精度为0.67%，马赫数测量精度为0.011。
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关键词:
- 菱形前体 /
- 无驻点压力 /
- 嵌入式大气数据传感系统 /
- 卡尔曼滤波 /
- 风洞试验
Abstract: Limited by equipment such as fire control radars near the nose, modern fighters cannot set pressure ports near the stagnation point, which causes the test accuracy of the conventional flush air data sensing system to greatly decline. For the diamond nose used in the typical fighter, the algorithm and accuracy of FADS (Flush Air Data Sensing) without stagnation pressure are studied. Through subsonic and transonic wind tunnel calibration experiments, the pressure distribution characteristics of the pressure ports were obtained and the FADS technology without stagnation pressure was constructed based on the Kalman filtering algorithm. The algorithm has been improved by importing differential pressure. The improved algorithm is partly decoupled, resulting in improved accuracy with few iterations and low computational complexity. The experimental results show that the algorithm without stagnation pressure can effectively work out air data in external experiment, with an accuracy of 0.33° for angle of attack, 0.30° for angle of sideslip, 0.67% for static pressure, and 0.011 for Mach number.-
Key words:
- diamond nose /
- non-stagnation /
- flush air data sensing system /
- Kalman filter /
- wind tunnel test
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图 4 不同马赫数下Cp1随迎角和侧滑角变化的空间曲面
Figure 4. Cp1 with angle of attack and sideslip at different Mach numbers
图 8 FADS优化算法测量值与真实值对比
Figure 8. Comparison of improved FADS measurement with real values
表 1 测压孔位置信息
Table 1. Location information of pressure ports
测压孔编号 轴向位置/mm 圆周角/(°) 1 35.0 0 2 52.5 0 3 70.0 0 4 87.5 0 5 105.0 0 6 70.0 −30 7 70.0 −60 8 70.0 −90 9 70.0 30 10 70.0 60 11 70.0 90 12 35.0 180 13 70.0 180 14 105.0 180 
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表 2 精度提升幅度
Table 2. The increase of accuracy
测压孔编号 迎角精度
/(°)侧滑角精度
/(°)静压精度
/%马赫数
精度3、6、7、10、12、14 −0.07 0.14 0.47 0.006 1、6、7、10、12、14 0.89 1.02 −0.08 0.059 1、3、6、7、10、14 0.59 0.14 0.37 0.018 1、3、6、7、10、12 0.75 0.61 0.05 0.035 3、7、9、10、12、14 1.13 0.25 −0.02 0.060 3、6、9、10、12、14 −0.22 0.13 0.37 0.004 3、6、7、9、12、14 −0.20 0.19 0.25 0.003
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[1] JOST M, SCHWEGMANN F, KOHLER T. Flush air data system – an advanced air data system for the aerospace industry[C]//Proc of the AIAA Guidance, Navigation, and Control Conference and Exhibit. 2004. . [2] 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. doi: 10.3873/j.issn.1000-1328.2019.03.001DING 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. doi: 10.3873/j.issn.1000-1328.2019.03.001 [3] WHITMORE S, MOES T, LARSON T. Preliminary results from a subsonic high-angle-of-attack flush airdata sensing (HI-FADS) system: Design, calibration, algorithm development, and flight test evaluation[C]//Proc of the 28th Aerospace Sciences Meeting. 1990. . [4] WEISS S. Comparing three algorithms for modeling flush air data systems[C]//Proc of the 40th AIAA Aerospace Sciences Meeting & Exhibit. 2002. . [5] WHITMORE S, COBLEIGH B, HAERING E Jr. Design and calibration of the X-33 flush airdata sensing (FADS) system[C]//Proc of the 36th AIAA Aerospace Sciences Meeting and Exhibit. 1998. . [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] 王禹, 郑伟, 童建忠, 等. 飞翼飞机嵌入式大气数据系统算法研究[J]. 测控技术, 2022, 41(9): 101–106. doi: 10.19708/j.ckjs.2021.08.263WANG Y, ZHENG W, TONG J Z, et al. Flush air data sensing system algorithm of flying wing aircraft[J]. Measurement & Control Technology, 2022, 41(9): 101–106. doi: 10.19708/j.ckjs.2021.08.263 [8] HENRY M, WOLF H, SIEMERS P. An evaluation of Shuttle Entry Air Data System (SEADS) flight pressures - Comparisons with wind tunnel and theoretical predictions[C]//Proc of the 15th Aerodynamic Testing Conference. 1988. . [9] STEPHAN T, MARKUS S, MICHAEL C, et al. Hybrid navigation system for the SHEFEX II mission[C]//Proc of the AIAA Guidance, Navigation and Control Sensor Technologies in Europe. 2008. . [10] 王希洋, 柏楠, 苑景春, 等. FADS系统变管径引气管路压力延迟误差补偿方法[J]. 战术导弹技术, 2015(2): 37–42. doi: 10.16358/j.issn.1009-1300.2015.02.08WANG X Y, BAI N, YUAN J C, et al. Compensation method for pressure delay error of variable diameter exhaust pipe in FADS system[J]. Tactical Missile Technology, 2015(2): 37–42. doi: 10.16358/j.issn.1009-1300.2015.02.08 [11] 李清东, 陈璐璐, 张孝功, 等. FADS快速智能故障检测和诊断技术[J]. 系统工程与电子技术, 2009, 31(10): 2544–2546. , 2009, 31(10): 2544–2546. doi: 10.3321/j.issn:1001-506X.2009.10.058 LI Q D, CHEN L L, ZHANG X G, et al . Flush airdata sensing system fast intelligent fault detection and diagnosis technology[J]. Systems Engineering and Electronics,2009 ,31 (10 ):2544 –2546 . doi: 10.3321/j.issn:1001-506X.2009.10.058[12] 杨胜江, 赵景朝, 杨志红. 嵌入式大气数据传感与惯性导航信息融合方法研究[J]. 战术导弹技术, 2016(2): 95–100. doi: 10.16358/j.issn.1009-1300.2016.02.17YANG S J, ZHAO J C, YANG Z H. Research on information fusion method of embedded atmospheric data sensing and inertial navigation[J]. Tactical Missile Technology, 2016(2): 95–100. doi: 10.16358/j.issn.1009-1300.2016.02.17 [13] 陈广强, 刘吴月, 豆修鑫, 等. 吸气式空空导弹FADS系统设计[J]. 中国科学:技术科学, 2016, 46(11): 1193–1206. doi: 10.1360/N092016-00258CHEN G Q, LIU W Y, DOU X X, et al. Flush air data sensing system design for air breathing air-to-air missile[J]. Scientia Sinica:Technologica, 2016, 46(11): 1193–1206. doi: 10.1360/N092016-00258 [14] 王岩, 郑伟. 分布嵌入式大气数据系统算法的初步研究[J]. 飞机设计, 2008, 28(6): 5–11,26. doi: 10.3969/j.issn.1673-4599.2008.06.002WANG Y, ZHENG W. Elementary study on the distributed flush air data system arithmetic[J]. Aircraft Design, 2008, 28(6): 5–11,26. doi: 10.3969/j.issn.1673-4599.2008.06.002 [15] 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. doi: 10.11729/syltlx20180125WANG 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. doi: 10.11729/syltlx20180125 [16] 陈广强, 刘吴月, 豆修鑫, 等. 超声速飞行器FADS系统实时解算设计与验证[J]. 空气动力学学报, 2018, 36(4): 561–570. doi: 10.7638/kqdlxxb-2016.0073CHEN G Q, LIU W Y, DOU X X, et al. Flush air data sensing system real-time solving design and verification for supersonic vehicle[J]. Acta Aerodynamica Sinica, 2018, 36(4): 561–570. doi: 10.7638/kqdlxxb-2016.0073 [17] WHITMORE S, MOES T, CZERNIEJEWSKI M, et al. Application of a flush airdata sensing system to a wing leading edge(LE-FADS)[C]//Proc of the 31st Aerospace Sciences Meeting. 1993. . [18] 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. doi: 10.3873/j.issn.1000-1328.2016.09.006WANG 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. doi: 10.3873/j.issn.1000-1328.2016.09.006 [19] WHITMORE S A, MOES T R, LEONDES C T. Development of a pneumatic high-angle-of-attack flush airdata sensing system[J]. Control and Dynamic Systems, 1992: 453-511. . [20] 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 [21] TAKAKI R, TAKIZAWA M, et al. ADS measurement of HYFLEX (HYpersonic FLight EXperiment)[C]//Proc of the 35th Aerospace Sciences Meeting and Exhibit. 1997. . [22] KARLGAARD C D, KUTTY P, SCHOENENBERGER M, et al. Mars entry atmospheric data system trajectory reconstruction algorithms and flight results[C]//Proc of the 51st AIAA Aerospace Sciences Meeting including the New Horizons Forum and Aerospace Exposition. 2013. . [23] 黄喜元, 陈洪波, 朱如意. 高超声速飞行器嵌入式大气数据获取技术研究[J]. 导弹与航天运载技术, 2017(3): 58–64. doi: 10.7654/j.issn.1004-7182.20170313HUANG X Y, CHEN H B, ZHU R Y. Research on air data acquisition technology of hypersonic vehicles[J]. Missiles and Space Vehicles, 2017(3): 58–64. doi: 10.7654/j.issn.1004-7182.20170313 -
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