Citation: | YI Xiangyu, YAN Ming, CHEN Xing, et al. Investigation of a Data-Driven Method for Analyzing Impulse Tunnel Aerothermal Test Data[J]. PHYSICS OF GASES, 2024, 9(4): 65-82. DOI: 10.19527/j.cnki.2096-1642.1111 |
The test time of impulse tunnels is short, and the heat flux transducers can be easily interfered in the test time. Thus the aerothermal test data analysis of impulse tunnels is complex and usually carried out manually, which leads to low efficiency and nonuniform standards. In this paper, the signals of heat transducers used in the 1 m gun/shock dual-mode impulse tunnel at the China Academy of Aerospace Aerodynamics were investigated. A series of key parameters, e.g., the FFT voltage profile, the correlation profile of nozzle total pressure and transducer heat flux, as well as the time derivative of the heat flux profile, were determined. The characterization capabilities of these parameters with respect to significant features, e.g., the start/end time of the nozzle flow, the time boundary of different operation modes and the effectiveness of the transducer profiles, were certified. Moreover, a profile effectiveness estimation model, a test time interval selection algorithm and a transducer valid time selection algorithm were developed based on convolutional neural networks and density-based clustering algorithms. Thereby the aerothermal test data of impulse tunnels can be analyzed automatically with these data-driven methods. The results show that the accuracy of the profile effectiveness estimation model is as high as 98%. According to the comparison between algorithm and manual results in dealing with 3 568 profiles, about 91% of the results show a difference of less than 10%, which implies that the data-driven method has the value of application to wind tunnel tests.
[1] |
Anderson J D Jr. Hypersonic and high-temperature gas dynamics[M]. Reston: American Institute of Aeronautics and Astronautics, 2006.
|
[2] |
梁伟, 金华, 孟松鹤, 等. 高超声速飞行器新型热防护机制研究进展[J]. 宇航学报, 2021, 42(4): 409-424. https://www.cnki.com.cn/Article/CJFDTOTAL-YHXB202104003.htm
Liang W, Jin H, Meng S H, et al. Research progress on new thermal protection mechanism of hypersonic vehicles[J]. Journal of Astronautics, 2021, 42(4): 409-424(in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-YHXB202104003.htm
|
[3] |
喻成璋, 刘卫华. 高超声速飞行器气动热预测技术研究进展[J]. 航空科学技术, 2021, 32(2): 14-21. https://www.cnki.com.cn/Article/CJFDTOTAL-HKKX202102002.htm
Yu C Z, Liu W H. Research status of aeroheating prediction technology for hypersionic aircraft[J]. Aeronautical Science & Technology, 2021, 32(2): 14-21(in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-HKKX202102002.htm
|
[4] |
罗月培, 孙宗祥, 孙杭义, 等. 美国高超声速风洞试验能力发展综述[J]. 飞航导弹, 2021(6): 33-41. https://www.cnki.com.cn/Article/CJFDTOTAL-FHDD202106006.htm
|
[5] |
吴宁宁, 康宏琳, 罗金玲. 高速飞行器翼舵缝隙激波风洞精细测热试验研究[J]. 空气动力学学报, 2019, 37(1): 133-139. https://www.cnki.com.cn/Article/CJFDTOTAL-KQDX201901015.htm
Wu N N, Kang H L, Luo J L. Experimental study on fine thermal measurement of high-speed aircraft wing rudder gapin shock wave tunnel[J]. Acta Aerodynamica Sinica, 2019, 37(1): 133-139(in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-KQDX201901015.htm
|
[6] |
韩曙光, 贾广森, 文帅, 等. 磷光热图技术在常规高超声速风洞热环境实验中的应用[J]. 气体物理, 2017, 2(4): 56-63. DOI: 10.19527/j.cnki.2096-1642.2017.04.006
Han S G, Jia G S, Wen S, et al. Heat transfer measurement using a quantitative phosphor thermography system in blowdown hypersonic facility[J]. Physics of Gases, 2017, 2(4): 56-63(in Chinese). DOI: 10.19527/j.cnki.2096-1642.2017.04.006
|
[7] |
段毅, 姚世勇, 李思怡, 等. 高超声速边界层转捩的若干问题及工程应用研究进展综述[J]. 空气动力学学报, 2020, 38(2): 391-403. https://www.cnki.com.cn/Article/CJFDTOTAL-KQDX202002022.htm
Duan Y, Yao S Y, Li S Y, et al. Review of progress in some issues and engineering application of hypersonic boundary layer transition[J]. Acta Aerodynamica Sinica, 2020, 38(2): 391-403(in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-KQDX202002022.htm
|
[8] |
周炳康. 高马赫数V形钝前缘上下游激波干扰及气动载荷特性[D]. 合肥: 中国科学技术大学, 2023.
Zhou B K. Shock interactions and aerodynamic loads in the upstream/downstream flow of V-shaped blunt leading edges at high Mach numbers[D]. Hefei: University of Science and Technology of China, 2023(in Chinese).
|
[9] |
毕志献. 磷光热图技术及其在脉冲风洞中的应用[J]. 气体物理-理论与应用, 2013, 8(4): 1-13. https://cpfd.cnki.com.cn/Article/CPFDTOTAL-AGLU201409001149.htm
Bi Z X. Phosphor thermography technology for aero heating applications in impulse facilities[J]. Physics of Gases, 2013, 8(4): 1-13(in Chinese). https://cpfd.cnki.com.cn/Article/CPFDTOTAL-AGLU201409001149.htm
|
[10] |
谌君谋. FD-21高焓激波风洞流动环境的建立与诊断[D]. 北京: 中国航天空气动力技术研究院, 2021.
Shen J M. Establishment and diagnosis of flow environment in FD-21 high enthalpy shock tunnel[D]. Beijing: China Academy of Aerospace Aerodynamics, 2021(in Chinese).
|
[11] |
李旭东, 张赋, 史增民, 等. 壁面催化条件对气动热环境的影响研究[J]. 导弹与航天运载技术, 2020(1): 112-117. https://www.cnki.com.cn/Article/CJFDTOTAL-DDYH202001021.htm
Li X D, Zhang F, Shi Z M, et al. Research on the influence and mechanisms of catalytic wall condition on aerodynamic-heating[J]. Missiles and Space Vehicles, 2020(1): 112-117(in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-DDYH202001021.htm
|
[12] |
Wang L Y, Chen W H, Yuan Y, et al. Effect of porous plate weak jet on aerodynamic heating of high-speed vehicles[J]. Journal of Thermophysics and Heat Transfer, 2023, 37(2): 331-340.
|
[13] |
张天姣, 钱炜祺, 周宇, 等. 人工智能与空气动力学结合的初步思考[J]. 航空工程进展, 2019, 10(1): 1-11. https://www.cnki.com.cn/Article/CJFDTOTAL-HKGC201901002.htm
Zhang T J, Qian W Q, Zhou Y, et al. Preliminary thoughts on the combination of artificial intelligence and aerodynamics[J]. Advances in Aeronautical Science and Engineering, 2019, 10(1): 1-11(in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-HKGC201901002.htm
|
[14] |
尹迪义. 航空地面试验的智能系统[J]. 测控技术, 1998, 17(4): 36-38. https://www.cnki.com.cn/Article/CJFDTOTAL-IKJS199804013.htm
Yin D Y. The intelligent system for aeronautical ground test[J]. Measurement & Control Technology, 1998, 17(4): 36-38(in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-IKJS199804013.htm
|
[15] |
Kohonen T. Self-organized formation of topologically correct feature maps[J]. Biological Cybernetics, 1982, 43(1): 59-69.
|
[16] |
Motter M A. Control of the NASA Langley 16-foot transonic tunnel with the self-organizing map[C]. Proceedings of the 1999 American Control Conference. San Diego: IEEE, 1999.
|
[17] |
吕鹏涛, 惠增宏. NF-3风洞神经网络自适应稳风速控制系统研制[J]. 实验流体力学, 2009, 23(4): 82-86. https://www.cnki.com.cn/Article/CJFDTOTAL-LTLC200904019.htm
Lyu P T, Hui Z H. The development of wind velocity adaptive control system based on neural networks for NF-3 wind tunnel[J]. Journal of Experiments in Fluid Mechanics, 2009, 23(4): 82-86(in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-LTLC200904019.htm
|
[18] |
芮伟, 杜宁, 袁平, 等. 暂冲式高速风洞流场控制系统建模与仿真[J]. 实验流体力学, 2015, 29(6): 89-95. https://www.cnki.com.cn/Article/CJFDTOTAL-LTLC201506015.htm
Rui W, Du N, Yuan P, et al. Modeling and simulation of flow field control system in intermittent transonic wind tunnel[J]. Journal of Experiments in Fluid Mechanics, 2015, 29(6): 89-95(in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-LTLC201506015.htm
|
[19] |
金志伟, 杨兴锐, 苏北辰. 基于神经网络的风洞马赫数预测控制仿真研究[J]. 兵工自动化, 2016, 35(3): 59-60, 65. https://www.cnki.com.cn/Article/CJFDTOTAL-BGZD201603016.htm
Jin Z W, Yang X R, Su B C. Predictive control simulation research of mach number in wind tunnel based on neural network[J]. Ordnance Industry Automation, 2016, 35(3): 59-60, 65(in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-BGZD201603016.htm
|
[20] |
Rennie R M, Cain A B. Management of wind tunnel performance data using neural networks[R]. AIAA 2012-0321, 2012.
|
[21] |
马国辉. 4米×3米风洞自主式维修保障系统信息化平台设计与实现[D]. 成都: 电子科技大学, 2014.
Ma G H. The design and realization for autonomous maintenance support system information platform in 4 m×3 m wind tunnel[D]. Chengdu: University of Electronic Science and Technology of China, 2014(in Chinese).
|
[22] |
Rosario R A, Steinle F W Jr. Neural network application for optimizing multi-stage wind tunnel compressor efficiency[R]. AIAA 2002-0308, 2002.
|
[23] |
Rogers J L, Hill J S, LaMarsh Ⅱ W J, et al. Application of a neural network as a potential aid in predicting NTF pump failure[R]. N93-18332, 1993.
|
[24] |
Vlachos P P, Telionis D P. The design and testing of a smart balance system[R]. AIAA 99-3165, 1999.
|
[25] |
Vijayagopal R, Pathak M M, Rediniotis O K. Miniature multi-hole pressure probes-their neural network calibration and frequency response enhancement[R]. AIAA 98-0204, 1998.
|
[26] |
竹朝霞, 惠增宏, 金承信. 虚拟仪器技术在风洞测控智能化中的应用[J]. 实验技术与管理, 2006, 23(9): 76-79. https://www.cnki.com.cn/Article/CJFDTOTAL-SYJL200609027.htm
Zhu Z X, Hui Z H, Jin C X. Virtual instrument application for intelligent system in wind tunnel test[J]. Experimental Technology and Management, 2006, 23(9): 76-79(in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-SYJL200609027.htm
|
[27] |
Ben Mosbah A, Flores Salinas M, Botez R, et al. New methodology for wind tunnel calibration using neural networks-EGD approach[J]. SAE International Journal of Aerospace, 2013, 6(2): 761-766.
|
[28] |
张靖, 孙文举, 尼文斌, 等. 基于深度学习的风洞天平测力试验数据异常检测方法研究[J]. 实验流体力学, 2022, 36(6): 67-73. https://www.cnki.com.cn/Article/CJFDTOTAL-LTLC202206007.htm
Zhang J, Sun W J, Ni W B, et al. Study on deep learning-based anomaly detection method for wind tunnel balance force data[J]. Journal of Experiments in Fluid Mechanics, 2022, 36(6): 67-73(in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-LTLC202206007.htm
|
[29] |
陈强. 激波管流动的理论和实验技术[M]. 合肥: 中国科技大学, 1979.
|
[30] |
李素循. 典型外形高超声速流动特性[M]. 北京: 国防工业出版社, 2007.
|
[31] |
汪运鹏, 杨瑞鑫, 聂少军, 等. 基于深度学习技术的激波风洞智能测力系统研究[J]. 力学学报, 2020, 52(5): 1304-1313. https://www.cnki.com.cn/Article/CJFDTOTAL-LXXB202005010.htm
Wang Y P, Yang R X, Nie S J, et al. Deep-learning-based intelligent force measurement system using in a shock tunnel[J]. Chinese Journal of Theoretical and Applied Mechanics, 2020, 52(5): 1304-1313(in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-LXXB202005010.htm
|
[32] |
汪运鹏, 姜宗林. 激波风洞智能测力系统[C]. 第十九届全国激波与激波管会议论文集. 厦门, 2020: 569-580.
Wang Y P, Jiang Z L. Intelligent force measurement system using in shock tunnel[C]. Proceedings of the 19th Chinese National Symposium on Shock Waves. Xiamen, 2020: 569-580(in Chinese).
|
[33] |
郭磊, 林滋宜, 王瑞林, 等. 气动热试验数据的智能选取算法: 中国, 110033038A[P]. 2019-07-19.
|
[34] |
林滋宜. 基于深度学习的测热数据标注与智能计算[D]. 成都: 电子科技大学, 2020.
Lin Z Y. Calorimetric data labeling and intelligent computing based on deep learning[D]. Chengdu: University of Electronic Science and Technology of China, 2020(in Chinese).
|
[35] |
Chen X, Song K Q, Shen J M, et al. The aerodynamic and structural design of the moderate mass piston used in the large scale hypersonic gun tunnel FD-20A[C]. Proceedings of the 32nd International Symposium on Shock Waves. Singapore: National University of Singapore, 2019.
|
[36] |
陈星, 宫建, 师军等. 一种薄膜电阻温度计的制作方法: 中国, 102749148A[P]. 2012-10-24.
|
[37] |
林键, 宫建, 陈星, 等. "S"形薄膜的铂电阻热流传感器: 中国, 204286741U[P]. 2015-04-22.
|
[38] |
周志华. 机器学习[M]. 北京: 清华大学出版社, 2016.
Zhou Z H. Machine learning[M]. Beijing: Tsinghua University Press, 2016(in Chinese).
|
[39] |
郭超. 基于深度学习的岩石破裂信号处理方法及岩石裂隙演化规律研究[D]. 北京: 北京科技大学, 2021.
Guo C. A deep-learning-based rock fractural signal processing method and the research of rock crack evolution law[D]. Beijing: University of Science and Technology Beijing, 2021(in Chinese).
|
[40] |
Ester M, Kriegel H P, Sander J, et al. A density-based algorithm for discovering clusters in large spatial databases with noise[C]. Proceedings of the Second International Conference on Knowledge Discovery and Data Mining. Portland: AAAI Press, 1996.
|
[41] |
易翔宇, 李海燕, 陈星, 等. 一种脉冲风洞测热信号干扰消除的数据处理方法: 中国, 112378617A[P]. 2021-02-19.
|
[42] |
易翔宇, 陈勇富, 姚大鹏, 等. 一种人工智能辅助的脉冲风洞测热测压数据处理方法: 中国, 117421974A[P]. 2024.
|