动态大气环境下高速飞行器气动噪声不确定性量化研究

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摘要临近空间内大气参数的动态变化会导致高速飞行器气动噪声具有显著的不确定性特征，准确量化气动噪声不确定性特征对飞行器结构设计具有重要意义。本文针对高速飞行器机翼结构，首先通过本征正交分解和代理模型技术建立气动噪声的降阶分析模型，提升气动噪声的分析效率；然后，基于降阶分析模型和大气参数的随机分布特性，高效量化动态大气环境下高速飞行器气动噪声的不确定性特征；最后，通过敏感性分析研究大气参数对气动噪声的影响程度。研究结果表明：本文所构建的降阶模型具有较高的分析效率和预示精度，能够准确高效地量化动态大气环境下飞行器气动噪声的不确定性特征；大气参数和气动噪声之间存在非线性关系，激波及激波后边界层分离非线性是造成气动噪声剧烈波动的重要原因；大气密度变化对气动噪声不确定度贡献较大。

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	郑伶华1
	2
	陈强1
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	李彦斌1
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	方芳3
	费庆国1
	2

关键词 ：动态大气环境, 气动噪声, 模型降阶, 神经网络, 敏感性分析

Abstract：Due to the dynamic variation of atmospheric parameters in near space, the aerodynamic noise of high-speed aircrafts presents significant uncertain characteristics, which is useful for structural design. In this paper, a typical wing of high-speed aircraft is taken as research model. First, the reduced order model for aerodynamic noise prediction is established, by using proper orthogonal decomposition and surrogate model, to improve the aerodynamic noise analysis efficiency. Then, based on the reduced order model and random distribution characteristics of the atmospheric parameters, the aerodynamic noise uncertainty characteristics are efficiently quantified. Finally, the influence of atmospheric parameters on aerodynamic noise is studied by sensitivity analysis. Results show that the uncertainty characteristics of aerodynamic noise could be accurately and efficiently quantified using the reduced order model. A nonlinear relationship between the atmospheric parameters and the aerodynamic noise can be observed. Shock wave and boundary layer separation behind shock wave are the important reasons for the aerodynamic noise violent fluctuation. The density variation contributes greatly to the uncertainty of aerodynamic noise.

Key words： dynamic atmosphere aerodynamic noise reduced order model neural network sensitivity analysis

收稿日期: 2022-04-21 出版日期: 2023-07-28

引用本文:

郑伶华1,2，陈强1,2，李彦斌1,2，方芳3，费庆国1,2. 动态大气环境下高速飞行器气动噪声不确定性量化研究[J]. 振动与冲击, 2023, 42(14): 306-313.
ZHENG Linghua1,2, CHEN Qiang1,2, LI Yanbin1,2,FANG Fang3, FEI Qingguo1,2 . Uncertainty quantification for the aerodynamic noise of high-speed aircrafts in dynamic atmospheric environment. JOURNAL OF VIBRATION AND SHOCK, 2023, 42(14): 306-313.

链接本文:

http://jvs.sjtu.edu.cn/CN/ 或 http://jvs.sjtu.edu.cn/CN/Y2023/V42/I14/306

[1] Pertsev N N, Semenov A I, Shefov N N. Empirical model of vertical structure of the middle stmosphere: Seasonal variationals and long-term changes of temperature and number density[J]. Advances in Space Research, 2006, 38(11): 2465-2469.
[2] Huang F T, Mayr H G, Russell J M, et al. Ozone and temperature decadal trends in the stratosphere, mesosphere and lower thermosphere, based on measurements from SABER on TIMED[J]. Annales Geophysicae, 2014, 32(8): 935-949.
[3] John S R, Kumar K K. Global normal mode planetary wave activity: a study using TIMED/SABER observations from the stratosphere to the mesosphere-lower thermosphere[J]. Climate Dynamics, 2016, 47(12): 3863-3881.
[4] Forbes J M, Xiao li Z, and Marsh D R. Solar cycle dependence of middle atmosphere temperatures[J]. Journal of Geophysical Research: Atmospheres, 2014, 119(16): 9615-9625.
[5] 陈闽慷, 杜涛, 胡雄, 等. 北半球高空大气参数波动对临近空间飞行热环境的影响[J]. 科学通报, 2017, 62(13): 1402-1409.
Chen Min-kang, Du Tao, Hu Xiong, et al. Effect of atmosphere parameter oscillation at high altitude in the northern hemisphere for near space hypersonic flight aerothermodynamic prediction. Chinese Science Bulletin, 2017, 62(13): 1402-1409.
[6] 李健, 侯中喜, 刘新建, 等. 基于扰动大气模型的动力推进高超声速飞行器弹道特性分析[J]. 国防科技大学学报, 2007, 29(4): 6-11.
LI Jian, HOU Zhong-xi, LIU Xin-jian, et al. Trajectories analyse for hypersonic vehicle with scramjet based on perturbation atmosphere model[J]. Journal of National University of Defense Technology, 2007, 29(4): 6-11.
[7] Robertson J. Prediction of flight fluctuating pressure environments including pro-tolerance induced flow: N71-36677[R]. Wyle Laboratories-Research Staff Report WR71-10.
[8] 刘振皓, 任方, 王骁峰, 等. 旋成体模型仪器舱脉动压力空间相关特性研究[J]. 宇航学报, 2016, 37(12): 1299-1305.
Liu Zhen-hao, Ren Fang, Wang Xiao-feng, et al. Study on spatial correlation characteristics of pulsating pressure in a rotating instrument cabin[J]. Journal of Astronautics, 2016, 37(12): 1299-1305.
[9] 吕文春, 汪建文, 段亚范, 等. 翼型凹变对风轮旋转噪声影响特性分析[J]. 振动与冲击, 2021, 40(1): 45-51+85.
LU Wen-chun, WANG Jian-wen, DUAN Ya-fan, et al. Effects of airfoil concave change on rotating noise of wind turbine [J]. Journal of Vibration and Shock, 2021, 40(1): 45-51+85.
[10] Robertson J E. Prediction of in flight fluctuating pressure environments including protuberance induced flow[R]. Wyle laboratories-research staff report, NASA-CR-119947, WR-71-10, 71N36677, 1971.
[11] 李鑫, 屈转利, 李耿, 等. 高效低噪的二维翼型优化设计[J]. 振动与冲击, 2017, 36(4): 66-72.
LI Xin, QU Zhuan-li, LI Geng, et al. A numerical optimization for high efficiency and low noise airfoils[J]. Journal of Vibration and Shock, 2017, 36(4): 66-72.
[12] 蒋树杰, 刘菲菲, 陈刚. 流固耦合振动效应对机翼气动噪声辐射的影响研究[J]. 振动与冲击, 2018, 37(19): 7-13.
JIANG Shu-jie, LIU Fei-fei, CHEN Gang. Influences of fluid-structure coupled vibration effect on airfoil aerodynamic noise radiation[J]. Journal of Vibration and Shock, 2018, 37(19): 7-13.
[13] 张冬冬, 郭勤涛. Kriging响应面代理模型在有限元模型确认中的应用[J]. 振动与冲击, 2013, 32(9): 187-191.
ZHANG Dong-dong, GUO Qin-tao. Application of Kriging response surface in finite element model validation[J]. Journal of Vibration and Shock, 2013, 32(9): 187-191.
[14] Liu S Y, Wang Y B, Qin N, et al. Quantification of airfoil aerodynamic uncertainty due to pressure sensitive paint thickness[J]. AIAA Journal, 2020, 58(4): 1432-1440.
[15] Batten P, Goldberg U, Charravarthy S. Reconstructed sub grid methods for acoustics predictions at all Reynolds numbers[R]. AIAA-2002-2511, 2002.
[16] Batten P, Ribaldone E, Casella M, et al. Towards a generalized non-Linear acoustics solver[C]. The 10th AIAA CEAS Aeroacoustics Conference. Manchester, Great Britan, May 10-12. 2004.
[17] Qiang L, Zhen B L, Xiong D, et al. Numerical investigation on flow field characteristics of dual synthetic cold/hot jets using POD and DMD methods[J]. Chinese Journal of Aeronautics, 2020, 33(1): 73-87.
[18] Simpson T, Booker A, Ghosh D, et al. Approximation methods in multidisciplinary analysis and optimization: a panel discussion[J]. Structural and Multidisplinary Optimization, 2004, 27(5): 303-313.
[19] 李磊, 秦卫阳, 张劲夫. 采用BP神经网络进行混沌运动控制[J]. 振动与冲击, 2006(5): 89-91+194.
LI Lei, QIN Wei-yang, ZHANG Jin-fu. Controlling chaosmotion to approach arbitrary objective orbit by BP neural network[J]. Journal of Vibration and Shock, 2006(5): 89-91+194.
[20] 刘晓东, 马飞, 张玉, 等. 基于BP神经网络的模型参考自适应姿态控制[J]. 航天控制, 2019, 37(6): 3-7.
Liu Xiao-dong, Ma Fei, Zhang Yu, et al. Model reference adaptive attitude control based on BP neural network[J]. Aerospace Control, 2019, 37(6): 3-7.
[21] Bin G F, Gao J J, Li X J, et al. Early fault diagnosis of rotating machinery based on wavelet packets-Empirical mode decomposition feature extraction and neural network[J]. Mechanical Systems and Signal Processing, 2012, 27: 696-711.
[22] Ngia L, Sjoberg J. Efficient training of neural nets for nonlinear adaptive filtering using a recursive Levenberg-Marquardt algorithm[J]. IEEE Transactions on Signal Processing, 2000, 48(7): 1915-1927.
[23] Widrow B, Mccool J. A comparison of adaptive algorithms based on the methods of steepest descent and random search[J]. IEEE transactions on antennas and propagation, 1976, 24(5): 615-637.
[24] Argyros I, Hilout S. On the Gauss-Newton method[J]. Journal of Applied Mathematics & computing, 2011, 35(1): 537-550.
[25] National Oceanic and Atmospheric Administration, National Aeronautics and Space Administration, United States Air Force. U.S. Standard Atmosphere, 1976. Technical Report, 1976. NASA-TM-X-74335.
[26] Justus C, Johnson D. The GRAM model: Status of development and future aspects[J]. Advances in Space Research, 1991, 19(4): 549-558.
[27] Justus C, Duvall A, Johnson D. Earth global reference atmospheric model(GRAM-99) and trace constituents[J]. Advances in Space Research, 2004, 34(8): 1731-1735.
[28] Sobol’ I. Global sensitivity indices for nonlinear mathematical models and their monte carlo estimates[J]. Mathematics and Computers in Sulation, 2001, 55: 271-280.