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| A Bayesian statistical inference-based flutter boundary prediction method under turbulence excitation |
| WANG Ruochan1, YAN Gang1, LI Yang2, SHI Yuan1, ZHOU Daheng1 |
1.State Key Laboratory of Mechanics and Control of Mechanical Structures, College of Aerospace Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China;
2.College of Aerospace Engineering, Nanjing Vocational University of Industry Technology, Nanjing 210023, China |
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Abstract This paper proposes to extend Bayesian statistical inference method to flutter analysis for aircraft structures under excitation of atmospheric turbulence to consider the uncertainties in modal parameter identification and flutter boundary prediction. After the free decay responses are extracted from the structural responses under the excitation of atmospheric turbulence by natural excitation technique (NExT), based on Bayesian statistical inference, the posterior probability density functions of structural modal parameters are sampled and identified by Markov chain Monte Carlo (MCMC) algorithm. Then the probability density distribution for flutter velocity is obtained by Z-W flutter margin method to predict the flutter boundary and analyze the associated uncertainties. Numerical simulation studies are carried out to analyze the structural response data excited by atmospheric turbulence, and the effectiveness of the proposed method is verified.
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Received: 25 May 2022
Published: 28 June 2023
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[1] 唐炜，史忠科.飞机颤振试飞数据处理[M].北京：国防工业出版社，2018.
Tang Wei, Shi Zhongke. Aircraft flutter test flight data processing[M].Beijing:National Defense Industry Press,2018.
[2] Kayran A. Flight flutter testing and aeroelastic stability of aircraft [J]. Aircraft Engineering and Aerospace Technology, 2007, 79(5): 494-506.
[3] Zheng H, Liu J, Duan S. Flutter test data processing based on improved Hilbert-Huang transform [J]. Mathematical Problems in Engineering, 2018, 2018: 3496870.
[4] 郭洪涛，路波，吕彬彬，余立. 面向紊流激励响应的颤振亚临界预测方法[C].第13届中国系统仿真技术及其应用学术年会论文集，2011:955-960.
Guo Hongtao, Lu Bo, LV Binbin, Yu Li Flutter subcritical prediction method for turbulent excitation response [C] Proceedings of the 13th China Annual Conference on system simulation technology and its applications, 2011:955-960
[5] Sudha,U P V, Deodhare G S, Venkatraman K. Frequency, damping and flutter prediction from aircraft flight data using autoregressive model [J]. Journal of Aircraft, 2018, 55(6): 2179–2190.
[6] 顾文景，周丽. 改进VMD算法在颤振试验信号模态参数辨识中的应用[J]. 振动工程学报，2021, 34(2): 292-300.
Gu Wenjin, Zhou Li. Application of improved VMD algorithm in modal parameter identification of flutter test signals[J]. Journal of Vibration Engineering, 2021, 34(2): 292-300.
[7] Iovnovich M, Nahom T, Presman M, et al. Assessment of advanced flutter flight-test techniques and flutter boundary prediction methods [J]. Journal of Aircraft, 2018, 55(5): 1877-1889.
[8] 郑宇宁. 多源不确定性条件下气动弹性系统颤振可靠性分析方法[J]. 振动与冲击，2021, 40(3): 54-62.
Zheng Yuning. Flutter reliability analysis method for aeroelastic systems with multi-source uncertainty[J]. Journal of Vibration and Shock, 2021, 40(3): 54-62.
[9] Poirel D, Dunn S, Porter J. Flutter-margin method accounting for modal parameter uncertainties [J]. Journal of Aircraft, 2005, 42(5): 1236-1243.
[10] Abbasi A A, Cooper J E. Statistical evaluation of flutter boundaries from flight flutter test data [J]. The Aeronautical Journal, 2009, 113(1139): 41-51.
[11] Khalil M, Poirel D, Sarkar A. Bayesian analysis of the flutter margin method in aeroelasticity [J]. Journal of Sound and Vibration, 2016, 384: 56-74.
[12] James G H, Carne T G, Lauffer J P. The natural excitation technique for modal parameter extraction from operating wind turbines [R].SAND 92 1666.ON:DE93010611,Albuquerque, NM (United States): Sandia National Lab-oratory, 1993.
[13] 钟军军，董聪. 环境激励下识别结构模态自然激励-时域分解法[J]. 振动与冲击，2013, 32(18): 121-125.
Zhong Junjun, Dong Cong. Identification of structural modes under environmental excitations by natural excitations time- domain decomposition method [J]. Journal of Vibration and Shock, 2013, 32(18): 121-125.
[14] Kordkheili S A H, Massouleh S H M, Hajirezayi S, et al. Experimental identification of closely spaced modes using NExT-ERA [J]. Journal of Sound and Vibration, 2018, 412: 116-129.
[15] 李扬,周丽,杨秉才.基于自然激励技术的颤振边界预测[J].航空动力学报,2016,31(11):2744-2749.
Li Yang, Zhou Li, Yang Bingcai. Flutter boundary prediction based on natural excitation technology [J]. Journal of Aerospace Power,2016,31(11):2744-2749.
[16] Nichols J M, Link W A, Murphy K D et al. A Bayesian approach to identifying structural nonlinearity using free-decay response: application to damage detection composites [J]. Journal of Sound and Vibration, 2010, 329: 2995-3007.
[17] Yan G. A Bayesian approach for damage localization in plate-like structures using Lamb waves[J]. Smart Materials and Structures，2013，22(3): 035012.
[18] 刘金山，夏强. 基于MCMC算法的贝叶斯统计方法 [M].北京：科学出版社, 2016.
Liu Jinshan, Xia Qiang. Bayesian statistical method bas- ed on MCMC algorithm [M].Beijing:Science Press, 2016.
[19] Zimmerman N H, Weissenburger J T. Prediction of flutter onset speed based on flight testing at subcritical speeds [J]. Journal of Aircraft, 1964, 1(4): 190-202.
[20] 张伟伟，钟华寿，肖华，等. 颤振飞行试验的边界预测方法回顾与展望[J]. 航空学报，2015, 36(05): 1367-1384.
Zhang Weiwei, Zhong Huashou, Xiao Hua,et al.Review and prospect of boundary prediction methods for flutter flight tests[J]. Acta Aeronautica et Astronautica Sinica, 2015, 36(05): 1367-1384.
[21] 李扬. 基于大气紊流激励响应的机翼颤振边界预测研究[D]. 南京：南京航空航天大学，2018.
Li Yang. Prediction of wing flutter boundary based on atmospheric turbulence excitation Response[D]. Nanjing: Nanjing University of Aeronautics and Astronautics,2018.
[22] Hassig H J. An approximate true damping solution of the flutter equation by determinant iteration [J]. Journal of Aircraft, 1971, 8(11):885-889. |
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