强迫振动和极限环振动时域响应的高效预测方法研究

刘艳 1,白俊强 1,华俊 2,刘南 1

振动与冲击 ›› 2016, Vol. 35 ›› Issue (13) : 140-147.

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振动与冲击 ›› 2016, Vol. 35 ›› Issue (13) : 140-147.
论文

强迫振动和极限环振动时域响应的高效预测方法研究

  • 刘艳 1,白俊强 1,华俊 2,刘南 1
作者信息 +

Investigation on Efficient Prediction for Time Responses of Forced Oscillation and Limit Cycle Oscillation

  •  LIU Yan 1  BAI Junqiang 1  HUA Jun 2  LIU Nan 1 
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文章历史 +

摘要

建立一种基于改进Kriging的KSBRF(Kriging-Surrogate-Based Recurrence Framework)降阶模型(Reduced-Order Model, ROM),用于高效地预测非线性非定常气动力及力矩、极限环振动(Limit Cycle Oscillations, LCO)等。本文首先基于Kriging代理模型建立非线性系统输入-输出关系的循环预测框架。然后对翼型做沉浮/俯仰组合运动时的非线性非定常气动力进行辨识。结果表明:在固定来流马赫数情况下,KSBRF预测结果与CFD计算结果吻合良好,阻力系数及俯仰力矩系数等的平均预测误差均在2.0%以内;在变来流马赫数情况下,升力系数和俯仰力矩系数的平均预测误差在2.5%以内,而阻力系数的预测误差则稍大,但也不超过7.0%。通过研究不同m、n取值对模型精度的影响,得出考虑历史效应有利于提高当前时间步的模型预测精度。除此之外,本文还对NACA64A010翼型的LCO振动形态进行预测,预测结果与CFD计算结果吻合良好,误差均保证均小于5.17%。
 

Abstract

A nonlinear reduced-order model (ROM), Kriging surrogate-based recurrence framework (KSBRF), is built. Because the Kriging interpolation has the ability of estimation nonlinear input-output relationship, the nonlinear aerodynamics and the characteristics of LCO (Limit Cycle Oscillations) can be estimated by KSBRF ROM. First, the relationship of input and output of nonlinear systems is built. Then, do the identification of nonlinear unsteady aerodynamics when the airfoil do the pitch/plunge oscillate using the KSBRF ROM. Otherwise, under the same free-stream Mach number, the ROM results also can match the CFD results very well, the mean of estimated errors of drag coefficient and pitch moment coefficient are under 2.0%. Considering the variable free-stream Mach number, the mean of estimated errors of lift coefficient and pitch moment coefficient are under 2.5%, the error of preceding drag coefficient is under 7%. Considering the history effect is helpful to improve the ROM’s accuracy, through studying the effects of ROM’s accuracy based on different m, n values. Otherwise, the LCO characteristics of NACA64A010 airfoil are estimated by the nonlinear ROM. The identification results match the CFD results very well, the errors are under 5.17%.

 

关键词

代理模型 / 降阶模型 / Kriging插值 / 非定常气动力 / 极限环振动

Key words

Surrogate model / Reduced-order model / Kriging interpolation / Unsteady aerodynamic / Limit cycle oscillations

引用本文

导出引用
刘艳 1,白俊强 1,华俊 2,刘南 1. 强迫振动和极限环振动时域响应的高效预测方法研究[J]. 振动与冲击, 2016, 35(13): 140-147
LIU Yan 1 BAI Junqiang 1 HUA Jun 2 LIU Nan 1 . Investigation on Efficient Prediction for Time Responses of Forced Oscillation and Limit Cycle Oscillation[J]. Journal of Vibration and Shock, 2016, 35(13): 140-147

参考文献

[1]  Lieu T, Farhat C. Adaptation of aeroelastic reduced-order models and application to an F-16 configuration[J]. AIAA Journal,2007,45 (6):1244–1257.
[2] Hall K C, Thomas J P, Dowell E H. Proper orthogonal decomposition technique for transonic unsteady aerodynamic flows[J]. AIAA Journal,2000 38 (10):1853–1862.
[3] Thomas J P, Dowell E H, Hall K C. Using automatic differentiation to create a nonlinear reduced-order- model aerodynamic solver[J]. AIAA Journal,1020,48 (1):19–24.
[4] Lucia D J, Beran P S, Silva W A. Reduced-order modeling: new approaches for computational physics[J]. Progress in Aerospace Sciences,2004,40 (2004):51–117.
[5] 邱亚松,白俊强,华俊. 基于本征正交分解和代理模型的流场预测方法.航空学报.2013,34 (6):1249-1260.
Qiu Ya-song, Bai Jun-qiang, Hua Jun. Flow field estimation method based on proper orthogonal decomposition and surrogate model[J]. Acta Aeronautica et Astronautica Sinica.2013,34 (6):1249-1260.
[6] Silva W. Identification of nonlinear aeroelastic systems based on the Volterra theory: progress and opportunities[J]. Nonlinear Dynamics,2005,39:25–62.
[7] J. P. Thomas, C. H. Custer, E. H. Dowell, et al. F-16 fighter aeroelastic computations using a harmonic balance implementation of the OVERFLOW2 flow solver[C]. AIAA Paper 2010-2632. Presented at the 51st AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Meterials Conference.
[8] Liu L, Friedmann P P, Padthe A K. An approximate unsteady aerodynamic model for flapped airfoils including improved drag predictions[C]. Proceedings of the 34th European Rotorcraft Forum,Liverpool,UK,2008: 1037-1081.
[9] Trizila P C, Kang C, Visbal M R, et al. Unsteady fluid physics and surrogate modeling of low Reynolds number, flapping airfoils[C]. 38th Fluid Dynamics Conference and Exhibit, Seattle, WA, AIAA Paper 2008-3821,2008.
[10] Trizila P C, Kang C, Visbal M R, et al. A surrogate model approach in 2D versus 3D flapping wing aerodynamic analysis[C]. 12th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference, Victoria,British Columbia, AIAA Paper 2008-5914,2008.
[11] Suresh S, Omkar S N, Mani V, et al. Lift coefficient prediction at high angle of attack using recurrent neural network[J]. Aerospace Science and Technology,2003,7 (8):595–602.
[12] Marques F D, Anderson J. Identification and prediction of unsteady transonic aerodynamic loads by Multi-Layer Functionals[J]. Journal of Fluids and Structures,2001,15:83–106.
[13] Lieu T, Farhat C. Adaptation of POD-based aeroelastic ROMs for varying mach number and angle of attack: application to a complete F-16 configuration[C]. 2005 U. S. Air Force T&E Days.2005-7666.
[14] Glaz B, Liu L, Friedmann P P. Reduced order nonlinear unsteady aerodynamic modeling using a Surrogate Based Recurrence Framework[J]. AIAA Journal, 2010, 48 (10):2418–2429.
[15] Glaz B, Liu L, Friedmann P P, et al. A Surrogate-Based approach to reduced-order dynamic stall modeling[J]. Journal of the American Helicopter Society, 2012, 57 (2): 1–9.
[16] Glaz B, Liu L, Friedmann P P, et al. A Surrogate Based Approach to reduced-order dynamic stall modeling[C]. 51st AIAA/ASME/ASCHE/AHS/ASC Structures, Structural Dynamics and Materials Conference, Orlando, FL, AIAA Paper 2010-3042,2010,1–24.
[17] Glaz B, Friedmann P P, Liu L. Reduced order dynamic stall modeling with swept flow effects using a Surrogate Based Recurrence Framework[J]. AIAA Journal, 2013, 51 (4):910–921.
[18] 王博斌,张伟伟,叶正寅.基于神经网络模型的动态非线气动力辨识方法[J]. 航空学报. 2010, 31(7):1379-1388.
Wang Bobin, Zhang Weiwei, Ye Zhengyin. Unsteady nonlinear aerodynamics identification based on neural network model[J]. Acta Aeronautica et Astronautica Sinica. 2010, 31(7):1379-1388.
[19] Sacks J, Schiller S B, Welch W.J. Designs for Computer Experiments[J]. Technometrics.1989,31 (1):41-47.
[20] 阎平凡,张长水. 人工神经网络与模拟进化计算[M]. 清华大学出版社. 2005.
Yan Ping-fan, Zhang Chang-shui. Artificial neural networks and evolutionary computation simulation[M]. Tsinghua University Press. 2005.
[21] Moody J E, Darken C J. Fast Learning in Networks of Locally-Tuned Processing Units[J]. Neural Computation, 1989, 1(2): 281-294.
[22] Vapnik V N., Somla A. Support Vector Method for Function Approximation[J], Regression Estimation and Signal Processing. 1997.
[23] Sacks J, Welch W J, Mitchell T J, Wynn H. Design and analysis of computer experiments[J]. Statistical Science, 1989 4(4):409-423.
[24] Simpson T W, Toropov V, Balabanov V, et al. Design and analysis of computer experiments in multidisciplinary design optimization: a review of how we have come or not[C]. 12th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference, Victoria, British Colombia, 10-12 September 2008.
[25] Queipo N V, Haftka R T, Shyy W, et al. Surrogate-Based analysis and optimization[J]. Progress in Aerospace Sciences, 2005, 41:1–28.
[26] Leontaritis I J, Billings S A, Input-output parametric models for nonlinear systems[J]. International Journal of Control,1985,41 (2): 03–344.
[27] Levin A U, Narendra K S. Control of nonlinear dynamical systems using neural networks—Part II: observability, identification, and control[J]. IEEE Transactions on Neural Networks,1996,7 (1):30–42.
[28] Jin R., Chen W, Sudjianto, A. An efficient algorithm for constructing optimal design of computer experiments[J]. Journal of Statistical Planning and Inference, 2005, 134 (1):268–287.
[29] Martin J, Simpson T. Use of Kriging models to approximate deterministic computer models[J]. AIAA Journal, 2005 43 (4):853–863.
[30] Jones D R. A taxonomy of global optimization methods based on response surfaces[J]. Journal of Global Optimization. 2001, 21:345–383.
[31] 赵永辉. 气动弹性力学与控制[M]. 科学出版社. 2007.
ZHAO Yong-hui. Aeroelastic Mechanics and Dominator[M]. Press of science. 2007.
[32] Thomas J P, Dowell E H, Hall K C. Nonlinear inviscid aerodynamic effects on transonic divergence, flutter, and limit-cycle oscillations[J]. AIAA Journal. 2002 40(4): 638-646.
 

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