基于频域本征正交分解的几何非线性动力学降阶

陈兵1,龚春林1,仇理宽2,谷良贤1

振动与冲击 ›› 2020, Vol. 39 ›› Issue (21) : 163-172.

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振动与冲击 ›› 2020, Vol. 39 ›› Issue (21) : 163-172.
论文

基于频域本征正交分解的几何非线性动力学降阶

  • 陈兵1,龚春林1,仇理宽2,谷良贤1
作者信息 +

Order reduction of geometrically nonlinear dynamic system based on POD in frequency domain

  • CHEN Bing1, GONG Chunlin1, QIU Likuan2, GU Liangxian1
Author information +
文章历史 +

摘要

为提升几何大变形条件下的结构非线性动力学系统的求解效率,研究指定频域段的动力学行为,以悬壁板为对象,利用频域本征正交分解(Proper Orthogonal Decomposition,POD)方法研究几何非线性结构动力学降阶问题。壁板的几何非线性刚度基于协同转动(Co-rotational,CR)方法求解,利用POD方法在指定频域范围内以计算快照生成基向量,通过Galerkin方法实现动力学系统降阶,非线性刚度以增量的形式加入外力项,系统的非线性行为通过广义外力的形式体现。通过对悬壁板的频域POD降阶分析与对比,结果表明:(1)对线性系统,频域POD降阶分析精度高,误差均在1%以内,求解时间远小于全阶系统,其中,1阶POD的求解时间不到全阶系统的50%;(2)对于非线性系统,在正弦和阶跃载荷作用下,一阶POD降阶分析误差小于1.5%,三阶误差小于0.5%,计算时间均少于全阶分析时间的75%;(3)对于多点随机激励下的几何非线性动力学,通过频域降阶,保留前六阶POD基向量,可保证降阶系统的分析误差在0.5%以内,且计算时间仅为全阶系统的79%。

Abstract

In order to improve solving efficiency of a structural nonlinear dynamic system under large geometric deformation condition and study its dynamic behavior in a specified frequency range, the proper orthogonal decomposition (POD) method in frequency domain was used to study the dynamic order reduction problem of a geometrically nonlinear structure with a cantilevered plate taken as the study object.The geometrically nonlinear stiffness of the plate was solved using the cooperative rotation (CR) method.POD base vectors were generated with snapshots computed in a specified frequency domain, and Galerkin method was used to realize the order reduction of dynamic system.The nonlinear stiffness was added to the external force term in the form of increment, and the nonlinear behavior of the system was reflectedin the form of generalized external force.The POD order reduction analysis in frequency domain and comparison were done for the cantilevered plate.Results showed that (1) for a linear system, the POD order reduction analysis in frequency-domain has high precision, the error is less than 1%, and its solving time is far less than that for the full order system, the solving time for 1 order POD is less than 50% of that for the full order system; (2) for a nonlinear system, the error of 1 order POD analysis is less than 1.5%, and the error of 3 order POD analysis is less than 0.5%, the solving time for the two cases is less than 75% of that for the full order analysis under sine and step loads; (3) for a geometrically nonlinear dynamic system under multi-point random loads, if the first 6 orders POD base vectors are kept after order reduction in frequency domain, the reduced order system’s analysis error is less than 0.5% and its solving time is just 79% of that for the full order system.

关键词

非线性动力学 / 降阶 / 本征正交分解 / 频域 / 协同转动方法 / 几何非线性

Key words

nonlinear dynamics / order reduction / proper orthogonal decomposition(POD) / frequency domain / cooperative rotation method / geometrically nonlinear

引用本文

导出引用
陈兵1,龚春林1,仇理宽2,谷良贤1. 基于频域本征正交分解的几何非线性动力学降阶[J]. 振动与冲击, 2020, 39(21): 163-172
CHEN Bing1, GONG Chunlin1, QIU Likuan2, GU Liangxian1. Order reduction of geometrically nonlinear dynamic system based on POD in frequency domain[J]. Journal of Vibration and Shock, 2020, 39(21): 163-172

参考文献

[1] 王勖成. 有限单元法[M]. 北京:清华大学出版社,2003.
Wang Xu-cheng. Finite Element Method. Beijing: Tsinghua Press, 2003.
[2] C. A. Felippa, B. Haugen. A unified formulation of small-strain corotational finite elements: I. Theory [J]. Computer Methods in Applied Mechnics and Engneering, 2005, 194(21): 2285-2335.
[3] S. E. Stapleton, A. M. Waas. Co-rotational Formulation for Bounded Joint Finite Elements [C]. 53rd AIAA/ASME/ ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference, AIAA 2012-1449.
[4] YANG JinSong, XIA PinQi. Finite element corotational formulation for geometric nonlinear analysis of thin shells with large rotation and small strain [J]. Science China, 2012, 55(11): 3142-3152.
[5] P. Bisegna, F. Caselli, S. Marfia, et al. A new SMA shell element based on the corotational formulation [J]. Computational Mechanics, 2014, 54(5): 1315-1329.
[6] S. K. Chimakurthi, B. K. Stanford, C. E. S. Cesinik, et al. Flapping Wing CFD/CSD Aeroelastic Formulation Based on a Co-rotational Shell Finite Element [C]. 51rd AIAA/ASME/ ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference, AIAA 2009-2412.
[7] 周强,陈刚,李跃明.基于CFD降阶的非线性气动弹性稳定性分析[J]. 振动与冲击,2016, 35(16):17-23.
ZHOU Qiang, CHEN Gang, LI Yueming. Nonlinear Aeroelastic Stability analysis based on CFD reduced order model[J]. Journal of Vibration and Shock, 2016, 35(16): 17-23.
[8] 仲继泽,徐自力.基于动网格降阶算法的机翼颤振边界预测[J]. 振动与冲击,2017,36(4):185-191.
ZHONG Jize, XU Zili. Wing Flutter Prediction using a Reduced Dynamic Mesh Method[J]. Journal of Vibration and Shock, 2017, 36(4): 185-191.
[9] D. Bonomi, A. Manzoni, A. Quarteroni. A Matrix DEIM Technique for Model Reduction of Nonlinear Parametrized problems in Cardiac Mechanics. Comput. Methods Appl. Mech Engrg. 324 (2017) :300-326.
[10] S. Chaturantabut. Temporal Localized Nonlinear Model Reduction with a Priori error Estimate. Applied Numerical Mathematics, 119 (2017) 255-238.
[11] M. Alotaibi, E. Chung. Global-loacal Model Reduction for Heterogeneous Forchheimer Flow. Journal of Computational and Applied Mathematics, 321 (2017) 160-184.
[12] S. Ilbeigi, D. Chelidze. Persistent Model Order Reduction for Complex Dynamical Systems using Sooth Orthogonal Decomposition. Mechanical Systems and Signal Processing, 96 (2017) 125-138.
[13] M. Mordhorst, T. Strecker, D. Wirtz, et al. POD-DEIM Reduction of Computational EMG Models. Journal of Computational Science, 19 (2017) 86-96.
[14] D. F. C. Silva, A. L. G. A. Coutinho. Practical implementation aspects of Galerkin reduced order models based on proper orthogonal decomposition for computational fluid dynamics [J]. J Braz. Soc. Mech. Sci. Eng., 2015, 37(4): 1309-1327.
[15] 罗佳奇,段焰辉,夏振华. 基于自适应本征正交分解混合模型的跨音速流场分析. 物理学报,2016,65(2):1-9
Luo Jiaqi, Duan Yanhui, Xia Zhenhua. Transonic Flow Reconstruction by an Adaptive Proper Orthogonal Decomposition Hybrid Model. Acta Phys. Sin, 2016, 65(12):1-9.
[16] 张立章,尹泽勇,米栋,等. 基于本征正交分解的离心压气机多学科设计优化. 推进技术,2017,02(38):323-333.
Zhang Lizhang, Yin Zeyong, Mi Dong, et al. Multidisciplinary Design Optimization for Centrifugal Compressor based on Proper Orthogonal Decomposition. Journal of Propulsion Technology, 2017, 02(38):323-333.
[17] 梅冠华,康灿,张家忠.二维壁板颤振的本征正交分解降阶模型研究[J].振动与冲击,2017,36(23):144-151.
MEI Guanhua, KANG Can, ZHANG Jiazhong. Reduced Order Model based on Proper Orthogonal Decomposition for Two-dimensional Pannel Flutter[J]. Journal of Vibration and Shock, 2017, 36(23): 144-151.
[18] CHEN Gang, LI YueMing and YAN GuiRong. A nonlinear POD reduced order model for limit cycle oscillation prediction [J]. SCIENCE CHINA Physics, Mechanics & Astronomy, 2010, 53(7): 1325-1332.
[19] K. Willcox. Unsteady flow sensing and estimation via the gappy proper orthogonal decomposition [J]. Computers & Fluids , 2006, 35(2): 208-226.
[20] Cusumano J., Sharkady M., Kimble B. . Spatial coherence measurements of a chaotic flexible-beam impact oscillator [J]. Am. Soc. Mech. Eng. Aerospace Div. (Publication) AD, 1993, 33(1): 13-22.
[21] T. Kim. Frequency-Domain Karhunen-Loeve Method and Its Application to Linear Dynamic Systems [J]. AIAA Journal, 1998, 36(11): 2117-2123.
[22] T. Kim. Efficient Reduced-Order System Identification for Linear Systems with Multiple Inputs [J]. AIAA Journal, 2005, 43(7): 1455-1464.
[23] T. Kim. Surrogate reduction for linear dynamic systems based on a frequency domain modal analysis [J]. Computational Mechanics, 2015, 56(4): 709-723.
[24] T. Kim, K. S. Nagaraja, K. G. Bhatia. Order Reduction of State-Space Aeroelastic Models Using Optimal Modal Analysis [J]. Journal of Aircraft, 2004, 41(6): 1440-1448.
[25] D. Amsallem, Charbel Farhat. Interpolation Method for Adapting Reducer-Order Models and Application to Aeroelasticity. AIAA Journal, 2008, 46(7): 1803-1813.
[26] G. Weickum, M. S. Eldred, K. Maute. A multi-point reduced-order modeling approach of transient structural dynamics with application to robust design optimization [J]. Struc Multidisc Optim, 2009, 38(6): 599-611.
[27] F. Bamer, C. Bucher. Application of the proper orthogonal decomposition for linear and nonlinear structures under transient excitations [J]. Acta Mechnica, 2012, 223(12): 2549-2563.
[28] K. Calberg, C. Bou-Mosleh, C. Farhat. Efficient non-linear model reduction via a least-squares Petrov-Galerkin projection and compressive tensor approximations [J]. International Journal for Numerical Methods in Engneering, 2011, 86(2): 155-181.

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