基于非线性状态空间辨识的气动弹性模型降阶

张家铭; 杨执钧; 黄锐

doi:10.6052/0459-1879-19-287

基于非线性状态空间辨识的气动弹性模型降阶

doi: 10.6052/0459-1879-19-287

南京航空航天大学机械结构力学及控制国家重点实验室, 南京210016

基金项目: 1) 国家自然科学基金项目(11972180);机械结构力学及控制国家重点实验室面上项目(MCMS-I-0118G02)

详细信息

通讯作者:
黄锐

中图分类号: V211,V215.3
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- 被引次数: 0
出版历程
- 收稿日期: 2019-10-17
- 刊出日期: 2020-02-10

REDUCED-ORDER MODELING FOR AEROELASTIC SYSTEMS VIA NONLINEAR STATE-SPACE IDENTIFICATION

State Key Laboratory of Mechanics and Control of Mechanical Structures, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China

摘要

摘要: 高维、非线性气动弹性系统的模型降阶是当前气动弹性力学与控制领域的研究热点之一.然而国内外现有的非线性模型降阶方法仍存在辨识算法复杂、精度有待提高等问题.本研究提出了一种基于非线性状态空间辨识的跨音速气动弹性模型降阶方法. 首先,该方法基于非定常空气动力的单位脉冲响应数据,采用特征系统实现算法对非线性状态空间模型的线性动力学部分进行系统辨识. 其次,引入状态和控制输入的非线性函数, 采用优化算法对非线性函数的系数矩阵进行优化,进而得到考虑非线性效应的空气动力降阶模型.为了验证该降阶模型在预测跨音速气动弹性力学行为的精确性,本文以三维机翼为研究对象,分别从基于非线性降阶模型的气动力辨识、跨声速颤振边界计算和极限环振荡预测三方面进行了算例验证,并与现有的模型降阶方法进行了对比, 进一步说明本文所提出方法的有效性.研究结果表明, 该降阶模型对上述三类问题的计算精度与直接流-固耦合方法相吻合,可用于高效预测飞行器跨声速气动弹性力学行为.
- 气动弹性力学 /
- 颤振 /
- 模型降阶 /
- 系统辨识
Abstract: Reduced-order modeling for high dimensional nonlinear aeroelastic systems is one of the hot issues in the field of aeroelasticity and control. Some linear/nonlinear reduced-order modeling methodologies, such as autoregressive exogenous, auto regressive-moving-average model, Volterra series, artificial neural networks, Wiener model, and Kriging technique, were proposed for reconstructing low-dimensional aerodynamic models. However, the previous nonlinear reduced-order models, such as the nonlinear Wiener model and neural network model, still have some problems need to be addressed. For example, the identification algorithm is too complexity and the accuracy in reconstructing the dynamic behaviors needs to be improved further. In this paper, a nonlinear state-space identification-based reduced-order modeling methodology for transonic aeroelastic systems is proposed. Firstly, the unit impulse response of the transonic aerodynamic system was computed via computational fluid dynamic method. By using the snapshots of the unit impulse response, the linear dynamics part of the nonlinear state-space model is identified by using the eigensystem realization algorithm. Then, the nonlinear functions of the state variables and control input are introduced and the coefficient matrices of these nonlinear functions are optimized via the optimization algorithm. As a result, a nonlinear reduced-order aerodynamic model can be obtained. To verify the accuracy of the reduced-order modeling in predicting the transonic aeroelastic behaviors, a three-dimensional wing is selected as the testbed and the aerodynamic forces, transonic flutter computation, and limit-cycle oscillation prediction are implemented as the numerical examples. Moreover, to demonstrate the accuracy of the present reduced-order modeling method in predicting unsteady aerodynamic forces, the numerical results are also compared with other reduced-order modeling method. The numerical results show that the above three dynamic behaviors predicted via the present reduced-order model have a good agreement with the direct fluid-structure interaction method. The comparison proves that the present reduced-order aerodynamic model can be used to predict the transonic aeroelastic behaviors of aircraft with high efficiency.
- aeroelasticity /
- flutter /
- reduced-order model /
- system identification

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参考文献(1)

[1]	杨超 . 飞行器气动弹性原理. 北京: 北京航空航天大学出版社, 2016: 1-10
[1]	( Yang Chao. Principles of Aeroelasticity for Aircraft. Beijing: Beihang University Press, 2016: 1-10(in Chinese))
[2]	叶柳青, 叶正寅 . 激波主导流动下壁板的热气动弹性稳定性理论分析. 力学学报, 2018,50(2):222-226
[2]	( Ye Liuqing, Ye Zhengyin . Aeroelastic stability analysis of heated flexible panel in shock-dominated flows. Chinese Journal of Theoretical and Applied Mechanics, 2018,50(2):222-226 (in Chinese))
[3]	刘楚源, 刘泽森, 宋汉文 . 基于主动控制策略的机翼颤振特性模拟. 力学学报, 2019,51(2):334-335
[3]	( Liu Chuyuan, Liu Zesen, Song Hanwen . The simulation of airfoil flutter characteristic based on active control strategy. Chinese Journal of Theoretical and Applied Mechanics, 2018,51(2):334-335 (in Chinese))
[4]	Hall KC, Thomas JP, Dowell EH . Proper orthogonal decomposition technique for transonic unsteady aerodynamic flows. AIAA Journal, 2000,38(10):1853-1862
[5]	Falkiewicz NJ, Cesnik CES, Crowell AR , et al. Reduced-order aerothermoelastic framework for hypersonic vehicle control simulation. AIAA Journal, 2011,49(8):1625-1646
[6]	Xie D, Xu M, Dowell EH . Proper orthogonal decomposition reduced-order model for nonlinear aeroelastic oscillations. AIAA Journal, 2014,52(2):229-241
[7]	Zhou Q, Li DF, Ronch AD , et al. Computational fluid dynamic-based transonic flutter suppression with control delay. Journal of Fluids and Structures, 2016,66:183-206
[8]	Silva WA, Bartels RE . Development of reduced-order models for aeroelastic analysis and flutter prediction using the CFL3D v6.0 code. Journal of Fluids and Structures, 2004,19(6):729-745
[9]	Hall KC, Thomas JP, Clark WS . Computation of unsteady nonlinear flows in cascades using a harmonic balance technique. AIAA Journal, 2002,40(5):879-886
[10]	Yao W, Marques S . Prediction of transonic limit-cycle oscillations using an aeroelastic harmonic balance method. AIAA Journal, 2015,53(7):2040-2051
[11]	Winter M, Breitsamter C . Neurofuzzy-model-based unsteady aerodynamic computations across varying freestream conditions. AIAA Journal, 2016,54(9):2705-2720
[12]	Balajewicz M, Nitzsche F, Feszty D . Application of multi-input volterra theory to nonlinear multi-degree-of-freedom aerodynamic systems. AIAA Journal, 2010,48(1):56-62
[13]	Silva WA . Identification of nonlinear aeroelastic systems based on the volterra theory: Progress and opportunities. Nonlinear Dynamics, 2005,39(1-2):25-62
[14]	Huang R, Hu HY, Zhao YH . Nonlinear reduced-order modeling for multiple-input/multiple-output aerodynamic systems. AIAA Journal, 2014,52(6):1219-1231
[15]	Huang R, Li HK, Hu HY , et al. Open/closed-loop aeroservoelastic predictions via nonlinear, reduced-order aerodynamic models. AIAA Journal, 2015,53(7):1812-1824
[16]	Glaz B, Friedmann PP, Liu L , et al. Reduced-order nonlinear unsteady aerodynamic modeling using a surrogate-based recurrence framework. AIAA Journal, 2010,48(10):2418-2429
[17]	Liu H, Hu HY, Zhao YH , et al. Efficient reduced order modeling of unsteady aerodynamics robust to flight parameter variations. Journal of Fluids and Structures, 2014,49:728-741
[18]	Kim T . Parametric model reduction for aeroelastic systems. Invariant aeroelastic modes. Journal of Fluids and Structures, 2016,65:196-216
[19]	Kou JQ, Zhang WW . A hybrid reduced-order framework for complex aeroelastic simulations. Aerospace Science and Technology, 2019,84:880-881
[20]	Li K, Kou JQ, Zhang WW . Deep neural network for unsteady aerodynamic and aeroelasitc modeling across multiple Mach numbers. Nonlinear Dynamics, 2019,96:2157-2159
[21]	Silva WA . Application of nonlinear systems theory to transonic unsteady aerodynamic responses. Journal of Aircraft, 1993,30(5):660-668
[22]	Balajewicz M, Dowell EH . Reduced-order modeling of flutter and limit-cycle oscillations using the sparse volterra series. Journal of Aircraft, 2012,49(6):1803-1812
[23]	郑保敬, 梁钰, 高效伟等. 功能梯度材料动力学问题的POD模型降阶分析. 力学学报, 2018,50(4):788-794
[23]	( Zheng Baojing, Liang Yu, Gao Xiaowei , et al. Analysis for dynamic response of functionally graded materials using POD based reduced order model. Chinese Journal of Theoretical and Applied Mechanics, 2018,50(4):788-794 (in Chinese))
[24]	Huang R, Liu HJ, Yang ZJ , et al. Nonlinear reduced-order models for transonic aeroelastic and aeroservoelastic problems. AIAA Journal, 2018,56(9):3719
[25]	Moré JJ . The Levenberg-marquardt algorithm: Implementation and theory. Numerical Analysis, 1978: 105-116
[26]	Mannarino A, Dowell EH . Reduced-order models for computational-fluid-dynamics-based nonlinear aeroelastic problems. AIAA Journal, 2015,53(9):2671-2685
[27]	胡海岩, 赵永辉, 黄锐 . 飞机结构气动弹性分析与控制研究. 力学学报, 2016,48(1):9-14
[27]	( Hu Haiyan, Zhao Yonghui, Huang Rui . Studies on aeroelastic analysis and control of aircraft structures. Chinese Journal of Theoretical and Applied Mechanics, 2016,48(1):9-14 (in Chinese))
[28]	Waszak MR . Modeling the benchmark active control technology wind-tunnel model for application to flutter suppression// 21st Atmospheric Flight Mechanics Conference, 1996: 1996-3437
[29]	黄锐 . 亚/跨音速飞机结构气动弹性控制及其实验研究. [博士论文]. 南京: 南京航空航天大学, 2014: 108-111
[29]	( Huang Rui . Aeroelastic control of aircraft structure in subsonic/transonic flows and its testification. [PhD Thesis]. Nanjing: Nanjing University of Aeronautics and Astronautics, 2014: 108-111 (in Chinese))
[30]	Walter AS . Simultaneous excitation of multiple-input/multiple-output CFD-based unsteady aerodynamic systems. Journal of Aircraft, 2008,45(4):1267-1269
[31]	Brent AM, Jack JM . Efficient fluid thermal structural time marching with computational fluid dynamics. AIAA Journal, 2018,56(9):3610-3611
[32]	Tomer R, Peretz PF . Multifidelity coKriging for high-dimensional output functions with application to hypersonic airloads computation. AIAA Journal, 2018,56(8):3060-3061
[33]	Ryan JK, Carlos ESC . Nonlinear thermal reduced-order modeling for hypersonic vehicles. AIAA Journal, 2017,55(7):2358-2359