流体力学与飞行力学

基于气动力降阶的弹性飞机阵风响应仿真分析及验证

  • 师妍 ,
  • 万志强 ,
  • 吴志刚 ,
  • 杨超
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  • 北京航空航天大学 航空科学与工程学院, 北京 100083

收稿日期: 2021-03-12

  修回日期: 2021-05-20

  网络出版日期: 2021-05-20

Gust response analysis and verification of elastic aircraft based on nonlinear aerodynamic reduced-order model

  • SHI Yan ,
  • WAN Zhiqiang ,
  • WU Zhigang ,
  • YANG Chao
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  • School of Aeronautic Science and Engineering, Beihang University, Beijing 100083, China

Received date: 2021-03-12

  Revised date: 2021-05-20

  Online published: 2021-05-20

摘要

低速飞机在阵风作用下容易产生非线性气动力,从而引发非线性气动弹性效应,对飞行安全造成威胁。针对此类问题的分析,经典面元法无法满足计算精度要求,计算流体力学(CFD)/计算结构动力学(CSD)全阶耦合分析效率低下,因此需建立满足工程应用的高精度、高效率的飞行动力学仿真分析模型。针对以上问题提出了一种适用于工程的非线性气动力降阶模型(ROM)用以实现弹性飞机飞行动力学仿真,特别是低速飞机在遭遇大幅值阵风情况下的阵风响应仿真。以风洞试验飞翼飞机模型为对象,利用CFD方法获得了该模型的气动力数据,利用自回归移动平均(ARMA)方法和径向基函数(RBF)神经网络方法分别建立了该模型的线性气动力ROM和非线性修正气动力ROM。结合模型的刚弹耦合飞行动力学方程对模型遭遇阵风情况下的响应进行仿真分析,并将仿真结果和风洞试验结果及CFD/CSD计算结果进行对比。结果表明建立的基于非线性气动力ROM的弹性飞机仿真模型在气动力预测、稳定性分析及阵风响应分析方面的表现都优于基于线性气动力ROM的仿真模型,和试验结果及CFD/CSD分析结果一致性较好,且所建模型在相同工况下的仿真时间远低于CFD/CSD分析方法,可应用于工程实践。

本文引用格式

师妍 , 万志强 , 吴志刚 , 杨超 . 基于气动力降阶的弹性飞机阵风响应仿真分析及验证[J]. 航空学报, 2022 , 43(1) : 125474 -125474 . DOI: 10.7527/S1000-6893.2021.25474

Abstract

For low-speed aircraft, gust is more likely to lead to the generation of nonlinear aerodynamic force and nonlinear aeroelastic response, consequently causing flight safety problems. To study these nonlinear problems, the classical panel method cannot meet the accuracy requirements, and the Computational Fluid Dynamics (CFD)/Computational Structure Dynamics (CSD) full order coupling analysis is inefficient. Therefore, it is necessary to establish a flight dynamics analysis model with high precision and high efficiency to satisfy the engineering requirements. A nonlinear aerodynamic Reduced-Order Model (ROM) is proposed in this paper to predict the nonlinear aerodynamic force of low-speed aircraft under high amplitude gusts. Taking a flying wing aircraft model in a wind tunnel test as an example, we obtain the aerodynamic data of the model with the CFD method. A linear aerodynamic ROM and a nonlinear aerodynamic correction ROM of the flying wing aircraft model are established by the Autoregressive Moving Average (ARMA) method and Radial Basis Function (RBF) neural network method, respectively. An elastic aircraft simulation model is then established and the response of the model under gusts is simulated and analyzed by combining the rigid-elastic coupling flight dynamics equation and the aerodynamic ROM. Comparison of the simulation results with the wind tunnel test results and the CFD/CSD calculation results shows that the performance of the elastic aircraft simulation model based on nonlinear aerodynamic ROM is better than that of the model based on linear aerodynamic ROM in aerodynamic prediction, stability analysis and gust response analysis, and that the analysis results are in good agreement with the test results and CFD/CSD analysis results. The time cost of the proposed simulation model based on ROM is much lower than that of the CFD/CSD analysis method under the same working conditions, indicating the applicability of the proposed model to the engineering practice.

参考文献

[1] 杨俊斌, 吴志刚, 戴玉婷, 等. 飞翼布局飞机阵风减缓主动控制风洞试验[J]. 北京航空航天大学学报, 2017, 43(1): 184-192. YANG J B, WU Z G, DAI Y T, et al. Wind tunnel test of gust alleviation active control for flying wing configuration aircraft[J]. Journal of Beijing University of Aeronautics and Astronautics, 2017, 43(1): 184-192(in Chinese).
[2] 金长江, 肖夜伦. 大气扰动中的飞行原理[M]. 北京: 国防工业出版社, 1992: 1-7. JIN C J, XIAO Y L. Flight principle in atmospheric disturbance[M]. Beijing: National Defense Industry Press, 1992: 1-7(in Chinese).
[3] WRIGHT J R, COOPER J E. Introduction to aircraft aeroelasticity and loads[M]. West Sussex: John Wiley and Sons Ltd, 2015: 293-326.
[4] MURROW H, PRATT K, HOUBOLT J. NACA/NASA research related to evolution of US gust design criteria[C]//30th Structures, Structural Dynamics and Materials Conference. Reston: AIAA, 1989.
[5] FULLER J R. Evolution of airplane gust loads design requirements[J]. Journal of Aircraft, 1995, 32(2): 235-246.
[6] 岑飞, 李清, 刘志涛, 等. 民机极限飞行状态的动态气动力试验与建模[J]. 航空学报, 2020, 41(8): 123664. CEN F, LI Q, LIU Z T, et al. Unsteady aerodynamics test and modeling of civil aircraft under extreme flight conditions[J]. Acta Aeronautica et Astronautica Sinica, 2020, 41(8): 123664(in Chinese).
[7] SU W H, CESNIK C E S. Dynamic response of highly flexible flying wings[J]. AIAA Journal, 2011, 49(2): 324-339.
[8] RAGHAVAN B, PATIL M J. Flight control for flexible, high-aspect-ratio flying wings[J]. Journal of Guidance, Control, and Dynamics, 2010, 33(1): 64-74.
[9] 马东立, 张良, 杨穆清, 等. 超长航时太阳能无人机关键技术综述[J]. 航空学报, 2020, 41(3): 623418. MA D L, ZHANG L, YANG M Q, et al. Review of key technologies of ultra-long-endurance solar powered unmanned aerial vehicle[J]. Acta Aeronautica et Astronautica Sinica, 2020, 41(3): 623418(in Chinese).
[10] HOBLIT F M. Gust loads on aircraft: Concepts and applications[M]. Washington, D.C.: AIAA, 1988: 115-186.
[11] PATIL M J, HODGES D H, CESNIK C E S. Nonlinear aeroelastic analysis of complete aircraft in subsonic flow[J]. Journal of Aircraft, 2000, 37(5): 753-760.
[12] PATIL M J, HODGES D H. On the importance of aerodynamic and structural geometrical nonlinearities in aeroelastic behavior of high-aspect-ratio wings[J]. Journal of Fluids and Structures, 2004, 19(7): 905-915.
[13] PETERS D A, KARUNAMOORTHY S, CAO W M. Finite state induced flow models. I—Two-dimensional thin airfoil[J]. Journal of Aircraft, 1995, 32(2): 313-322.
[14] KARPEL M. Time-domain aeroservoelastic modeling using weighted unsteady aerodynamic forces[J]. Journal of Guidance, Control, and Dynamics, 1990, 13(1): 30-37.
[15] ZOLE A, KARPEL M. Continuous gust response and sensitivity derivatives using state-space models[J]. Journal of Aircraft, 1994, 31(5): 1212-1214.
[16] KARPEL M, MOULIN B, CHEN P C. Dynamic response of aeroservoelastic systems to gust excitation[J]. Journal of Aircraft, 2005, 42(5): 1264-1272.
[17] KIER T. Comparison of unsteady aerodynamic modelling methodologies with respect to flight loads analysis[C]//AIAA Atmospheric Flight Mechanics Conference and Exhibit. Reston: AIAA, 2005.
[18] 陈浩, 袁先旭, 毕林, 等. 基于RANS/LES混合方法的分离流动模拟[J]. 航空学报, 2020, 41(8): 123642. CHEN H, YUAN X X, BI L, et al. Simulation of separated flow based on RANS/LES hybrid method[J]. Acta Aeronautica et Astronautica Sinica, 2020, 41(8): 123642(in Chinese).
[19] PALACIOS R, CESNIK C. Static nonlinear aeroelasticity of flexible slender wings in compressible flow[C]//46th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference. Reston: AIAA, 2005.
[20] HALLISSY B, CESNIK C. High-fidelity aeroelastic analysis of very flexible aircraft[C]//52nd AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference. Reston: AIAA, 2011.
[21] 闵耀兵, 马燕凯, 李松. CFD中统计误差的数值精度分析[J]. 航空学报, 2020, 41(4): 123554. MIN Y B, MA Y K, LI S. Accuracy analysis of numerical error with statistical forms in CFD[J]. Acta Aeronautica et Astronautica Sinica, 2020, 41(4): 123554(in Chinese).
[22] 王年华, 常兴华, 赵钟, 等. 非结构CFD软件MPI+OpenMP混合并行及超大规模非定常并行计算的应用[J]. 航空学报, 2020, 41(10): 123859. WANG N H, CHANG X H, ZHAO Z, et al. Implementation of hybrid MPI+OpenMP parallelization on unstructured CFD solver and its applications in massive unsteady simulations[J]. Acta Aeronautica et Astronautica Sinica, 2020, 41(10): 123859(in Chinese).
[23] COWAN T J. Efficient aeroelastic CFD predictions using system identification[D]. Oklahoma: Oklahoma State University, 1998: 11-15.
[24] TORII H, MATSUZAKI Y. Flutter margin evaluation for discrete-time systems[J]. Journal of Aircraft, 2001, 38(1): 42-47.
[25] 杨国伟, 王济康. CFD结合降阶模型预测阵风响应[J]. 力学学报, 2008, 40(2): 145-153. YANG G W, WANG J K. Gust response prediction with cfd-based reduced order modeling[J]. Chinese Journal of Theoretical and Applied Mechanics, 2008, 40(2): 145-153(in Chinese).
[26] 张伟伟, 叶正寅. 基于气动力降阶模型的跨音速气动弹性稳定性分析[J]. 计算力学学报, 2007, 24(6): 768-772. ZHANG W W, YE Z Y. Transonic aeroelastic analysis basing on reduced order aerodynamic models[J]. Chinese Journal of Computational Mechanics, 2007, 24(6): 768-772(in Chinese).
[27] 张伟伟, 叶正寅. 大后掠翼前缘涡对其颤振特性的影响[J]. 航空学报, 2009, 30(12): 2263-2268. ZHANG W W, YE Z Y. Effects of leading-edge vortex on flutter characteristics of high sweep angle wing[J]. Acta Aeronautica et Astronautica Sinica, 2009, 30(12): 2263-2268(in Chinese).
[28] ZHANG W W, YE Z Y. Effect of control surface on airfoil flutter in transonic flow[J]. Acta Astronautica, 2010, 66(7-8): 999-1007.
[29] ZHANG W W, LI X T, YE Z Y, et al. Mechanism of frequency lock-in in vortex-induced vibrations at low Reynolds numbers[J]. Journal of Fluid Mechanics, 2015, 783: 72-102.
[30] SILVA W A. Discrete-time linear and nonlinear aerodynamic impulse responses for efficient CFD analyses[M]. Williamsburg: The College of William and Mary, 1997: 33-48.
[31] MARZOCCA P, SILVA W A, LIBRESCU L. Open/closed-loop nonlinear aeroelasticity for airfoils via Volterra series approach[C]//43rd AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference. Reston: AIAA, 2002.
[32] LIND R, PRAZENICA R J, BRENNER M J, et al. Identifying parameter-dependent Volterra kernels to predict aeroelastic instabilities[J]. AIAA Journal, 2005, 43(12): 2496-2502.
[33] PRAZENICA R J, REISENTHEL P H, KURDILA A J, et al. Volterra kernel extrapolation for modeling nonlinear aeroelastic systems at novel flight conditions[J]. Journal of Aircraft, 2007, 44(1): 149-162.
[34] OMRAN A, NEWMAN B. Full envelope nonlinear parameter-varying model approach for atmospheric flight dynamics[J]. Journal of Guidance, Control, and Dynamics, 2012, 35(1): 270-283.
[35] 王云海, 韩景龙, 张兵, 等. 空气动力二阶核函数辨识方法[J]. 航空学报, 2014, 35(11): 2949-2957. WANG Y H, HAN J L, ZHANG B, et al. Identification method of second-order kernels in aerodynamics[J]. Acta Aeronautica et Astronautica Sinica, 2014, 35(11): 2949-2957(in Chinese).
[36] 陈森林, 高正红, 饶丹. 基于多小波的Volterra级数非定常气动力建模方法[J]. 航空学报, 2018, 39(1): 121379. CHEN S L, GAO Z H, RAO D. Unsteady aerodynamics modeling method using Volterra series based on multiwavelet[J]. Acta Aeronautica et Astronautica Sinica, 2018, 39(1): 121379(in Chinese).
[37] 奚之飞, 徐安, 寇英信, 等. 基于改进粒子群算法辨识Volterra级数的目标机动轨迹预测[J]. 航空学报, 2020, 41(12): 324183. XI Z F, XU A, KOU Y X, et al. Target maneuver trajectory prediction based on Volterra series identified by improved particle swarm algorithm[J]. Acta Aeronautica et Astronautica Sinica, 2020, 41(12): 324183(in Chinese).
[38] 王超, 王贵东, 白鹏. 飞行仿真气动力数据机器学习建模方法[J]. 空气动力学学报, 2019, 37(3): 488-497. WANG C, WANG G D, BAI P. Machine learning method for aerodynamic modeling based on flight simulation data[J]. Acta Aerodynamica Sinica, 2019, 37(3): 488-497(in Chinese).
[39] ZHANG W W, WANG B B, YE Z Y, et al. Efficient method for limit cycle flutter analysis based on nonlinear aerodynamic reduced-order models[J]. AIAA Journal, 2012, 50(5): 1019-1028.
[40] 杨朝旭, 郭毅, 雷廷万, 等. 先进战斗机过失速机动大气数据融合估计方法[J]. 航空学报, 2020, 41(6): 523456. YANG Z X, GUO Y, LEI T W, et al. Air data fusion and estimation method for advanced aircrafts in post-stall maneuver[J]. Acta Aeronautica et Astronautica Sinica, 2020, 41(6): 523456(in Chinese).
[41] KOU J Q, ZHANG W W. Layered reduced-order models for nonlinear aerodynamics and aeroelasticity[J]. Journal of Fluids and Structures, 2017, 68: 174-193.
[42] KOU J Q, ZHANG W W. A hybrid reduced-order framework for complex aeroelastic simulations[J]. Aerospace Science and Technology, 2019, 84: 880-894.
[43] 何磊, 钱炜祺, 汪清, 等. 机器学习方法在气动特性建模中的应用[J]. 空气动力学学报, 2019, 37(3): 470-479. HE L, QIAN W Q, WANG Q, et al. Applications of machine learning for aerodynamic characteristics modeling[J]. Acta Aerodynamica Sinica, 2019, 37(3): 470-479(in Chinese).
[44] 陈森林, 高正红, 朱新奇, 等. 非稳定动态过程非定常气动力建模[J]. 航空学报, 2020, 41(8): 123675. CHEN S L, GAO Z H, ZHU X Q, et al. Unsteady aerodynamic modeling of unstable dynamic process[J]. Acta Aeronautica et Astronautica Sinica, 2020, 41(8): 123675(in Chinese).
[45] KOU J Q, ZHANG W W. Reduced-order modeling for nonlinear aeroelasticity with varying Mach numbers[J]. Journal of Aerospace Engineering, 2018, 31(6): 04018105.
[46] KOU J Q, ZHANG W W. Multi-kernel neural networks for nonlinear unsteady aerodynamic reduced-order modeling[J]. Aerospace Science and Technology, 2017, 67: 309-326.
[47] WINTER M, BREITSAMTER C. Neurofuzzy-model-based unsteady aerodynamic computations across varying freestream conditions[J]. AIAA Journal, 2016, 54(9): 2705-2720.
[48] LI K, KOU J Q, ZHANG W W. Deep neural network for unsteady aerodynamic and aeroelastic modeling across multiple Mach numbers[J]. Nonlinear Dynamics, 2019, 96(3): 2157-2177.
[49] LI W J, LAIMA S J, JIN X W, et al. A novel long short-term memory neural-network-based self-excited force model of limit cycle oscillations of nonlinear flutter for various aerodynamic configurations[J]. Nonlinear Dynamics, 2020, 100(3): 2071-2087.
[50] 黄超. 柔性飞翼飞机颤振主动抑制系统建模、设计与验证[D]. 北京: 北京航空航天大学, 2018: 52-55, 76-85, 127. HUANG C. Modeling, design and verification of active flutter suppression system for flexible flying wing aircraft[D]. Beijing: Beihang University, 2018: 52-55, 76-85, 127(in Chinese)
[51] SHENG W N, GALBRAITH R, COTON F. A modified dynamic stall model for low Mach numbers[J]. Journal of Solar Energy Engineering, 2007; 130(3): 653.
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