RESEARCH ON THE FLUTTER CHARACTERISTCIS OF AGARD445.6 SOLID WING CONSIDERING THE INITIAL ANGLE OF ATTACK BASED ON REDUCED ORDER MODEL
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摘要: 以AGARD445.6硬机翼为研究对象,发展了基于计算流体力学与模态叠加的并行流固耦合方法,计算该机翼在不同初始迎角、不同来流速度的气动弹性时域响应,结果表明:初始迎角小于7°时,该机翼颤振速度随着初始迎角增加而降低;初始迎角7°~10°,颤振速度随着迎角增大而增加。在10°迎角条件建立了基于径向基神经网络的非定常气动降阶模型,准确预测不同速度、减缩频率的非定常气动力,并使用时域龙格库塔法和频域VG法预测10°迎角的颤振特性;建立考虑初始迎角输入的非定常气动降阶模型,预测机翼不同初始迎角的颤振特性。基于降阶模型的初始迎角对颤振边界影响的机理分析表明:小迎角时,随着迎角的增加广义力系数幅值比增加,导致颤振速度的下降;迎角大于7°后展向涡改变了机翼表面压强分布,导致一扭广义力系数幅值比降低,从而增加该机翼颤振速度。Abstract: The AGARD445.6 solid wing is considered as the object of this study. The fluid-structure interaction method based on CFD and modal superposition is developed, and the aeroelastic responses at various angles of attack and velocities are calculated. Results indicate that with the increase of initial angle of attack, the flutter velocity decreases when the angle of attack is under 7°, and the flutter velocity increases when the angle of attack is between 7° and 10°. Then, at 10° angle of attack, the unsteady aerodynamic reduced order model based on radial basis function neural network is developed to predict the unsteady aerodynamic forces at different velocities and reduced frequencies. The flutter characteristics at 10° angle of attack are predicted using the time domain Runge-Kutta method and the frequency domain VG method. Then the unsteady aerodynamic reduced order model considering the input of initial angle is established to predict the flutter characteristics at different angles of attack. The analysis on the effect of initial angle on the flutter boundary shows that, at small angle, the increase of the generalized force coefficient amplitude ratio with the increase of the angle leads to the decrease of flutter velocity. When the initial angle is greater than 7°, the flow separation region expands, which changes the pressure distribution on the wing surface, leading to the decrease of the generalized force coefficient amplitude ratio of torsional mode, and hence results in the increase of flutter velocity.
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表 1 迎角10°法向力系数Cn对比
Table 1. The comparison result of normal force coefficient Cn at angle of 10°
网格量 最大y+ 法向力系数Cn 误差/(%) 1.38×106 25 0.2320 0.3 2.34×106 6 0.2313 − 表 2 AGARD445.6软机翼前两阶模态频率
Table 2. Mode frequency of AGARD445.6 weakened wing
模态 一阶模态 二阶模态 频率/Hz 9.60 38.17 表 3 试验与流固耦合计算结果对比
Table 3. Flutter results of CFD and experiment
试验结果颤振速度/(m/s) 流固耦合颤振速度/(m/s) 误差/(%) 172.46 171.4 0.61 表 4 AGARD445.6硬机翼前两阶模态频率
Table 4. Mode frequency of AGARD445.6 solid wing
模态 一阶模态 二阶模态 频率/Hz 14.12 50.91 表 5 ROM1和ROM2辨识误差
Table 5. Identification error of ROM1 and ROM2
广义力系数 f11 f21 f12 f22 ROM1 0.0160 0.0180 0.0352 0.0411 ROM2 0.0332 0.0335 0.0435 0.0547 表 6 基于降阶模型的10°迎角颤振预测与流固耦合对比
Table 6. Flutter prediction by ROM and FSI, α = 10°
颤振特性 颤振边界 $V_{\rm f}^*$ 频率比 $\omega {}_{\rm f}/{\omega _\alpha }$ ROM1, RK法 0.487 0.74 ROM1, VG法 0.489 0.72 ROM2, RK法 0.472 0.77 ROM2, VG法 0.477 0.74 流固耦合 0.459-0.472 0.77~0.79 表 7 ROM3辨识误差
Table 7. Identification error of ROM3
广义力系数 f11 f21 f12 f22 ROM3A 0.0228 0.0210 0.0181 0.0095 ROM3B 0.0283 0.0560 0.0222 0.0472 -
[1] ASHLEY H. On the role of shocks in the'sub-transonic'flutter phenomenon [C]// 20th Structures, Structural Dynamics, and Materials Conference. America, 1979: 765. [2] EDWARDS J W, BENNETT R M, WHITLOW W, et al. Time-marching transonic flutter solutions including angle-of-attack effects [J]. Journal of Aircraft, 1983, 20(11): 899 − 906. doi: 10.2514/3.48190 [3] DOGGETT JR R V, RICKETTS R A. Effects of angle of attack and ventical fin on transonic flutter characteristics of an arrow-wing configuration [J]. National Aeronautics and Space Administration, 1980, 26: 1 − 29. [4] YATES JR E C, WYNNE E C, FARMER M G. Measured and calculated effects of angle of attack on the transonic flutter of a supercritical wing [C]// Proceedings of the AIAA Structures, Structural Dynamics, and Materials Conference. New Orleans, La., AIAA Structures, 1982: 122 − 144. [5] YE Z Y, ZHAO L C. Nonlinear flutter analysis of wings at high angle of attack [J]. Journal of Aircraft, 1994, 31(4): 973 − 974. doi: 10.2514/3.46587 [6] 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. doi: 10.3321/j.issn:1000-6893.2009.12.004ZHANG Weiwei, YE Zhengyin. 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) doi: 10.3321/j.issn:1000-6893.2009.12.004 [7] 全景阁, 叶正寅, 张伟伟. 削尖三角翼涡破裂前后的气动弹性特性对比研究[J]. 航空学报, 2011, 32(3): 379 − 389.QUAN Jingge, YE Zhengyin, ZHANG Weiwei. Comparative study on aeroelastic characteristics of a cropped delta wing before and after vortex breakdown [J]. Acta Aeronautica et Astronautica Sinica, 2011, 32(3): 379 − 389. (in Chinese) [8] 全景阁. 分离流中若干气动弹性问题研究[D]. 西安: 西北工业大学, 2019.QUAN Jingge. Study on aeroelastic analysis in separated flows [D]. Xi’an: Northwestern Polytechnical University, 2019. (in Chinese) [9] 叶正寅, 谢飞. 中等及大迎角下的翼型气动弹性性质研究[J]. 风机技术, 2009(2): 9 − 13. doi: 10.3969/j.issn.1006-8155.2009.02.004YE Zhengyin, XIE Fei. Research on the aeroelastic characteristics of an airfoil in moderate and high incidences [J]. Chinese Journal of Turbomachinery, 2009(2): 9 − 13. (in Chinese) doi: 10.3969/j.issn.1006-8155.2009.02.004 [10] 刘畅畅, 刘子强, 季辰. 不同迎角的翼型气弹特性风洞试验研究[J]. 空气动力学学报, 2012, 30(2): 271 − 276. doi: 10.3969/j.issn.0258-1825.2012.02.025LIU Changchang, LIU Ziqiang, JI Chen. Aeroelastic characteristics of airfoils at different angels of attack in wind tunnel testing [J]. Acta Aerodynamica Sinica, 2012, 30(2): 271 − 276. (in Chinese) doi: 10.3969/j.issn.0258-1825.2012.02.025 [11] LI W, GAO X, LIU H. Efficient prediction of transonic flutter boundaries for varying Mach number and angle of attack via LSTM network [J]. Aerospace Science and Technology, 2021, 110: 106451. doi: 10.1016/j.ast.2020.106451 [12] TALLEY C S, WRAY T J, WANG Y, et al. Flutter analysis at variable mach and angle of attack utilizing reduced-order-models-ifasd 2019-101 [C]// International Forum on Aeroelasticity and Structural Dynamics (IFASD). America, Florida, 2019. [13] TANG D, DOWELL E H. Experimental and theoretical study on aeroelastic response of high-aspect-ratio wings [J]. AIAA Journal, 2001, 39(8): 1430 − 1441. doi: 10.2514/2.1484 [14] TANG D, DOWELL E H. Limit-cycle hysteresis response for a high-aspect-ratio wing model [J]. Journal of Aircraft, 2015, 39(5): 885 − 888. [15] 戴玉婷, 严慧, 王林鹏. 基于非线性气动力的失速颤振计算与试验研究[J]. 工程力学, 2020, 37(8): 230 − 236. doi: 10.6052/j.issn.1000-4750.2019.03.0141DAI Yuting, YAN Hui, WANG Linpeng. Calculation and experimental study of stall flutter based on nonlinear aerodynamics [J]. Engineering Mechanics, 2020, 37(8): 230 − 236. (in Chinese) doi: 10.6052/j.issn.1000-4750.2019.03.0141 [16] 高超, 贾娅娅, 刘庆宽. 相对厚度对翼型气动特性的影响研究[J]. 工程力学, 2020, 37(增刊): 380 − 386. doi: 10.6052/j.issn.1000-4750.2019.04.S062GAO Chao, JIA Yaya, LIU Qingkuan. Effect of relative thickness on aerodynamic performance of airfoil [J]. Engineering Mechanics, 2020, 37(Suppl): 380 − 386. (in Chinese) doi: 10.6052/j.issn.1000-4750.2019.04.S062 [17] 张照煌, 李魏魏. 座头鲸胸鳍前缘仿生叶片空气动力学特性研究[J]. 工程力学, 2020, 37(增刊): 376 − 379, 386. doi: 10.6052/j.issn.1000-4750.2019.04.S061ZHANG Zhaohuang, LI Weiwei. Aerodynamic characteristics of bionic wing of leading-edge of humpback whale flipper [J]. Engineering Mechanics, 2020, 37(Suppl): 376 − 379, 386. (in Chinese) doi: 10.6052/j.issn.1000-4750.2019.04.S061 [18] KUBAT M. Neural networks: A comprehensive foundation by Simon Haykin, Macmillan, 1994, ISBN 0-02-352781-7 [J]. The Knowledge Engineering Review, 1999, 13(4): 409 − 412. [19] MARQUES F D, ANDERSON J. Identification and prediction of unsteady transonic aerodynamic loads by multi-layer func-tions [J]. Journal of Fluids and Structures, 2001, 15(1): 83 − 106. doi: 10.1006/jfls.2000.0321 [20] QIU Z, WANG F. On aeroelastic response of an airfoil under dynamic stall using time delay neural network [J]. Aerospace Systems, 2018, 1(2): 87 − 97. doi: 10.1007/s42401-018-0010-3 [21] WINTER M, BREITSAMTER C. Neurofuzzy-model-based unsteady aerodynamic computations across varying freestream conditions [J]. AIAA Journal, 2016, 54(9): 2705 − 2720. doi: 10.2514/1.J054892 [22] 王博斌, 张伟伟, 叶正寅. 基于神经网络模型的动态非线性气动力辨识方法[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. (in Chinese) [23] KOU J, ZHANG W. Layered reduced-order models for nonlinear aerodynamics and aeroelasticity [J]. Journal of Fluids and Structures, 2017, 68: 174 − 193. doi: 10.1016/j.jfluidstructs.2016.10.011 [24] LI K, KOU J, ZHANG W. Deep neural network for unsteady aerodynamic and aeroelastic modeling across multiple Mach numbers [J]. Nonlinear Dynamics, 2019, 96(3): 2157 − 2177. doi: 10.1007/s11071-019-04915-9 [25] KOU J, ZHANG W. A hybrid reduced-order framework for complex aeroelastic simulations [J]. Aerospace Science and Technology, 2019, 84: 880 − 894. doi: 10.1016/j.ast.2018.11.014 [26] YATES JR E C. AGARD standard aeroelastic configurations for dynamic response I-Wing 445.6 [R]. France: Advisory Group for Aerospace Research and Development Neuilly-Sur-Seine, 1988. [27] BROOMHEAD D S, LOWE D. Multivariable Functional Interpolation and Adaptive Networks [J]. Complex Systems, 1988, 2(3): 321 − 355. [28] THEODORSEN T. General theory of aerodynamic instability and the mechanism of flutter [R]. Washington, D. C.: National Aeronautics and Space Administration, 1979. [29] CHU J, LUCKRING J M. Experimental surface pressure data obtained on 65゜delta wing across reynolds number and mach number ranges, Volume 1. Sharp leading edge: NASA-TM-4645 [R]. Washington, D. C.: NASA, 1996. [30] FLUENT A. ANSYS fluent theory guide [J]. ANSYS Inc., 2011, 15317: 724 − 746. -