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Journal articles on the topic 'Flutter Prediction'

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Author: Grafiati
Published: 6 September 2023

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1

Dimitriadis, G., and J. E. Cooper. "Flutter Prediction from Flight Flutter Test Data." Journal of Aircraft 38, no. 2 (March 2001): 355–67. http://dx.doi.org/10.2514/2.2770.

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2

Sudha, U. P. V., G. S. Deodhare, and K. Venkatraman. "A comparative assessment of flutter prediction techniques." Aeronautical Journal 124, no. 1282 (October 27, 2020): 1945–78. http://dx.doi.org/10.1017/aer.2020.84.

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ABSTRACTTo establish flutter onset boundaries on the flight envelope, it is required to determine the flutter onset dynamic pressure. Proper selection of a flight flutter prediction technique is vital to flutter onset speed prediction. Several methods are available in literature, starting with those based on velocity damping, envelope functions, flutter margin, discrete-time Autoregressive Moving Average (ARMA) modelling, flutterometer and the Houbolt–Rainey algorithm. Each approach has its capabilities and limitations. To choose a robust and efficient flutter prediction technique from among the velocity damping, envelope function, Houbolt–Rainey, flutter margin and auto-regressive techniques, an example problem is chosen for their evaluation. Hence, in this paper, a three-degree-of-freedom model representing the aerodynamics, stiffness and inertia of a typical wing section is used(1). The aerodynamic, stiffness and inertia properties in the example problem are kept the same when each of the above techniques is used to predict the flutter speed of this aeroelastic system. This three-degree-of-freedom model is used to generate data at speeds before initiation of flutter, during flutter and after occurrence of flutter. Using these data, the above-mentioned flutter prediction methods are evaluated and the results are presented.
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3

Gabriela, STROE, and ANDREI Irina-Carmen. "STUDIES ON FLUTTER PREDICTION." INCAS BULLETIN 4, no. 1 (March 9, 2012): 115–23. http://dx.doi.org/10.13111/2066-8201.2012.4.1.12.

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4

CANFIELD, ROBERT A., RAYMOND G. TOTH, and REID MELVILLE. "VIBRATION AND TRANSONIC FLUTTER ANALYSIS FOR F-16 STORES CONFIGURATION CLEARANCE." International Journal of Structural Stability and Dynamics 06, no. 03 (September 2006): 377–95. http://dx.doi.org/10.1142/s0219455406002039.

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This paper supports quick and accurate prediction of the flutter onset speed of an F-16 Block 40/50 configured with external stores in the transonic flight regime. Current flutter prediction methods are reviewed and hypothesized mechanisms for limit cycle oscillation (LCO) are summarized. New efforts to correlate transonic small disturbance (TSD) theory methods with flight tests are outlined. Vibration analysis and structural optimization of an F-16 finite element model were used to match ground vibration testing results. Frequency tuning was found to be critical for accurate flutter speed predictions. Sensitivity to nonlinear aerodynamic effects and store modeling was examined.
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5

Chi, R. M., and A. V. Srinivasan. "Some Recent Advances in the Understanding and Prediction of Turbomachine Subsonic Stall Flutter." Journal of Engineering for Gas Turbines and Power 107, no. 2 (April 1, 1985): 408–17. http://dx.doi.org/10.1115/1.3239741.

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In this paper, some recent advances in the understanding and prediction of subsonic flutter of jet engine fan rotor blades are reviewed. Among the topics discussed are (i) the experimental evidence of mistuning in flutter responses, (ii) new and promising unsteady aerodynamic models for subsonic stall flutter prediction, (iii) an overview of flutter prediction methodologies, and (iv) a new research effort directed toward understanding the mistuning effect on subsonic stall flutter of shrouded fans. A particular shrouded fan of advanced design is examined in the detailed technical discussion.
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6

Sun, Zhi Wei, and Jun Qiang Bai. "Time-Domain Aeroservoelastic Modeling and Active Flutter Suppression by Model Predictive Control." Advanced Materials Research 898 (February 2014): 688–95. http://dx.doi.org/10.4028/www.scientific.net/amr.898.688.

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A time-domain aeroservoelastic model is developed to calculate the flutter speed and an active flutter suppression system is designed by model predictive control. The finite-state, induced-flow theory and equilibrium beam finite element method are chosen to formulate the aeroservoelastic governing equations in state-space form, which is necessary for active flutter suppression design with modern control theory. A sensitivity analysis is performed to find the most appropriate number of induced-flow terms and beam elements. Model predictive control theory is adopted to design an active flutter suppression system due to its ability to deal with the constraints on rate change and amplitude of input. The numerical result shows a satisfactory precision of the flutter speed prediction, the close loop analysis shows that the flutter boundary is considerable expanded.
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7

Dimitriadis, G., and J. E. Cooper. "Comment on "Flutter Prediction from Flight Flutter Test Data"." Journal of Aircraft 43, no. 3 (May 2006): 862–63. http://dx.doi.org/10.2514/1.c9463tc.

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8

Bae, Jae-Sung, Jong-Yun Kim, In Lee, Yuji Matsuzaki, and Daniel J. Inman. "Extension of Flutter Prediction Parameter for Multimode Flutter Systems." Journal of Aircraft 42, no. 1 (January 2005): 285–88. http://dx.doi.org/10.2514/1.6440.

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9

Arifianto, Dhany. "Flutter prediction on combined EPS and carbon sandwich structure for light aircraft wing." Journal of the Acoustical Society of America 150, no. 4 (October 2021): A345. http://dx.doi.org/10.1121/10.0008533.

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Flutter prediction is an important step before conducting a flight test. In this study, we performed flutter prediction of a half-wing structure without control surfaces. The half-wing structure is made to resemble the scaled-down wing of a Boeing 737 NG at a scale of 1:39.34. The airfoil profile used is the wing profile of the Boeing 737 NG obtained from airfoiltools. The structure is constructed using a combination of carbon sandwich andEPS. The advantages of choosing this material are its low-cost and easymanufacture. We used the p-k method in the FEMAP software for flutter prediction. From the prediction results, the calculated flutter speed is ∼14.5 m/s. The flutter mode shape is a combination of lateral bending and twist. Dimensional analysis was also carried out to predict the maximum speed on the scaled model and predicted at 27.88 m/s. Based on calculated flutter speed, the maximum operating speed of a constructed structure should be far less than the flutter speed. Thus, the structure's maximum speed is below the predicted value. Based on carried out flutter prediction, the wing structure, constructed using a combination of carbon sandwich and EPS, can fly safely at a maximum cruise speed of 10 m/s.
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10

Zheng, Hua, Junhao Liu, and Shiqiang Duan. "Novel Nonstationarity Assessment Method for Hypersonic Flutter Flight Tests." Mathematical Problems in Engineering 2018 (October 25, 2018): 1–12. http://dx.doi.org/10.1155/2018/9742591.

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Hypersonic aircraft have been rapidly developed in recent years both theoretically and experimentally. Aerothermoelastic simulation is very challenging due to its inherent complexity, but physical tests are a workable approach. Flutter tests with variable speed are a popular alternative to hypersonic tests which provide nonstationary structural response data. This paper proposes a nonstationarity assessment method based on energy distribution in the time-frequency domain. The proposed method reveals the nonstationarity level corresponding to the appropriate modal identification algorithm or flutter boundary prediction (FBP) method. Several classic flutter criteria are utilized to build a hypersonic aircraft FBP framework. Numerical simulation and experimental applications demonstrate the effectiveness and feasibility of the proposed method, which facilitates accurate flutter predictions for the subcritical turbulence response during hypersonic flutter flight.
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11

Torii, Hiroshi, and Yuji Matsuzaki. "Real-Time Flutter Prediction Based on Non-Stationary Flutter Testing." JOURNAL OF THE JAPAN SOCIETY FOR AERONAUTICAL AND SPACE SCIENCES 50, no. 576 (2002): 30–35. http://dx.doi.org/10.2322/jjsass.50.30.

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12

MATSUZAKI, Yuji, and Hiroshi TORII. "Survey of Flutter Boundary Prediction Method." Journal of the Japan Society for Aeronautical and Space Sciences 44, no. 511 (1996): 473–78. http://dx.doi.org/10.2322/jjsass1969.44.473.

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13

UEDA, Tetsuhiko, Masanobu IIO, and Tadashige IKEDA. "Flutter Prediction Using Continuous Wavelet Transform." TRANSACTIONS OF THE JAPAN SOCIETY FOR AERONAUTICAL AND SPACE SCIENCES 51, no. 174 (2009): 275–81. http://dx.doi.org/10.2322/tjsass.51.275.

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14

TORII, Hiroshi, and Yuji MATSUZAKI. "New Flutter Prediction Method Based on ARMA Model. (Part 1). Proposal of New Flutter Prediction Parameter." Journal of the Japan Society for Aeronautical and Space Sciences 47, no. 551 (1999): 443–48. http://dx.doi.org/10.2322/jjsass.47.443.

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15

Liu, Junhao, Hua Zheng, Shiqiang Duan, and Chengming Pei. "A New Method of Flutter Boundary Prediction for Progressive Variable Speed Based on EM-KS Algorithm." Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University 37, no. 6 (December 2019): 1231–37. http://dx.doi.org/10.1051/jnwpu/20193761231.

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The flutter test with progression variable speed is actively explored in recent years. This paper proposes an improved Kalman smoothing filter (EM-KS) algorithm based on expectation maximization for the non-stationary characteristics of the signal in this type of experiment, which can effectively improve the estimation accuracy of time-varying parameter modeling. Combining with the flutter time domain criterion, a new method for flutter boundary prediction of flutter test with progression variable speed that can be recursively implemented is given. Finally, the reliability and engineering applicability of this method are validated by numerical simulation and measured data. The results show that the flutter boundary prediction method based on EM-KS does not depend on the assumption of stationary stochastic process, and the accuracy can meet the actual engineering needs.
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16

Wang, Yi-Ren, and Yi-Jyun Wang. "Flutter speed prediction by using deep learning." Advances in Mechanical Engineering 13, no. 11 (November 2021): 168781402110622. http://dx.doi.org/10.1177/16878140211062275.

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Deep learning technology has been widely used in various field in recent years. This study intends to use deep learning algorithms to analyze the aeroelastic phenomenon and compare the differences between Deep Neural Network (DNN) and Long Short-term Memory (LSTM) applied on the flutter speed prediction. In this present work, DNN and LSTM are used to address complex aeroelastic systems by superimposing multi-layer Artificial Neural Network. Under such an architecture, the neurons in neural network can extract features from various flight data. Instead of time-consuming high-fidelity computational fluid dynamics (CFD) method, this study uses the K method to build the aeroelastic flutter speed big data for different flight conditions. The flutter speeds for various flight conditions are predicted by the deep learning methods and verified by the K method. The detailed physical meaning of aerodynamics and aeroelasticity of the prediction results are studied. The LSTM model has a cyclic architecture, which enables it to store information and update it with the latest information at the same time. Although the training of the model is more time-consuming than DNN, this method can increase the memory space. The results of this work show that the LSTM model established in this study can provide more accurate flutter speed prediction than the DNN algorithm.
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17

Zheng, Hua, Junhao Liu, and Shiqiang Duan. "Flutter Test Data Processing Based on Improved Hilbert-Huang Transform." Mathematical Problems in Engineering 2018 (August 12, 2018): 1–8. http://dx.doi.org/10.1155/2018/3496870.

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Flutter tests are conducted primarily for the purpose of modal parameter estimation and flutter boundary prediction, the accuracy of which is severely affected by the acquired data quality, structural modal density, and nonstationary conditions. An improved Hilbert-Huang Transform (HHT) algorithm is presented in this paper which mitigates the typical mode mixing effect via modulation. The algorithm is validated by theory, by numerical simulation, and per actual flight flutter test data. The results show that the proposed method could extract the flutter model parameters and predict the flutter speed more accurately, which is feasible for the current flutter test data processing.
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18

Zeng, Jie, and Sunil L. Kukreja. "Flutter Prediction for Flight/Wind-Tunnel Flutter Test Under Atmospheric Turbulence Excitation." Journal of Aircraft 50, no. 6 (November 2013): 1696–709. http://dx.doi.org/10.2514/1.c031710.

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19

Iovnovich, Michael, Tzlil Nahom, Michael Presman, Dorin Avsaid, Tomer Braier, and Daniella E. Raveh. "Assessment of Advanced Flutter Flight-Test Techniques and Flutter Boundary Prediction Methods." Journal of Aircraft 55, no. 5 (September 2018): 1877–89. http://dx.doi.org/10.2514/1.c034716.

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20

Corpas, J. L. Casado, and J. López Díez. "Flutter margin with non-linearities: Real-time prediction of flutter onset speed." Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering 222, no. 6 (June 2008): 921–29. http://dx.doi.org/10.1243/09544100jaero251.

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21

Saputra, Angga Dwi, and R. Wibawa Purabaya. "Prediction of Flutter Boundary Using Flutter Margin for The Discrete-Time System." Journal of Physics: Conference Series 1005 (April 2018): 012019. http://dx.doi.org/10.1088/1742-6596/1005/1/012019.

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22

Price, S. J., and B. H. K. Lee. "Evaluation and Extension of the Flutter-Margin Method for Flight Flutter Prediction." Journal of Aircraft 30, no. 3 (May 1993): 395–402. http://dx.doi.org/10.2514/3.56887.

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23

Yu, Li, Bin Bin Lv, Hong Tao Guo, Yu Yan, Xing Hua Yang, and Jian Guo Luo. "Research on Transonic Wind Tunnel Flutter Test for a Wing Model." Advanced Materials Research 1006-1007 (August 2014): 26–29. http://dx.doi.org/10.4028/www.scientific.net/amr.1006-1007.26.

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This paper adopts self-designed wing model to conduct flutter test on subsonic and transonic, and obtains flutter characteristic of the model, and the test results are used for calibration and verification of flutter procedures. The sub-critical extrapolation is used to obtain the flutter sub-critical parameters and the direct observation method is used to obtain comparison of results. Error of results obtained by the two approaches does not exceed 5%, and validates reliability of the sub-critical prediction approach in continuous adjusted dynamic pressure flutter test.
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24

Gao, Guozhu, Junqiang Bai, Guojun Li, Nan Liu, and Yufei Li. "Flutter Boundary Prediction of a Two Dimensional Airfoil in Transonic Flight Regime with the Preset Angles of Attack." Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University 36, no. 2 (April 2018): 229–37. http://dx.doi.org/10.1051/jnwpu/20183620229.

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Angle of attack has impact on transonic flow filed and aerodynamic force, but most of current researches on flutter use zero angle hypothesis, which has no consideration about angle of attack. Therefore, we use unsteady Reynold Averaged Navier-Stokes (RANS) equation and structural dynamic equation to establish the time domain aeroelastic analysis method. The solution in time domain is the fourth-order implicit Adams linear multi-step method which is based on prediction-correction method. The numerical simulations were used to analyze the transonic flutter boundary of Isogai Case A Model which was based on zero angle condition and nonzero angle respectively. The simulation results show that the reduced flutter speed decreases as the preset angle of attack decreases between 0.73 and 0.76, which shows a 12.5% decrease of the flutter speed at the farthest. Nonzero angle makes the transonic dip weaker and wider than fully turbulent flow. Changing in angle of attack of 6°, the flutter speed in the deepest position of transonic dip has increased by 124% compared to the flutter speed of 0°. Therefore, when flutter characters of airfoil is analyzed, the effects of the initial angle of attack must be taken into account in order to analyze flutter boundary correctly. In other words, increasing the angle of attack offers a way to control the system in terms of delaying flutter.
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25

Li, Jinhua, Zhan Quan, Yao Zhang, Liyuan Cao, and Chunxiang Li. "Computational Fluid Dynamics Based Kriging Prediction on Flutter Derivatives of Flat Steel Box Girders." Symmetry 14, no. 7 (June 23, 2022): 1304. http://dx.doi.org/10.3390/sym14071304.

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An investigation on the flutter derivative prediction of flat steel box girders is carried out based on CFD simulations. Firstly, by taking the flat steel girder section of Qingshan Yangtze River Bridge as the basic section and considering its width and height as the design variables of cross-section shape, the design domain of cross-section shape is defined by controlling the possible variation range of cross-section design variables. A small number of cross-sections are selected for the calculation of aerodynamic forces by CFD simulations. Secondly, according to the aerodynamic lift and moment time-histories of these steel box girders, of which the flutter derivatives are identified by the least square method. Next, these selected cross-section shape design parameters are used as the inputs, and the flutter derivatives obtained from CFD simulations are used as the outputs to train Kriging models. To improve the prediction accuracy of Kriging models, a modified method of model training is presented. Finally, the flutter derivatives of other cross-sections in the design domain are predicted by using the trained Kriging models, and the predicted flutter derivatives are verified by CFD simulations. It is feasible to directly predict the flutter derivatives of steel box girders by Kriging models.
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26

Lu, Bo, Bin Bin Lv, Li Yu, Hong Tao Guo, Yu Yan, and Xi Ping Kou. "Design and Application of an all Moving Wing Model Limiting and Locking Device." Advanced Materials Research 753-755 (August 2013): 1031–34. http://dx.doi.org/10.4028/www.scientific.net/amr.753-755.1031.

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To effectively excite the all moving wing flutter model and limiting or quick locking model in case of bigger amplitude of the model, an excitation and limiting and locking device is designed for the high-speed wind tunnel flutter test model. This paper introduces the structure arrangement, control principle and strategy of this device. The wind tunnel flutter test indicates that this device can enhance the SNR of the test data, improve the boundary prediction precision of flutter, prevent the model from entering the flutter divergence state and protect the model and wind tunnel test equipment.
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27

Yu, Changkun, Zhigang Wu, and Chao Yang. "Flutter Boundary Prediction Based on Structural Frequency Response Functions Acquired from Ground Test." International Journal of Aerospace Engineering 2022 (August 26, 2022): 1–19. http://dx.doi.org/10.1155/2022/2058755.

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Establishing an accurate, fast, and low-risk flutter boundary prediction method is of great significance for flight vehicle design. In this paper, a ground flutter boundary prediction method (GFBP) based on experimental structural frequency response functions (FRFs) is proposed. A low-order multi-input multi-output (MIMO) aeroelastic system is established by combining the structural FRFs acquired from a ground test and the calculated unsteady aerodynamic FRFs in physical coordinates. The multivariable Nyquist criterion is used to predict the flutter boundary. A fixed-root aluminum plate wing is selected as the research model. A GFBP experiment is carried out for the wing’s normal state, leading-edge clump weight state, and trailing-edge clump weight state. The feasibility and accuracy of the proposed method are verified by comparison with theoretical flutter results, in which the errors of flutter speed and frequency in the test statistics are no more than 1.7%. In a simulation model established by the proposed method, Monte Carlo simulation is used to study the influence of deviations in the mode frequency and damping of the structural FRFs and deviations in the positions of excitation and measurement points in the ground test. The experiment and simulation results show that the proposed method can predict the flutter boundary accurately with accurate positions of excitation and measurement points, and it has good robustness to deviations in the mode frequency and amplitude of the structural FRFs.
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28

Lowe, Brandon M., and David W. Zingg. "Efficient Flutter Prediction Using Reduced-Order Modeling." AIAA Journal 59, no. 7 (July 2021): 2670–83. http://dx.doi.org/10.2514/1.j060006.

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29

Vedeneev, Vasily V., Mikhail Kolotnikov, and Pavel Makarov. "Experimental Validation of Numerical Blade Flutter Prediction." Journal of Propulsion and Power 31, no. 5 (September 2015): 1281–91. http://dx.doi.org/10.2514/1.b35419.

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30

French, M., T. Noll, D. Cooley, R. Moore, and F. Zapata. "Flutter prediction involving trailing-edge control surfaces." Journal of Aircraft 25, no. 5 (May 1988): 393–94. http://dx.doi.org/10.2514/3.45593.

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31

Sedaghat, A., J. E. Cooper, J. R. Wright, and A. Y. T. Leung. "Curve fitting approach for transonic flutter prediction." Aeronautical Journal 107, no. 1075 (September 2003): 565–72. http://dx.doi.org/10.1017/s0001924000013452.

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Abstract This paper outlines an initial investigation for determining nonlinear aerodynamics for unsteady transonic flows through the use of curve fitting unsteady computational fluid dynamics (CFD) data. The full aerodynamics including linear and non-linear aerodynamics can be identified as a polynomial function. Through the curve fitting method, the important non-linear terms can be identified and the smaller terms can be neglected. Having modelled the non-linear aerodynamics and included into the aeroelastic model, the characteristics and stability of non-linear aeroelastic system can then be investigated using normal form theory. The methodology is demonstrated upon a simple two-degrees-of-freedom aeroelastic wing model with structural and aerodynamics nonlinearity. A good agreement is obtained for all cases studied between analytical and simulation results.
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32

Pidaparti, R. M. V., V. A. Tischler, and V. B. Venkayya. "Flutter Prediction Methods for Aeroelastic Design Optimization." Journal of Aircraft 38, no. 3 (May 2001): 557–59. http://dx.doi.org/10.2514/2.2797.

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33

Afolabi, Dare, Ramana M. V. Pidaparti, and Henry T. Y. Yang. "Flutter Prediction Using an Eigenvector Orientation Approach." AIAA Journal 36, no. 1 (January 1998): 69–74. http://dx.doi.org/10.2514/2.353.

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34

Lind, Rick. "Flight-Test Evaluation of Flutter Prediction Methods." Journal of Aircraft 40, no. 5 (September 2003): 964–70. http://dx.doi.org/10.2514/2.6881.

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35

Crowther, W. J., and J. E. Cooper. "Flight test flutter prediction using neural networks." Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering 215, no. 1 (January 2001): 37–47. http://dx.doi.org/10.1243/0954410011531736.

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36

Pankaj, Achuthan C., G. Shanthini, M. V. Shivaprasad, and M. Manjuprasad. "Aircraft flutter prediction using experimental modal parameters." Aircraft Engineering and Aerospace Technology 85, no. 2 (March 15, 2013): 87–96. http://dx.doi.org/10.1108/00022661311302698.

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37

Afolabi, Dare, M. V. Pidaparti, and Henry T. Y. Yang. "Flutter prediction using an eigenvector orientation approach." AIAA Journal 36 (January 1998): 69–74. http://dx.doi.org/10.2514/3.13779.

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38

Zhang, X. W., Y. R. Wang, and K. N. Xu. "Flutter prediction in turbomachinery with energy method." Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering 225, no. 9 (July 22, 2011): 995–1002. http://dx.doi.org/10.1177/0954410011405942.

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39

Fang, Li Cheng, and Shun Ming Li. "A Review of the Research on Aeroelasticity in Aero Turbomachinery." Advanced Materials Research 651 (January 2013): 694–700. http://dx.doi.org/10.4028/www.scientific.net/amr.651.694.

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Aeroelasticity in the form of blade flutter is a major concern for designers in the field of turbomachinery. This paper presents a review of the research and development on blade flutter modeling, including the unsteady aerodynamic model, the structural model and flutter prediction methods. Based on the presentation of these models, the fundamental mechanism and effects of different treatments are discussed. At the end of paper, some deficiencies in the research of flutter and difficulties in modeling fluid-solid coupling effects are pointed out, to which attention should be paid in future.
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40

Schäfer, Dominik. "T-tail flutter simulations with regard to quadratic mode shape components." CEAS Aeronautical Journal 12, no. 3 (June 18, 2021): 621–32. http://dx.doi.org/10.1007/s13272-021-00524-8.

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AbstractIt is known that the dynamic aeroelastic stability of T-tails is dependent on the steady aerodynamic forces at aircraft trim condition. Accounting for this dependency in the flutter solution process involves correction methods for doublet lattice method (DLM) unsteady aerodynamics, enhanced DLM algorithms, unsteady vortex lattice methods (UVLM), or the use of CFD. However, the aerodynamic improvements along with a commonly applied modal approach with linear displacements results in spurious stiffness terms, which distort the flutter velocity prediction. Hence, a higher order structural approach with quadratic mode shape components is required for accurate flutter velocity prediction of T-tails. For the study of the effects of quadratic mode shape components on T-tail flutter, a generic tail configuration without sweep and taper is used. Euler based CFD simulations are applied involving a linearized frequency domain (LFD) approach to determine the generalized aerodynamic forces. These forces are obtained based on steady CFD computations at varying horizontal tail plane (HTP) incidence angles. The quadratic mode shape components of the fundamental structural modes for the vertical tail plane (VTP), i.e., out-of-plane bending and torsion, are received from nonlinear as well as linear finite element analyses. Modal coupling resulting solely from the extended modal representation of the structure and its influence on T-tail flutter is studied. The g-method is applied to solve for the flutter velocities and corresponding flutter mode shapes. The impact of the quadratic mode shape components is visualized in terms of flutter velocities in dependency of the HTP incidence angle and the static aerodynamic HTP loading.
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41

Casoni, Marco, and Ernesto Benini. "A Review of Computational Methods and Reduced Order Models for Flutter Prediction in Turbomachinery." Aerospace 8, no. 9 (September 2, 2021): 242. http://dx.doi.org/10.3390/aerospace8090242.

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Abstract:
Aeroelastic phenomena in turbomachinery are one of the most challenging problems to model using computational fluid dynamics (CFD) due to their inherent nonlinear nature, the difficulties in simulating fluid–structure interactions and the considerable computational requirements. Nonetheless, accurate modelling of self-sustained flow-induced vibrations, known as flutter, has proved to be crucial in assessing stability boundaries and extending the operative life of turbomachinery. Flutter avoidance and control is becoming more relevant in compressors and fans due to a well-established trend towards lightweight and thinner designs that enhance aerodynamic efficiency. In this paper, an overview of computational techniques adopted over the years is first presented. The principal methods for flutter modelling are then reviewed; a classification is made to distinguish between classical methods, where the fluid flow does not interact with the structure, and coupled methods, where this interaction is modelled. The most used coupling algorithms along with their benefits and drawbacks are then described. Finally, an insight is presented on model order reduction techniques applied to structure and aerodynamic calculations in turbomachinery flutter simulations, with the aim of reducing computational cost and permitting treatment of complex phenomena in a reasonable time.
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42

Pak, Chan-gi. "Unsteady Aerodynamic Model Tuning for Precise Flutter Prediction." Journal of Aircraft 48, no. 6 (November 2011): 2178–84. http://dx.doi.org/10.2514/1.c031495.

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43

Baldelli, Darío H., Richard Lind, and Martin Brenner. "Control-Oriented Flutter/Limit-Cycle-Oscillation Prediction Framework." Journal of Guidance, Control, and Dynamics 31, no. 6 (November 2008): 1634–43. http://dx.doi.org/10.2514/1.36117.

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44

Argaman, Matan, and Daniella E. Raveh. "Multioutput Autoregressive Aeroelastic System Identification and Flutter Prediction." Journal of Aircraft 56, no. 1 (January 2019): 30–42. http://dx.doi.org/10.2514/1.c034789.

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45

Gu, Wenjing, and Li Zhou. "Flutter Onset Prediction Based on Parametric Model Estimation." Journal of Aircraft 57, no. 6 (November 2020): 1026–43. http://dx.doi.org/10.2514/1.c035833.

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46

Georghiades, G. A., and J. R. Banerjee. "Flutter Prediction for Composite Wings Using Parametric Studies." AIAA Journal 35, no. 4 (April 1997): 746–48. http://dx.doi.org/10.2514/2.170.

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47

Torii, Hiroshi, and Yuji Matsuzaki. "Flutter Boundary Prediction Based on Nonstationary Data Measurement." Journal of Aircraft 34, no. 3 (May 1997): 427–32. http://dx.doi.org/10.2514/2.2187.

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48

Sisto, F., S. Thangam, and A. Abdel-Rahim. "Computational prediction of stall flutter in cascaded airfoils." AIAA Journal 29, no. 7 (July 1991): 1161–67. http://dx.doi.org/10.2514/3.10718.

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49

Chung, Chan-Hoon, and Sang-Joon Shin. "Validation of a Robust Flutter Prediction by Optimization." International Journal of Aeronautical and Space Sciences 13, no. 1 (March 30, 2012): 43–57. http://dx.doi.org/10.5139/ijass.2012.13.1.43.

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50

Chen, Chern-Hwa, Jong-Cheng Wu, and Jow-Hua Chen. "Prediction of flutter derivatives by artificial neural networks." Journal of Wind Engineering and Industrial Aerodynamics 96, no. 10-11 (October 2008): 1925–37. http://dx.doi.org/10.1016/j.jweia.2008.02.044.

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