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A comparative assessment of flutter prediction techniques

Published online by Cambridge University Press:  27 October 2020

U.P.V. Sudha Open the ORCID record for U.P.V. Sudha [Opens in a new window] ,
G.S. Deodhare  and
K. Venkatraman
U.P.V. Sudha*
Affiliation:
Aeronautical Development Agency Bangalore India 560017
G.S. Deodhare
Affiliation:
Programme Director (Combat Aircraft) & Director Aeronautical Development Agency Bangalore India 560017
K. Venkatraman
Affiliation:
Aerospace Engineering Department Indian Institute of Science Bangalore India 560012
*
[email protected]
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Abstract

To 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.

Keywords

Flight flutter test prediction techniqueARMAFlutter MarginThree-degree-of-freedom model
Type
Survey Paper
Information
The Aeronautical Journal , Volume 124 , Issue 1282 , December 2020 , pp. 1945 - 1978
Copyright
© The Author(s), 2020. Published by Cambridge University Press on behalf of Royal Aeronautical Society

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References

REFERENCES

Nam, C., Kim, Y. and Weisshaar, T.A. Computational Aids in Aerservoelastic Analysis using MATLAB, Tata McGraw-Hill Publishing Company Limited, 2001.Google Scholar
Hayes, W.B., Goodman, C.E. and Sisk, K.D. F/A-18 E/F Super Hornet flutter clearance program, No. AIAA Paper 2003-1940, AIAA/ASME/ASCE/AHS Structures, Structural Dynamics and Materials Conference, Norfolk, Virginia, USA, April 2003.10.2514/6.2003-1940Google Scholar
Kehoe, M.W. A historical overview of flight flutter testing, Tech. Rep. NASA TM 4720, NASA, Washington, DC 20546-0001, May 8--10 1995.Google Scholar
MIL-A-8870C, Military Specification - Airplane Strength and Rigidity, Vibration, Flutter, and Divergence, Air Force Flight Dynamics Laboratory (AFFDL), 1993.Google Scholar
Desmarais, R.N. and Bennett, R.M. An automated procedure for computing flutter eigenvalues, J. Aircr., February 1974, 11, (2), pp 75–83.Google Scholar
Koenig, K. Flight vibration test analysis–Methods, theory and application, AIAA/AHS/IES/SetP/SFTE/DGLR Flight testing conference, No. AIAA Paper 1983-2752, Las Vegas, Nevada, November 16–18 1983, pp 1–20.Google Scholar
Zimmerman, N.H. and Weissenburger, J.T. Prediction of flutter onset speed based on flight testing at subcritical speeds, J. Aircr., July-August 1964, 1, (4), pp 190–202.Google Scholar
Cooper, J.E. Parameter estimation methods for flight flutter testing, 80th AGARD Structures and Materials Panel, No. AGARD CP-566, Rotterdam, The Netherlands, 8-10 May 1995, pp 10–1–10–11.Google Scholar
Kayran, A. Flight flutter testing and aeroelastic stability of aircraft, Int. J. Aircr. Eng. Aerosp. Technol., 2007, 79, (5), pp 494–506.Google Scholar
Dawson, K.S. and Maxwell, D.L. Limit cycle oscillation flight-test results for asymmetric store configuration, J. Aircr., November-December 2005, 42, (6), pp 1589–1596.CrossRefGoogle Scholar
Cooper, J.E., Emmet, P.R. and Wright, J.R. Envelope function – A tool for analyzing flutter data, J. Aircr., September–October 1993, 30, (5), pp 785–790.Google Scholar
Dimitriadis, G. and Cooper, J.E. Flutter prediction from flight flutter test data, J. Aircr., 2001, 38, (2), pp 355–367.Google Scholar
Houbolt, J.C. and Rainey, A.G. On the prediction of critical flutter conditions from subcritical response data and some related wind-tunnel experience, Proceedings of the 1958 Flight Flutter Testing Symposium, No. NASA-SP-385, Washington, D.C., May 15–16 1958, pp 23–29.Google Scholar
Sandford, M.C., Abel, I. and Gray, D.L. Development and demonstration of a flutter-suppression system using active controls, Tech. Rep. NASA TR R-450, NASA Langley Research Center, Washington D.C., 1975.Google Scholar
Foughner, J.T.J. Some experience using subcritical response methods in wind-tunnel flutter model studies, Flutter testing techniques, No. NASA SP-415, NASA, Washington D.C, October 1976.Google Scholar
Doggett, R.V.J. Some observations on the houbolt-rainey and peak-hold methods of flutter onset prediction, Tech. Rep. NASA TM-102745, NASA, Langley Research Center, Hampton, Virginia, 23665-5225, November 1990.Google Scholar
Brenner, M.J., Lind, R.C. and Voracek, D.F. Overview of recent flight flutter testing research at NASA Dryden, 38th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Material Conference and Exhibit, Kissimmee, Floida, No. NASA TM-4792, NASA Dryden Flight Research Center, Kissimmee, Floida, April 7–10 1997.Google Scholar
Lind, R. A flight test to demonstrate flutter and evaluate the flutterometer, Aeronaut. J., June 2003, 107, pp 577–588.Google Scholar
Lind, R. Flight-test evaluation of flutter prediction methods, J. Aircr., September–October 2003, 40, (5), pp 964–970.Google Scholar
Lind, R. Flight testing with the flutterometer, J. Aircr., May–June 2003, 40, (5), pp 574–579.Google Scholar
Lind, R. and Brenner, M. Robust flutter magins of an F/A -18 aircraft from aeroelastic flight data, J. Guid. Control Dyn., May–June 1997, 20, (3), pp 597–604.Google Scholar
Lind, R. and Brenner, M. Incorporating flight data into a robust aeroelastic model, J. Aircr., May–June 1998, 35, (3), pp 470–477.CrossRefGoogle Scholar
Lind, R. and Brenner, M. Flutterometer: An on-line tool to predict robust flutter margins, J. Aircr., November–December 2000, 37, (6), pp 1105–1112.CrossRefGoogle Scholar
Price, S.J. and Lee, B.H.K. Evaluation and extension of the flutter-margin method for flight flutter prediction, J. Aircr., May–June 1993, 30, (3), pp 395–402.Google Scholar
Lind, R. Flutter margins for multi-mode unstable couplings with associated flutter confidence, J. Aircr., September–October 2009, 46, (5), pp 1563–1568.Google Scholar
Torii, H. and Matsuzaki, Y. Flutter margin evaluation for discrete-time systems, J. Aircr., January–February 2001, 38, (1), pp 42–47.CrossRefGoogle Scholar
Katz, H., Foppe, F.G. and Grossman, D.T. F-15 flight flutter test program, Flutter Testing Techniques, No. NASA SP-415, Dryden Flight Research Center, 1976, pp 413–431.Google Scholar
Astrom, K.J. and Eykhoff, P. System identification - A survey, Automatica, 1971, 7, pp 123–162.Google Scholar
Ljung, L. System Identification - Theory for the user, Prentice Hall PTR, 1999, New Jersey.Google Scholar
Sundaramurthy, H., Jategaonkar, R.V. and Balakrishna, S. Dynamic stability measurements from tunnel unsteadiness excited random response, J. Aircr., January 1980, 17, (1), pp. 7–12.Google Scholar
Perangelo, H.J. and Waisanen, P.R. Application of advanced parameter identification methods for flight flutter data analysis with comparisons to current techniques, AGARD Conference Proceedings: Flight Test Techniques, Vol. 29, Grumman Aerospace Corporation, Calverton, New York, July 1984, pp 1–28.Google Scholar
Batill, S.M., Carey, D.M. and Kehoe, M.W. Digital time series analysis for flutter test data, AIAA Dynamics Specialist Conference, Dallas, Texas, No. AIAA Paper 92-2103-CP, April 1992, pp 215–223.CrossRefGoogle Scholar
Pinkelman, J.K., Batill, S.M. and Kehoe, M.W. An investigation of the total least square criteria in time domain based, parameter identification for flight flutter testing, AIAA/ASME/ASCE/AHS/ASC 36th Structures, Dynamics and Materials Conference, No. AIAA Paper 95-1247-CP, New Orleans, Louisiana, April 1995, pp 783–793.CrossRefGoogle Scholar
Pinkelman, J.K., Batill, S.M. and Kehoe, M.W. Total least square criteria in time domain based, parameter identification for flight flutter testing, J. Aircr., July–August 1996, 33, (4), pp 784–792.CrossRefGoogle Scholar
McNamara, J.J. and Friedmann, P.P. Flutter-boundary identification for time-domain computational aeroelasticity, AIAA J., 2007, 45, (7), pp 1546–1555.CrossRefGoogle Scholar
Jategaonkar, R.V. and Balakrishna, S. Autoregressive modeling and damping evaluation from random excitation response of structures, Proceedings, National System Conference, India, 1977, pp B1–1–5.Google Scholar
Kay, S.M. Modern Spectral Estimation, Prentice-Hall Inc., 1988, Englewoods Cliff, New Jersey.Google Scholar
Kay, S.M. and Marple, S.L. Spectrum analysis - A modern prospective, Proc. IEEE, November 1981, 69, pp 1380–1419.CrossRefGoogle Scholar
Raol, J.R., Jategaonkar, R.V. and Balakrishna, S. Determination of model order for dynamical system, IEEE Trans. Syst. Man Cybern., January/February 1982, SMC-12, (1), pp 56–62.Google Scholar
Akaike, H. Statistical predictor identification, Ann. Inst. Stat. Math., 1970, 22, pp 203–217.CrossRefGoogle Scholar
Akaike, H. A new look at the statistical model identification, IEEE Trans. Autom. Control, December 1974, AC-19, (6), pp 716–723.CrossRefGoogle Scholar
Pan, W. Akaike’s information criterion in generalized estimating equations, Biometrics, March 2001, 57, (1), pp 120–125.CrossRefGoogle ScholarPubMed
Bozdogan, H. Akaike’s information criterion and recent developments in information complexity, J. Math. Psychol., 2000, 44, pp 62–91.CrossRefGoogle ScholarPubMed
Matsuzaki, Y. and Ando, Y. Estimation of flutter boundary from random responses due to turbulence at subcritical speeds, J. Aircr., 1981, 18, (10), pp 862–868.CrossRefGoogle Scholar
Kuo, B.C. Automatic Control System, Prentice-Hall Inc., Englewoods Cliff, 1987, New Jersey.Google Scholar
Bae, J.S., Matsuzaki, Y. and Inman, D.J. Extension of flutter prediction parameter for multimode flutter system, J. Aircr., 2010, 42, (1), pp 285–288.CrossRefGoogle Scholar
Torii, H. and Matsuzaki, Y. Flutter margin evaluation for three-mode discrete-time sytems, 52th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Material Conference, No. AIAA Paper 2011-2144, 4-7 April 2011, pp 1–8.CrossRefGoogle Scholar
Theodorsen, T. General theory of aerodynamic instability and the mechanism of flutter, Tech. Rep. NACA Technical Report 496, NACA, 1935.Google Scholar
Vepa, R. Finite State Modeling of Aeroelastic Systems, Tech. Rep. NASA CR-2779, NASA, 1977.Google Scholar
Edwards, J. Applications of Laplace transform methods to airfoil motion and stability calculations, AIAA J., 1979, (79-0772), pp 465–481.Google Scholar
Karpel, M. Design for active flutter suppression and gust alleviation using state-space aeroelastic modeling, J. Aircr., 1982, 19, (3), pp 221–227.CrossRefGoogle Scholar
Karpel, M. Design for active and passive flutter suppression and gust alleviation, Tech. Rep. NASA CR-3482, NASA, 1981.CrossRefGoogle Scholar
Karpel, M. and Strul, E. Minimum-state unsteady aerodynamic approximations with flexible constraints, J. Aircr., 1996, 33, (6), pp 1190–1196.CrossRefGoogle Scholar
Wang, Z. and Chen, P.C. Adapted K-method for frequency-domain ASE control stability margin analysis, AIAA/ASMe/ASCE/AHS/SC Structures, Structural Dynamics, and Materials Conference, National Harbor, Maryland, January 2014.CrossRefGoogle Scholar
Karpel, M. Extensions to the minimum-state aeroelastic modeling method, AIAA J., 29, (11), 2008, pp 2007–2009.CrossRefGoogle Scholar
Biskri, D., Botez, R.M., Stathopoulos, N., Therien, S., Rathe, A. and Dickinson, M. New mixed method for unsteady aerodynamic force approximations for aeroservoelasticity studies, AIAA J., 2006, 43, (5), pp 1538–1542.Google Scholar
Dinu, A., Botez, R.M. and Cotoi, I. Chebyshev polynomials for unsteady aerodynamic calculations in aeroservoelasticity, J. Aircr., 2006, 43, (1), pp 165–171.CrossRefGoogle Scholar
Botez, R.M., Dinu, A. and Cotoi, I. Method based on Chebyshev polynomials for aerservoelastic interactions on an FA-18 aircraft, J. Aircr., 2007, 44, (1), pp 330–333.CrossRefGoogle Scholar
Roger, K.L. Airplane math modeling methods for active control design, Tech. Rep. AGARD-CP-228, AGARD, 1977.Google Scholar
Wright, J.R. and Cooper, J.E. Introduction to Aircraft Aeroelasticity and Loads, John Wiley and Sons, 2007, U.K. CrossRefGoogle Scholar
Eldred, M.S., Venkayya, V.B. and Anderson, W.J. New mode tracking methods in aeroelastic analysis, AIAA J., July 1995, 33, (7), pp 1292–1299.CrossRefGoogle Scholar
Fung, Y.C. An Introduction to the Theory of Aeroelasticity, John Wiley & Sons, Inc., 1955, New York.Google Scholar
Hancock, G.J., Wright, J.R. and Simpson, A. On the teaching of the principles of wing flexure-torsion flutter, Aeronaut. J., October 1985, 89, (888), pp 285–305.Google Scholar
Ljung, L. System Identification Tool Box - For use with Matlab, Version 5, The Mathworks, Inc., 2002, 3 Apple Hill Drive, Natick, MA, USA.Google Scholar
Jury, I.E. Theory and Application of the Z-Transform Method, Wiley, 1964, New York.Google Scholar