Hostname: page-component-cd9895bd7-jkksz Total loading time: 0 Render date: 2024-12-22T10:49:38.487Z Has data issue: false hasContentIssue false

Statistical evaluation of flutter boundaries from flight flutter test data

Published online by Cambridge University Press:  03 February 2016

A. A. Abbasi
Affiliation:
School of Mechanical, Aerospace and Civil Engineering, University of Manchester, Manchester UK
J. E. Cooper
Affiliation:
Department of Engineering, University of Liverpool, Liverpool, UK

Abstract

A methodology is described that determines the statistical confidence bounds on the results from flight flutter tests: modal parameter estimates, flutter margin values and flutter speed estimates, without the need for Monte-Carlo simulation. The approach is based on least squares statistics and eigenvalue perturbation theory applied to the various stages of the analysis process, starting with frequency and damping estimation through to the flutter margin calculations. The technique is demonstrated upon a number of data sets from aeroelastic simulations of flight flutter tests.

Type
Research Article
Copyright
Copyright © Royal Aeronautical Society 2009 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

1. Garrick, I.E. and Reed, W.H., Historical development of aircraft flutter. J Aircr, 1981, 18, (11), pp 897912.Google Scholar
2. Wright, J.R. and Cooper, J.E., Introduction to Aircraft Aeroelasticity and Loads, 2007, John Wiley & Sons.Google Scholar
3. Collar, A.R., The first fifty years of aeroelasticity, Aerospace, February 1978, 545, pp 1220.Google Scholar
4. Kehoe, M.W., A historical overview of flight flutter testing, AGARD Conference Proceedings 566, 1995, Advanced Aeroservoelastic Testing and Data Analysis, Rotterdam, The Netherlands.Google Scholar
5. Cooper, J.E. et al. Advances in the analysis of flight flutter test data, Advanced Aeroservoelastic Testing and Data Analysis (CP-566), 1995, Rotterdam.Google Scholar
6. Zimmerman, N.H. and Weissenberger, J.T., Prediction of flutter onset speed based on flight flutter testing at subcritical speeds, J Aircr, 1964. 1, (4), pp 190202.Google Scholar
7. Nissim, E. and Gilyard, G.B., Method for experimental determination of flutter speed by parameter identification, 1989, AIAA-89_1324-CP.Google Scholar
8. Cooper, J.E., Desforges, M.J. and Wright, J.R., The on-line envelope function — a guide to aeroelastic stability, 1993, IFASD Conference, pp 981997.Google Scholar
9. Cooper, J.E., Emmett, P.R. and Wright, J.R., Envelope function — a tool for analyzing flutter data, J Aircr, 1993, 30, (3), pp 785790.Google Scholar
10. Torii, H. and Matsuzaki, Y., Flutter margin evaluation for discrete-time systems, J Aircr, 2001, 38, (1), pp 4247.Google Scholar
11. Dimitriadis, G. and Cooper, J.E., Flutter prediction from flight flutter test data, J Aircr, 2001, 38, (2), pp 355367.Google Scholar
12. Lind, R., Flight test evaluation of flutter prediction methods, J Aircr, 2003, 40, (5), pp 964970.Google Scholar
13. Lind, R.C., Flight testing with the flutterometer, J Aircr, 2003, 40, (3), pp 574579.Google Scholar
14. Lind, R.C. and Brenner, M., The flutterometer: an on-line tool to predict robust flutter margins, J Aircr, 2000, 37, (6), pp 11051112.Google Scholar
15. Lind, R.C., Robust flutter margin analysis that incorporates flight data, 1998, NASA/TP-1998 206543.Google Scholar
16. Lind, R.C. and Brenner, M., Robust flutter margins of an F/A-18 aircraft from aeroelastic flight data, J Guidance, Control and Dynamics, 1997, 20, (3), pp 597604.Google Scholar
17. Petit, C.L., Uncertainty quantification in aeroelasticity: recent results and research challenges, J Aircr, 2004, 41, (5), pp 12171229.Google Scholar
18. Peterson, L.D., Bullock, S.J. and Doebling, S.W., The statistical sensitivity of experimental modal frequencies and damping ratios to measurement noise, Modal Analysis (USA), 1996, 11, (1), pp 6375.Google Scholar
19. Bergmann, M., Longman, R.W. and Juang, J.-N.. Variance and bias computation for enhanced system identification, 1989, 28th IEEE Conference on Decision and Control.Google Scholar
20. Perriot, A., Raynaud, J.L. and Cogan, S.. Uncertainties in identified modal parameters, 1998, ISMA23.Google Scholar
21. Doebling, S.W. and Farrar, C.R., Estimation of statistical distributions for modal parameters identified from averaged frequency response function data, J Vibration and Control, 2001, 7, (4), pp 603624.Google Scholar
22. Farrar, C.R., Doebling, S.W. and Cornwell, P.J., A comparison study of modal parameter confidence intervals computed using the Monte Carlo and Bootstrap techniques, 1998, 16th International Modal Analysis Conference, Santa Barbara, California, pp 936944.Google Scholar
23. Longman, R.W. et al. Variance and bias computation for improved modal identification using ERA/DC, 1991, American Control Conference, Boston, MA.Google Scholar
24. Troyer, T.D. et al Fast derivation of uncertainty bounds for on-line flight flutter testing, 2006, ISMA, Leuven, Belgium.Google Scholar
25. Pintelon, R., Guillaume, P. and Schoukens, J., Uncertainty calculation in (operational) modal analysis, Mechanical Systems and Signal Processing, 2007, 21, (6), pp 23592373.Google Scholar
26. Guillaume, P., Verboven, P. and Vanlanduit, S., Frequency-domain maximum likelihood identification of modal parameters with confidence intervals. 1998, ISMA 23.Google Scholar
27. Poirel, D., Dunn, S. and Porter, J., Flutter-margin method accounting for modal parameter uncertainties, J Aircr, 2005, 42, (5), pp 12361243.Google Scholar
28. Price, S.J. and Lee, B.H.K., Evaluation and extension of flutter-margin method for flight flutter prediction, J Aircr, 1993, 30, (3), pp 395402.Google Scholar
29. Heeg, J., Stochastic characterization of flutter using historical wind tunnel data, 2007, 48th AIAA SDM Conference.Google Scholar
30. Cooper, J.E., Comparison of some time domain system identification techniques using approximate data correlations, Int J Analytical and Experimental Modal Analysis, 1989, 4, (2), pp 5157.Google Scholar
31. Brown, D.L. et al Parameter estimation techniques for modal analysis, 1979, SAE Technical Paper 790221.Google Scholar
32. Deblauwe, F., Brown, D.L. and Allemang, R.J., The polyreference time domain technique, 1987, IMAC V, pp 832845.Google Scholar
33. Juang, J.N. and Pappa, R.S., An eigensystem realization algorithm for modal parameter identification and model reduction, J Guidance, Control and Dynamics, 1985. 8, (5), pp 620627.Google Scholar
34. Overschee, P.V. and Moor, B.L.D., Subspace Identification for Linear Systems: Theory, Implementation, Applications, 1996, p 256, Kluwer Academic Publishers.Google Scholar
35. Benini, G.R. et al Flutter clearance of a non-linear aircraft, 2005, International Forum on Aeroelasticity and Structural Dynamics, Munich, Germany.Google Scholar
36. Katz, H., Foppe, F.G. and Grossman, D.T., F-15 Flight Flutter Test Program, 1976, NASA SP-415, pp 413431, NASA, Washington, DC.Google Scholar
37. Cooper, J.E., Emmett, P.R. and Wright, J.R., A statistical confidence factor for modal parameter identification, 1992, 17th International Seminar on Modal Analysis, pp 16111626.Google Scholar
38. Dimitriadis, G. and Cooper, J.E., Online flight flutter testing, 2001, International Conference on Structural System Identification, Kassel, Germany.Google Scholar
39. Desforges, M.J., Cooper, J.E. and Wright, J.R., Spectral and modal parameter estimation from output-only measurements, Mech Systems and Signal Processing, 1995, 9, (2), pp 169186.Google Scholar
40. Mucheroni, M.F. and Cardoso, A., Output-only structural identification of random vibrating systems, J Brazilian Society of Mechanical Sciences and Engineering, 2006, 28, (1), pp 99104.Google Scholar
41. Longman, R.W. and Juang, J.-N., A variance based confidence criterion for ERA identified modal parameters, AAS/AIAA Astrodynamics Specialist Conference, 1987, Kalispell, Montana.Google Scholar
42. Markovsky, I. et al When is a pole spurious? 2006, International Conference on Noise and Vibration Engineering (ISMA 2006).Google Scholar
43. Guillaume, P., Schoukens, J. and Pintelon, R., Sensitivity of roots to errors in the coefficients of polynomials obtained by frequency-domain estimation methods, IEEE transactions on instrumentation and measurement, 1989, 38, (6), pp 10501056.Google Scholar
44. Golub, G.H. and Loan, C.F.V., Matrix Computations, 2nd Edition, 1996, Baltimore, London: Johns Hopkins University Press.Google Scholar
45. Kendall, M.G., Stuart, A., and Ord, J.K, The Advanced Theory of Statistics, 4th Edition, Design and Analysis, and Time-Series Vol 3, 1983, Charles Griffin & Company, London.Google Scholar
46. Yates, F., Sampling Methods for Censuses and Surveys. 4th Edition, 1981 Charles Griffin & Company, London.Google Scholar
47. Dieters, M.J. et al. Application of approximate variances of variance components and their ratios in genetic tests, Theoretical and Applied Genetics, 1995, 91, (1), pp 1524.Google Scholar
48. Blumenfeld, D., Operations Research Calculations Handbook, 2001, Boca Raton, FL 33431, CRC Press.Google Scholar
49. Funkhouser, E.M. and Grossman, M., Bias in estimation of the sampling variance of an estimate of heritability. J Heredity, 1982, 73, (2), pp 139141.Google Scholar
50. Pearson, K., Mathematical contributions to the theory of evolution.— on a form of spurious correlation which may arise when indices are used in the measurement of organs, Royal Society of London, 1897, 60, (18961897), pp 489498.Google Scholar