Hostname: page-component-6898cfcb55-p2bvx Total loading time: 0 Render date: 2026-11-11T03:14:25.819Z Has data issue: false hasContentIssue false
  • English
  • Français

Flight parameter based fatigue analysis approach for a fighter aircraft

Published online by Cambridge University Press:  03 February 2016

J. A. Tikka
J. A. Tikka*
Affiliation:
Patria Aviation, Halli, Finland
Rights & Permissions [Opens in a new window]

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the 'Save PDF' action button.

This paper describes a flight parameter based fatigue life analysis approach, which is developed for the Finnish Air Force F-18 fighters. It produces a flight specific fatigue life estimate for structural details using flight parameter data stored by each aircraft. Artificial neural networks are used to model structural response of analyzed details as a function of flight parameters. The analysis development is based on strain gauge data from 25 flights of an instrumented aircraft. The results show a satisfactory accuracy for the fatigue life estimates and prove the concept level analysis capability. The average difference between measured and modelled fatigue life is 21% for the fuselage bulkhead and 30% for the leading edge flap’s hinge area. The total differences in the Finnish Air Force average usage are extremely small, being –2% for the bulkhead and +2% for the leading edge flap.

Information

Type
Research Article
Information
The Aeronautical Journal , Volume 112 , Issue 1128 , February 2008, pp. 79 - 91
Copyright
Copyright © Royal Aeronautical Society 2008 

References

1. Molent, L. et al, Development of analytical techniques for calibration of F/A-18 horizontal stabilator and wing fold strain sensors, report DSTO-TR-0205, Melbourne, Australia, 1995.Google Scholar
2. Uhl, T., Applications of neural networks for identifications of loads in mechanical structures, SMART conference, Warsaw, Poland, 2001.Google Scholar
3. Reed, S., Development of a parametric aircraft fatigue monitoring system using artificial neural networks, at the 22nd ICAF conference, Lucerne, Switzerland, 2003.Google Scholar
4. Siljander, A., A review of aeronautical fatigue investigations in Finland during the period April 2003 to April 2005, 29th Conference of the International Committee on Aeronautical Fatigue (ICAF), Hamburg, Germany, June 2005.Google Scholar
5. Spline toolbox for use with Matlab, The MathWorks., Natick, MA, USA, 2004.Google Scholar
6. Öström, J., Inverse flight simulation for a fatigue life management system, AIAA Paper 2005-6212, in AIAA Modeling and Simulation Technologies Conference, San Francisco, California, 15-18 August 2005.Google Scholar
7. Hyötyniemi, H., Multivariate regression models, Helsinki University of Tech., ISSN 0356-0872, Picaset Oy, Espoo, 2000.Google Scholar
8. Salonen, T., An assessment of the feasibility of a parameter-based aircraft strain monitoring method, M.Sc. dissertation, Helsinki Univ of Tech, Espoo 2005 (in Finnish).Google Scholar
9. Neural network toolbox for use with Matlab, The MathWorks, Natick, MA, USA, 2001.Google Scholar
10. Tikka, J., Parameter based fatigue life analysis, Influence of Training Data Selection to Analysis Performance, AIAA Paper 2006-6473, in AIAA Modeling and Simulation Technologies Conference, Keystone, CO, 21-24 August, 2006.Google Scholar
11. McCracken, K., Clarification on Reynolds Number and the correlator thermal design, SMA Technical Memorandum # 118, http://sma-www.cfa.harvard.edu/private/memos/118.pdf, 21.11.2005 Google Scholar