Hostname: page-component-78c5997874-mlc7c Total loading time: 0 Render date: 2024-11-19T09:38:29.807Z Has data issue: false hasContentIssue false
  • English
  • Français

Bayesian sensitivity analysis of flight parameters that affect main landing gear yield locations

Published online by Cambridge University Press:  27 January 2016

P. Sartor ,
K. Worden ,
R. K. Schmidt  and
D. A. Bond
P. Sartor*
Affiliation:
Department of Aerospace Engineering, University of Bristol, Bristol, UK
K. Worden*
Affiliation:
Department of Mechanical Engineering, University of Sheffield, Sheffield, UK
R. K. Schmidt*
Affiliation:
Messier-Bugatti-Dowty, Gloucester, UK
D. A. Bond*
Affiliation:
Messier-Bugatti-Dowty, Gloucester, UK
*
[email protected]
[email protected]
[email protected]
[email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

An aircraft and landing gear loads model was developed to assess the Margin of Safety (MS) in main landing gear components such as the main fitting, sliding tube and shock absorber upper diaphragm tube. Using a technique of Bayesian sensitivity analysis, a number of flight parameters were varied in the aircraft and landing gear loads model to gain an understanding of the sensitivity of the MS of the main landing gear components to the individual flight parameters in symmetric two-point landings. The significant flight parameters to the main fitting MS, sliding tube bending moment MS and shock absorber upper diaphragm tube MS include: longitudinal tyre-runway friction coefficient, aircraft vertical descent velocity, aircraft Euler pitch angle and aircraft mass. It was also shown that shock absorber servicing state and tyre pressure do not contribute significantly to the MS.

Type
Research Article
Information
The Aeronautical Journal , Volume 118 , Issue 1210 , December 2014 , pp. 1481 - 1497
Copyright
Copyright © Royal Aeronautical Society 2014 

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. Schmidt, R.K. and Sartor, P. Landing Gear, Encyclopaedia of Structural Health Monitoring, edited by Boller, C., Chang, F.-K. and Fujino, Y. Wiley, 2009, pp 1983–1994.Google Scholar
2. Reddick, H.K. Safe-Life and Damage-Tolerant Design Approaches for Helicopter Structures,Tech. rep, Applied Technology Laboratory, US Army Research and Technology Laboratories, Ft Eustis, Virginia USA.Google Scholar
3. Sartor, P., Bond, D., Staszewski, W. and Schmidt, R.K. Assessment of the value of an overload indication system through analysis of aviation industry occurrence data, AIAA J Aircr, 2009, 46, (5), pp 1692–1705.Google Scholar
4. Saltelli, A., Chan, K. and Scott, E.M. Sensitivity Analysis, Wiley, 2000.Google Scholar
5. Oakley, J.E. and O’hagen, A. Probabilistic sensitivity analysis of complex models: A Bayesian approach, J Royal Statistical Society Series B, 2004, 66, (3), pp 751–769.Google Scholar
6. Worden, K. and Manson, G. On the Identifcation of Hysteretic Systems, Part I: Fitness Landscapes and Evolutionary Identifcation, Mechanical Systems and Signal Processing, 2012, 29, pp 201–212.Google Scholar
7. Wright, J.R. and cooper, J.E. Introduction to Aircraft Aeroelasticity and Loads, Wiley, 2007.Google Scholar
8. Milwitzky, B., Lindquist, D. and Potter, D. An Experimental Study of Applied Ground Loads in Landing, Tech Rep 1248, NACA, 1955.Google Scholar
9. Kennedy, M.C., Anderson, C.W., Conti, S. and O’hagen, A. Case studies in gaussian process modelling of computer codes, Reliability Engineering and System Safety, 2006, 91, (10-11), pp 1301–1309.Google Scholar
10. Kennedy, M.C. and O’hagen, A. Bayesian calibration of computer models, J Royal Statistical Society: Series B Statistical Methodology, 2001, 63, (3), pp 425–464.Google Scholar
11. Sacks, J., Welch, W.J., Mitchell, T.J. and wynn, H.P. Design and analysis of computer experiments, Statistical Science, 1989, 4, (4), pp 409–435.Google Scholar
12. Worden, K. and Becker, W. On the identifcation of hysteretic systems, Part II: Bayesian sensitivity analysis and parameter confdence, Mechanical Systems and Signal Processing, 2012, 29, pp 213–227.Google Scholar
13. Becker, W., Oakley, J., Surace, C., Gili, P., Rowson, J. and Worden, K. Bayesian sensitivity analysis of a nonlinear finite element model, Mechanical Systems and Signal Processing, 2012, 32, pp 18–31.Google Scholar
14. Sartor, P., worden, K., Schmidt, R.K. and Bond, D. Bayesian Sensitivity Analysis of Flight, Parameters in the Development of a Hard Landing Indication System, In Preparation, 2012.Google Scholar
15. Kennedy, M.C. GEM-SA Homepage, http://ctcd.group.shef.ac.uk/gem.html.Google Scholar
16. Petropoulos, G., wooster, M., Carlson, T., Kennedy, M.C. and Scholze, M. A Global, Bayesian sensitivity analysis of the 1d simsphere soil-vegitation-atmospheric transfer, SVAT. Model using Gaussian model emulation, Ecological Modelling, 2009, 220, pp 2427–2440.Google Scholar
17. Bayarri, M., Berger, J., Higdon, D., Kennedy, M., Kottas, A., Paulo, R., Sacks, J., Cafeo, J., Cavendish, J., Lin, C. and Tu, J. A Framework for Validation of Computer Models, Tech rep, National Institute of Statistical Sciences, General Motors, October 2002 Google Scholar
18. Becker, W.J. Rowson, J., Oakley, J., Yoxall, A., Manson, G. and Worden, K. Bayesian sensitivity analysis of a model of the aortic valve, J Biomechanics, May 2011, 44, pp 1499–1506.Google Scholar