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Shock and Vibration
Volume 16, Issue 1, Pages 75-87

Clustering of Parameter Sensitivities: Examples from a Helicopter Airframe Model Updating Exercise

H. Shahverdi,1 C. Mares,2 W. Wang,1 and J.E. Mottershead1

1Mechanical Engineering Division, Department of Engineering, University of Liverpool, Liverpool, L69 3GH, UK
2School of Engineering and Design, Brunel University, Uxbridge, Middlesex, UB8 3PH, UK

Received 25 July 2007; Revised 27 January 2008

Copyright © 2009 Hindawi Publishing Corporation. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


The need for high fidelity models in the aerospace industry has become ever more important as increasingly stringent requirements on noise and vibration levels, reliability, maintenance costs etc. come into effect. In this paper, the results of a finite element model updating exercise on a Westland Lynx XZ649 helicopter are presented. For large and complex structures, such as a helicopter airframe, the finite element model represents the main tool for obtaining accurate models which could predict the sensitivities of responses to structural changes and optimisation of the vibration levels. In this study, the eigenvalue sensitivities with respect to Young's modulus and mass density are used in a detailed parameterisation of the structure. A new methodology is developed using an unsupervised learning technique based on similarity clustering of the columns of the sensitivity matrix. An assessment of model updating strategies is given and comparative results for the correction of vibration modes are discussed in detail. The role of the clustering technique in updating large-scale models is emphasised.