Abstract
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.
Highlights
The choice of parameters is one of the most important steps in updating a finite element model [1,2] and it is worthwhile at the beginning to establish a few guiding principles. (i) The amount of information that can be obtained is limited and taking more measurements in the same frequency range won’t necessarily result in more information
Often the resulting equations are ill-conditioned and it is necessary to apply additional information in the form of a side constraint by regularisation [3] – the reader is referred to the recent paper of Titurus and Friswell [4] for a qualitative treatment of Tikhonov regularisation in finite element model updating. (iii) The parameters should be justified by physical understanding of the structure under test and the test set-up
The chosen parameters should have a physical meaning directly, but this is not always possible in practice. Equivalent models and their parameters often lead to improved models when ‘physical’ parameters cannot be found. (iv) The data should be sensitive to small changes in the parameters
Summary
The choice of parameters is one of the most important steps in updating a finite element model [1,2] and it is worthwhile at the beginning to establish a few guiding principles. (i) The amount of information that can be obtained is limited and taking more measurements in the same frequency range won’t necessarily result in more information. The choice of parameters is one of the most important steps in updating a finite element model [1,2] and it is worthwhile at the beginning to establish a few guiding principles. Often the resulting equations are ill-conditioned and it is necessary to apply additional information in the form of a side constraint by regularisation [3] – the reader is referred to the recent paper of Titurus and Friswell [4] for a qualitative treatment of Tikhonov regularisation in finite element model updating. The chosen parameters should have a physical meaning directly, but this is not always possible in practice. Equivalent models and their parameters often lead to improved models when ‘physical’ parameters cannot be found. Shahverdi et al / Clustering of parameter sensitivities: Examples from a helicopter airframe model updating exercise
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
