Clustering of Parameter Sensitivities: Examples from a Helicopter Airframe Model Updating Exercise
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...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2009-01-01
|
| Series: | Shock and Vibration |
| Online Access: | http://dx.doi.org/10.3233/SAV-2009-0455 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832554664298545152 |
|---|---|
| author | H. Shahverdi C. Mares W. Wang J.E. Mottershead |
| author_facet | H. Shahverdi C. Mares W. Wang J.E. Mottershead |
| author_sort | H. Shahverdi |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-cade8adcc8744091a97a1abc3d267c0a |
| institution | Kabale University |
| issn | 1070-9622 1875-9203 |
| language | English |
| publishDate | 2009-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Shock and Vibration |
| spelling | doaj-art-cade8adcc8744091a97a1abc3d267c0a2025-02-03T05:50:57ZengWileyShock and Vibration1070-96221875-92032009-01-01161758710.3233/SAV-2009-0455Clustering of Parameter Sensitivities: Examples from a Helicopter Airframe Model Updating ExerciseH. Shahverdi0C. Mares1W. Wang2J.E. Mottershead3Mechanical Engineering Division, Department of Engineering, University of Liverpool, Liverpool, L69 3GH, UKSchool of Engineering and Design, Brunel University, Uxbridge, Middlesex, UB8 3PH, UKMechanical Engineering Division, Department of Engineering, University of Liverpool, Liverpool, L69 3GH, UKMechanical Engineering Division, Department of Engineering, University of Liverpool, Liverpool, L69 3GH, UKThe 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.http://dx.doi.org/10.3233/SAV-2009-0455 |
| spellingShingle | H. Shahverdi C. Mares W. Wang J.E. Mottershead Clustering of Parameter Sensitivities: Examples from a Helicopter Airframe Model Updating Exercise Shock and Vibration |
| title | Clustering of Parameter Sensitivities: Examples from a Helicopter Airframe Model Updating Exercise |
| title_full | Clustering of Parameter Sensitivities: Examples from a Helicopter Airframe Model Updating Exercise |
| title_fullStr | Clustering of Parameter Sensitivities: Examples from a Helicopter Airframe Model Updating Exercise |
| title_full_unstemmed | Clustering of Parameter Sensitivities: Examples from a Helicopter Airframe Model Updating Exercise |
| title_short | Clustering of Parameter Sensitivities: Examples from a Helicopter Airframe Model Updating Exercise |
| title_sort | clustering of parameter sensitivities examples from a helicopter airframe model updating exercise |
| url | http://dx.doi.org/10.3233/SAV-2009-0455 |
| work_keys_str_mv | AT hshahverdi clusteringofparametersensitivitiesexamplesfromahelicopterairframemodelupdatingexercise AT cmares clusteringofparametersensitivitiesexamplesfromahelicopterairframemodelupdatingexercise AT wwang clusteringofparametersensitivitiesexamplesfromahelicopterairframemodelupdatingexercise AT jemottershead clusteringofparametersensitivitiesexamplesfromahelicopterairframemodelupdatingexercise |