Design Optimization Utilizing Dynamic Substructuring and Artificial Intelligence Techniques

Share/Save/Bookmark

Akcay Perdahcioglu, D. and Ellenbroek, M.H.M. and Hoogt, P.J.M. van der and Boer, A. de (2008) Design Optimization Utilizing Dynamic Substructuring and Artificial Intelligence Techniques. In: 26th International Modal Analysis Conference, IMAC-XXVI: A Conference & Exposition on Structural Dynamics, February 4-7, 2008, Orlando, Florida, US.

open access
[img]
Preview
PDF
161kB
Abstract:In mechanical and structural systems, resonance may cause large strains and stresses which can lead to the failure of the system. Since it is often not possible to change the frequency content of the external load excitation, the phenomenon can only be avoided by updating the design of the structure. In this paper, a design optimization strategy based on the integration of the Component Mode Synthesis (CMS) method with numerical optimization techniques is presented. For reasons of numerical efficiency, a Finite Element (FE) model is represented by a surrogate model which is a function of the design parameters. The surrogate model is obtained in four steps: First, the reduced FE models of the components are derived using the CMS method. Then the components are aassembled to obtain the entire structural response. Afterwards the dynamic behavior is determined for a number of design parameter settings. Finally, the surrogate model representing the dynamic behavior is obtained. In this research, the surrogate model is determined using the Backpropagation Neural Networks which is then optimized using the Genetic Algorithms and Sequential Quadratic Programming method. The application of the introduced techniques is demonstrated on a simple test problem.
Item Type:Conference or Workshop Item
Faculty:
Engineering Technology (CTW)
Research Chair:
Research Group:
Link to this item:http://purl.utwente.nl/publications/58899
Export this item as:BibTeX
EndNote
HTML Citation
Reference Manager

 

Repository Staff Only: item control page

Metis ID: 255403