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Structural optimization based on internal energy distribution
Linköping University, Department of Management and Engineering, Solid Mechanics. Linköping University, The Institute of Technology.
Linköping University, Department of Management and Engineering, Solid Mechanics. Linköping University, The Institute of Technology.
2011 (English)Manuscript (preprint) (Other academic)
Abstract [en]

Structural optimization is a valuable tool to improve the performance of products, but it is in general expensive to perform due to the required extensive number of function evaluations. Therefore, an approximate method based on the internal energy distribution, which only requires a small number of function evaluations, is presented here. By this method, structural optimization can be enabled already in the initial steps in the design of new products when fast, but not necessarily precise, results are often desired. However, the accuracy of the approximate solution depends on the structural behaviour. The internal energy based optimization method is here validated for three structures, but it is believed to be applicable to any structure subjected to a single load where the functions considered are related to the displacement of the loaded area and/or the material thicknesses of the structural parts.

Place, publisher, year, edition, pages
2011.
Keyword [en]
Structural Optimization, Internal Energy Gradients, Internal Energy Based Method, Internal Energy Distribution, Product Family Optimization
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:liu:diva-70789OAI: oai:DiVA.org:liu-70789DiVA: diva2:441708
Available from: 2011-09-19 Created: 2011-09-19 Last updated: 2011-09-19Bibliographically approved
In thesis
1. Optimization and Robustness of Structural Product Families
Open this publication in new window or tab >>Optimization and Robustness of Structural Product Families
2011 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

This thesis concerns structural optimization and robustness evaluations, and new methods are presented that considerably reduce the computational cost of these evaluations.

Optimization is an effective tool in the design process and the interest from industry of its usage is quickly increasing. However, the usage would probably have grown faster if the required number of computationally costly finite element analyses could be reduced. Especially in the case of product family optimization, the problem size can easily get too large to be solved within a reasonable time. This is sometimes also true for robustness evaluations. To enable the usage of optimization and robustness evaluations also for large scale industrial problems, two new methods are here presented, which require a considerably smaller number of finite element analyses.

The first method concerns structural optimization of product families subjected to multiple crash load cases. Here, the number of required finite element analyses are considerably reduced by only considering the critical constraint in each iteration step.

The second method is an approach to approximate the variable sensibility based on the distribution of internal energy in a structure. The method can be used to evaluate the relative robustness of different design proposals or for structural optimization. Since the method is independent of the number of parameters and design variables the computational cost of such evaluations is drastically reduced for computationally large problems.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2011. 50 p.
Series
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 1397
National Category
Engineering and Technology
Identifiers
urn:nbn:se:liu:diva-70372 (URN)978-91-7393-072-7 (ISBN)
Public defence
2011-10-18, Planck, Fysikhuset, Campus Valla, Linköpings universitet, Linköping, 10:15 (English)
Opponent
Supervisors
Available from: 2011-09-05 Created: 2011-09-05 Last updated: 2011-09-19Bibliographically approved

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Öman, MichaelNilsson, Larsgunnar

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