Identification of optimal topologies for crashworthiness with the evolutionary level set method

Structural topology optimisation (TO) methods are well established in many engineering disciplines. However, for highly non-linear problems, including crashworthiness, methods are less developed and still subject to active research. In particular, due to the simplifications made in the state-of-the-...

Full description

Saved in:
Bibliographic Details
Published in:International journal of crashworthiness 2018-07, Vol.23 (4), p.395-416
Main Authors: Bujny, Mariusz, Aulig, Nikola, Olhofer, Markus, Duddeck, Fabian
Format: Article
Language:English
Subjects:
Crashworthiness
Evolutionary algorithms
Heuristic methods
Impact strength
level set method
Methods
optimal structures
Topology
topology optimisation
Topology optimization
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Structural topology optimisation (TO) methods are well established in many engineering disciplines. However, for highly non-linear problems, including crashworthiness, methods are less developed and still subject to active research. In particular, due to the simplifications made in the state-of-the-art methods as well as heuristic character of most of them, the optimality of the obtained structures is arguable. In this paper, a TO approach based on the level set method and evolutionary algorithms is presented. The method is free from the heuristic assumptions and therefore, allows for a direct optimisation of the underlying crash problem. As a result, a reliable information about the optima of crash problems can be obtained. In order to assure the consistency of the results, a comprehensive study of optimisation settings is performed and discussed. Optimisation results for 2D crash test cases are presented and general conclusions concerning the behaviour of the optimal crash structures are derived.
ISSN:1358-8265
1754-2111
DOI:10.1080/13588265.2017.1331493