The University of Southampton
University of Southampton Institutional Repository

Evolutionary topology optimization via direct and generative encodings: applications to aerospace and heat transfer engineering

Evolutionary topology optimization via direct and generative encodings: applications to aerospace and heat transfer engineering
Evolutionary topology optimization via direct and generative encodings: applications to aerospace and heat transfer engineering
Evolutionary algorithms are global search methods that are well-suited for ‘black-box’ type objective functions and multi-objective optimization. However, as search methods in topology optimization, they have gained only limited acceptance, mainly due to their poor efficiency; they tend to require more objective function evaluations than gradientbased methods. Motivated by their benefits, the first aim of this work is to improve the performance, i.e. effectiveness and efficiency, of evolutionary topology optimization. We parameterize the design domains using both the ground structure approach (direct encoding) and L-systems-based methods (generative encoding). We investigate the use of two interpretation formalisms of L-systems, i.e. map L-systems and the turtle interpretation. In terms of improving the performance, the main contribution of this work is a statistical analysis of the effects of over 400 genetic control parameter combinations on the performance of the map L-systems-based method, which results we report as a Pareto front in the space of effectiveness and efficiency. The second aim of this work is to identify engineering applications to which L-systems-based methods are particularly suitable. We studied three applications, which are related to aerospace and heat transfer engineering. We found that the method with the turtle interpretation is well-suited to topology optimization of a heat conductor due to its natural tendency to produce bifurcating tree-structures. We show that the method is more effective in 10 out of 12 tested optimization problems and is two orders of magnitude more efficient on all 12 problems than a representative direct encoding method. In addition, our results indicate that the method is more effective than the well-established SIMP method (Solid Isotropic Material with Penalization) in optimization problems where the product of volume fraction and the ratio of high and low conductive material is less or equal to 1.
University of Southampton
Ikonen, Teemu Johannes
d94a7607-5b27-4f95-8030-07e89f232231
Ikonen, Teemu Johannes
d94a7607-5b27-4f95-8030-07e89f232231
Sobester, Andras
096857b0-cad6-45ae-9ae6-e66b8cc5d81b

Ikonen, Teemu Johannes (2018) Evolutionary topology optimization via direct and generative encodings: applications to aerospace and heat transfer engineering. University of Southampton, Doctoral Thesis, 251pp.

Record type: Thesis (Doctoral)

Abstract

Evolutionary algorithms are global search methods that are well-suited for ‘black-box’ type objective functions and multi-objective optimization. However, as search methods in topology optimization, they have gained only limited acceptance, mainly due to their poor efficiency; they tend to require more objective function evaluations than gradientbased methods. Motivated by their benefits, the first aim of this work is to improve the performance, i.e. effectiveness and efficiency, of evolutionary topology optimization. We parameterize the design domains using both the ground structure approach (direct encoding) and L-systems-based methods (generative encoding). We investigate the use of two interpretation formalisms of L-systems, i.e. map L-systems and the turtle interpretation. In terms of improving the performance, the main contribution of this work is a statistical analysis of the effects of over 400 genetic control parameter combinations on the performance of the map L-systems-based method, which results we report as a Pareto front in the space of effectiveness and efficiency. The second aim of this work is to identify engineering applications to which L-systems-based methods are particularly suitable. We studied three applications, which are related to aerospace and heat transfer engineering. We found that the method with the turtle interpretation is well-suited to topology optimization of a heat conductor due to its natural tendency to produce bifurcating tree-structures. We show that the method is more effective in 10 out of 12 tested optimization problems and is two orders of magnitude more efficient on all 12 problems than a representative direct encoding method. In addition, our results indicate that the method is more effective than the well-established SIMP method (Solid Isotropic Material with Penalization) in optimization problems where the product of volume fraction and the ratio of high and low conductive material is less or equal to 1.

Text
Thesis Teemu Ikonen final - Version of Record
Download (52MB)
Text
Thesis_Teemu Ikonen_final - Other
Restricted to Repository staff only

More information

Published date: 21 September 2018

Identifiers

Local EPrints ID: 432260
URI: http://eprints.soton.ac.uk/id/eprint/432260
PURE UUID: f51d158d-ca67-4cd0-be96-8babd26c5c57
ORCID for Teemu Johannes Ikonen: ORCID iD orcid.org/0000-0001-7413-9310
ORCID for Andras Sobester: ORCID iD orcid.org/0000-0002-8997-4375

Catalogue record

Date deposited: 05 Jul 2019 16:30
Last modified: 06 Jul 2019 00:35

Export record

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×