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Topology optimization of conductive heat transfer problems using parametric L-systems

Topology optimization of conductive heat transfer problems using parametric L-systems
Topology optimization of conductive heat transfer problems using parametric L-systems
Generative encodings have the potential of improving the performance of evolutionary algorithms. In this work we apply parametric L-systems, which can be described as developmental recipes, to evolutionary topology optimization of widely studied two-dimensional steady-state heat conduction problems. We translate L-systems into geometries using the turtle interpretation, and evaluate their objective functions, i.e. average and maximum temperatures, using the Finite Volume Method (FVM). The method requires two orders of magnitude fewer function evaluations, and yields better results in 10 out of 12 tested optimization problems (the result is statistically significant), than a reference method using direct encoding. Further, our results indicate that the method yields designs with lower average temperatures than the widely used and well established SIMP (Solid Isotropic Material with Penalization) method in optimization problems where the product of volume fraction and the ratio of high and low conductive material is less or equal to 1. Finally, we demonstrate the ability of the methodology to tackle multi-objective optimization problems with relevant temperature and manufacturing related objectives.
Topology Optimization, Generative Encoding, L-Systems, Heat Transfer, Conduction
1615-147X
1-18
Ikonen, Teemu J.
d94a7607-5b27-4f95-8030-07e89f232231
Marck, Gilles
26cab4ea-41ed-4ecb-aa57-36931b608a99
Sobester, Andras
096857b0-cad6-45ae-9ae6-e66b8cc5d81b
Keane, Andy J.
26d7fa33-5415-4910-89d8-fb3620413def
Ikonen, Teemu J.
d94a7607-5b27-4f95-8030-07e89f232231
Marck, Gilles
26cab4ea-41ed-4ecb-aa57-36931b608a99
Sobester, Andras
096857b0-cad6-45ae-9ae6-e66b8cc5d81b
Keane, Andy J.
26d7fa33-5415-4910-89d8-fb3620413def

Ikonen, Teemu J., Marck, Gilles, Sobester, Andras and Keane, Andy J. (2018) Topology optimization of conductive heat transfer problems using parametric L-systems. Structural and Multidisciplinary Optimization, 1-18. (doi:10.1007/s00158-018-2055-7).

Record type: Article

Abstract

Generative encodings have the potential of improving the performance of evolutionary algorithms. In this work we apply parametric L-systems, which can be described as developmental recipes, to evolutionary topology optimization of widely studied two-dimensional steady-state heat conduction problems. We translate L-systems into geometries using the turtle interpretation, and evaluate their objective functions, i.e. average and maximum temperatures, using the Finite Volume Method (FVM). The method requires two orders of magnitude fewer function evaluations, and yields better results in 10 out of 12 tested optimization problems (the result is statistically significant), than a reference method using direct encoding. Further, our results indicate that the method yields designs with lower average temperatures than the widely used and well established SIMP (Solid Isotropic Material with Penalization) method in optimization problems where the product of volume fraction and the ratio of high and low conductive material is less or equal to 1. Finally, we demonstrate the ability of the methodology to tackle multi-objective optimization problems with relevant temperature and manufacturing related objectives.

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More information

Accepted/In Press date: 24 July 2018
e-pub ahead of print date: 21 September 2018
Published date: November 2018
Keywords: Topology Optimization, Generative Encoding, L-Systems, Heat Transfer, Conduction

Identifiers

Local EPrints ID: 422930
URI: http://eprints.soton.ac.uk/id/eprint/422930
ISSN: 1615-147X
PURE UUID: 899cf5fe-6756-4d33-b003-3f9d8af2bc8e
ORCID for Teemu J. 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: 08 Aug 2018 16:30
Last modified: 07 Oct 2020 05:03

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