Aerodynamic topology optimisation using an implicit representation and a multiobjective genetic algorithm

Windo Hutabarat; Geoffrey T. Parks; Jerome P. Jarrett; William N. Dawes; P. John Clarkson

doi:10.1007/978-3-540-79305-2_13

Aerodynamic topology optimisation using an implicit representation and a multiobjective genetic algorithm

Windo Hutabarat^*, Geoffrey T. Parks, Jerome P. Jarrett, William N. Dawes, P. John Clarkson

^*Corresponding author for this work

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

7 Citations (Scopus)

Abstract

Given the focus on incremental change in existing empirical aerodynamic design methods, radical, unintuitive, new optimal solutions in previously unexplored regions of design space are very unlikely to be found using them. We present a framework based on an implicit shape representation and a multiobjective evolutionary algorithm that aims to produce a variety of optimal flow topologies for a given requirement, providing designers with insights into possibly radical solutions. A revolutionary integrated flow simulation system developed specifically for design work is used to evaluate candidate designs.

Original language	English
Title of host publication	Artificial Evolution - 8th International Conference Evolution Artificielle, EA 2007, Revised Selected Papers
Pages	148-159
Number of pages	12
DOIs	https://doi.org/10.1007/978-3-540-79305-2_13
Publication status	Published - 2008
Externally published	Yes
Event	8th International Conference on Artificial Evolution, EA 2007 - Tours, France Duration: 29 Oct 2007 → 31 Oct 2007

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	4926 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	8th International Conference on Artificial Evolution, EA 2007
Country/Territory	France
City	Tours
Period	29/10/07 → 31/10/07

Access to Document

10.1007/978-3-540-79305-2_13

Cite this

Hutabarat, W., Parks, G. T., Jarrett, J. P., Dawes, W. N., & Clarkson, P. J. (2008). Aerodynamic topology optimisation using an implicit representation and a multiobjective genetic algorithm. In Artificial Evolution - 8th International Conference Evolution Artificielle, EA 2007, Revised Selected Papers (pp. 148-159). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4926 LNCS). https://doi.org/10.1007/978-3-540-79305-2_13

Hutabarat, Windo ; Parks, Geoffrey T. ; Jarrett, Jerome P. et al. / Aerodynamic topology optimisation using an implicit representation and a multiobjective genetic algorithm. Artificial Evolution - 8th International Conference Evolution Artificielle, EA 2007, Revised Selected Papers. 2008. pp. 148-159 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "Given the focus on incremental change in existing empirical aerodynamic design methods, radical, unintuitive, new optimal solutions in previously unexplored regions of design space are very unlikely to be found using them. We present a framework based on an implicit shape representation and a multiobjective evolutionary algorithm that aims to produce a variety of optimal flow topologies for a given requirement, providing designers with insights into possibly radical solutions. A revolutionary integrated flow simulation system developed specifically for design work is used to evaluate candidate designs.",

author = "Windo Hutabarat and Parks, {Geoffrey T.} and Jarrett, {Jerome P.} and Dawes, {William N.} and Clarkson, {P. John}",

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Hutabarat, W, Parks, GT, Jarrett, JP, Dawes, WN & Clarkson, PJ 2008, Aerodynamic topology optimisation using an implicit representation and a multiobjective genetic algorithm. in Artificial Evolution - 8th International Conference Evolution Artificielle, EA 2007, Revised Selected Papers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 4926 LNCS, pp. 148-159, 8th International Conference on Artificial Evolution, EA 2007, Tours, France, 29/10/07. https://doi.org/10.1007/978-3-540-79305-2_13

Aerodynamic topology optimisation using an implicit representation and a multiobjective genetic algorithm. / Hutabarat, Windo; Parks, Geoffrey T.; Jarrett, Jerome P. et al.
Artificial Evolution - 8th International Conference Evolution Artificielle, EA 2007, Revised Selected Papers. 2008. p. 148-159 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4926 LNCS).

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

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AB - Given the focus on incremental change in existing empirical aerodynamic design methods, radical, unintuitive, new optimal solutions in previously unexplored regions of design space are very unlikely to be found using them. We present a framework based on an implicit shape representation and a multiobjective evolutionary algorithm that aims to produce a variety of optimal flow topologies for a given requirement, providing designers with insights into possibly radical solutions. A revolutionary integrated flow simulation system developed specifically for design work is used to evaluate candidate designs.

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Hutabarat W, Parks GT, Jarrett JP, Dawes WN, Clarkson PJ. Aerodynamic topology optimisation using an implicit representation and a multiobjective genetic algorithm. In Artificial Evolution - 8th International Conference Evolution Artificielle, EA 2007, Revised Selected Papers. 2008. p. 148-159. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-540-79305-2_13

Aerodynamic topology optimisation using an implicit representation and a multiobjective genetic algorithm

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