State-of-the-art in aerodynamic shape optimisation methods

Skinner, S.N. and Zare-Behtash, H. (2018) State-of-the-art in aerodynamic shape optimisation methods. Applied Soft Computing, 62, pp. 933-962. (doi:10.1016/j.asoc.2017.09.030)

Skinner, S.N. and Zare-Behtash, H. (2018) State-of-the-art in aerodynamic shape optimisation methods. Applied Soft Computing, 62, pp. 933-962. (doi:10.1016/j.asoc.2017.09.030)

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Abstract

Aerodynamic optimisation has become an indispensable component for any aerodynamic design over the past 60 years, with applications to aircraft, cars, trains, bridges, wind turbines, internal pipe flows, and cavities, among others, and is thus relevant in many facets of technology. With advancements in computational power, automated design optimisation procedures have become more competent, however, there is an ambiguity and bias throughout the literature with regards to relative performance of optimisation architectures and employed algorithms. This paper provides a well-balanced critical review of the dominant optimisation approaches that have been integrated with aerodynamic theory for the purpose of shape optimisation. A total of 229 papers, published in more than 120 journals and conference proceedings, have been classified into 6 different optimisation algorithm approaches. The material cited includes some of the most well-established authors and publications in the field of aerodynamic optimisation. This paper aims to eliminate bias toward certain algorithms by analysing the limitations, drawbacks, and the benefits of the most utilised optimisation approaches. This review provides comprehensive but straightforward insight for non-specialists and reference detailing the current state for specialist practitioners.

Item Type:Articles
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Skinner, Mr Shaun and Zare-Behtash, Dr Hossein
Authors: Skinner, S.N., and Zare-Behtash, H.
College/School:College of Science and Engineering > School of Engineering > Aerospace Sciences
Journal Name:Applied Soft Computing
Publisher:Elsevier
ISSN:1568-4946
ISSN (Online):1872-9681
Published Online:29 September 2017
Copyright Holders:Copyright © 2017 The Authors
First Published:First published in Applied Soft Computing 62: 933-962
Publisher Policy:Reproduced under a Creative Commons License

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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
647261National Wind Tunnel FacilityFrank CotonEngineering and Physical Sciences Research Council (EPSRC)EP/L024888/1VPO (ACADEMIC & EDUCATIONAL INNOVATION)