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The Behaviour of Evolutionary Algorithms for the CFD-Driven Design Optimisation of Aerofoils / HANNAH DITCHBURN

Swansea University Author: HANNAH DITCHBURN

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Abstract

This thesis utilises aerodynamic shape optimisation software AerOpt and FLITE2D, to explore the behaviour of three Evolutionary Algorithms, Differential Evolution (DE), Modified Cuckoo Search (MCS), and Particle Swarm Optimisation (PSO), to optimise a 2D nonsymmetric aerofoil, providing an evaluatio...

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Published: Swansea, Wales, UK 2023
Institution: Swansea University
Degree level: Master of Research
Degree name: MSc by Research
Supervisor: Evans, Ben J. and Walton, S.
URI: https://cronfa.swan.ac.uk/Record/cronfa63505
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fullrecord <?xml version="1.0" encoding="utf-8"?><rfc1807 xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:xsd="http://www.w3.org/2001/XMLSchema"><bib-version>v2</bib-version><id>63505</id><entry>2023-05-19</entry><title>The Behaviour of Evolutionary Algorithms for the CFD-Driven Design Optimisation of Aerofoils</title><swanseaauthors><author><sid>20a53704cc83c616702a9891619e3e92</sid><firstname>HANNAH</firstname><surname>DITCHBURN</surname><name>HANNAH DITCHBURN</name><active>true</active><ethesisStudent>false</ethesisStudent></author></swanseaauthors><date>2023-05-19</date><abstract>This thesis utilises aerodynamic shape optimisation software AerOpt and FLITE2D, to explore the behaviour of three Evolutionary Algorithms, Differential Evolution (DE), Modified Cuckoo Search (MCS), and Particle Swarm Optimisation (PSO), to optimise a 2D nonsymmetric aerofoil, providing an evaluation of their aerodynamic optimising capabilities.The aerofoil used in test cases is the NACA21120, where a variation of control node approaches are utilised to alter the aerofoil’s geometry. In the first set of test cases, a control node is placed on the upper surface to allow the thickness to be altered, and in the second set of cases, six control nodes are arranged along the boundary of the aerofoil, to examine the overall shape change.A mesh convergence study helped to determine the best mesh settings for the given problem. Each algorithm is tested in a subsonic, transonic, and supersonic flow regime to ensure the test cases fulfil the CFD aspect of the research. All flow regimes were treated as viscous with the relevant Reynolds number applied. To provide an analysis on how tuning the input parameters affects the algorithm’s behaviour, the number of agents were inputted were varied from 10 to 50 to 99. The generations number was set to 99, and the fitness objective was to optimise for the lift-drag ratio (L/D), throughout all optimisations.The first set of results (one control node) found that fitness improvements were largest in the transonic cases, increasing the L/D by an average percentage of 213%. The aerofoil’s L/D at Mach 0.5 was improved by an average of 80%, and Mach 1.5 by 33%. Each algorithm showed a similar trend in which the control node was positioned at the final generation in the design space, this varied depending on the Mach number being optimised for, either resulting in an increase or decrease in the aerofoil thickness. Varying the number of agents inputted, had a more significant effect on MCS, whereas DE and PSO showed more consistent results regardless of the number of inputted agents. Generally, PSO displayed fastest convergence of all the agents, shortly followed by DE, followed by MCS.The second set of results (six control nodes) were optimised for identical input parameters but for simplicity, at a single flow regime, Mach 1.5. Differing from the first set of results showing similar control node placement within the design space, the second set of results showed the algorithm’s position some of the control nodes in different locations within the design space. Despite the similar fitness improvement values seen between DE and PSO, the final geometries were observed to be somewhat varied, where DE reduced the thickness of the trailing edge, but PSO increased it. MCS displayed similar geometry change to PSO but with more conservative control node movement.</abstract><type>E-Thesis</type><journal/><volume/><journalNumber/><paginationStart/><paginationEnd/><publisher/><placeOfPublication>Swansea, Wales, UK</placeOfPublication><isbnPrint/><isbnElectronic/><issnPrint/><issnElectronic/><keywords>Aerofoil, Agent, Computational Fluid Dynamics, Control Node, Evolutionary Algorithm, Fitness, Generation, High Performance Computing</keywords><publishedDay>4</publishedDay><publishedMonth>5</publishedMonth><publishedYear>2023</publishedYear><publishedDate>2023-05-04</publishedDate><doi/><url/><notes/><college>COLLEGE NANME</college><CollegeCode>COLLEGE CODE</CollegeCode><institution>Swansea University</institution><supervisor>Evans, Ben J. and Walton, S.</supervisor><degreelevel>Master of Research</degreelevel><degreename>MSc by Research</degreename><apcterm/><funders/><projectreference/><lastEdited>2023-10-27T15:50:24.3180132</lastEdited><Created>2023-05-19T09:16:01.2321976</Created><path><level id="1">Faculty of Science and Engineering</level><level id="2">School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Aerospace Engineering</level></path><authors><author><firstname>HANNAH</firstname><surname>DITCHBURN</surname><order>1</order></author></authors><documents><document><filename>63505__27533__2572b223cab8432e9b1bcf3f7676c143.pdf</filename><originalFilename>2023_Ditchburn_HR.final.63505.pdf</originalFilename><uploaded>2023-05-19T09:18:44.0594454</uploaded><type>Output</type><contentLength>4508967</contentLength><contentType>application/pdf</contentType><version>E-Thesis – open access</version><cronfaStatus>true</cronfaStatus><documentNotes>Copyright: The Author, Hannah R. 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spelling v2 63505 2023-05-19 The Behaviour of Evolutionary Algorithms for the CFD-Driven Design Optimisation of Aerofoils 20a53704cc83c616702a9891619e3e92 HANNAH DITCHBURN HANNAH DITCHBURN true false 2023-05-19 This thesis utilises aerodynamic shape optimisation software AerOpt and FLITE2D, to explore the behaviour of three Evolutionary Algorithms, Differential Evolution (DE), Modified Cuckoo Search (MCS), and Particle Swarm Optimisation (PSO), to optimise a 2D nonsymmetric aerofoil, providing an evaluation of their aerodynamic optimising capabilities.The aerofoil used in test cases is the NACA21120, where a variation of control node approaches are utilised to alter the aerofoil’s geometry. In the first set of test cases, a control node is placed on the upper surface to allow the thickness to be altered, and in the second set of cases, six control nodes are arranged along the boundary of the aerofoil, to examine the overall shape change.A mesh convergence study helped to determine the best mesh settings for the given problem. Each algorithm is tested in a subsonic, transonic, and supersonic flow regime to ensure the test cases fulfil the CFD aspect of the research. All flow regimes were treated as viscous with the relevant Reynolds number applied. To provide an analysis on how tuning the input parameters affects the algorithm’s behaviour, the number of agents were inputted were varied from 10 to 50 to 99. The generations number was set to 99, and the fitness objective was to optimise for the lift-drag ratio (L/D), throughout all optimisations.The first set of results (one control node) found that fitness improvements were largest in the transonic cases, increasing the L/D by an average percentage of 213%. The aerofoil’s L/D at Mach 0.5 was improved by an average of 80%, and Mach 1.5 by 33%. Each algorithm showed a similar trend in which the control node was positioned at the final generation in the design space, this varied depending on the Mach number being optimised for, either resulting in an increase or decrease in the aerofoil thickness. Varying the number of agents inputted, had a more significant effect on MCS, whereas DE and PSO showed more consistent results regardless of the number of inputted agents. Generally, PSO displayed fastest convergence of all the agents, shortly followed by DE, followed by MCS.The second set of results (six control nodes) were optimised for identical input parameters but for simplicity, at a single flow regime, Mach 1.5. Differing from the first set of results showing similar control node placement within the design space, the second set of results showed the algorithm’s position some of the control nodes in different locations within the design space. Despite the similar fitness improvement values seen between DE and PSO, the final geometries were observed to be somewhat varied, where DE reduced the thickness of the trailing edge, but PSO increased it. MCS displayed similar geometry change to PSO but with more conservative control node movement. E-Thesis Swansea, Wales, UK Aerofoil, Agent, Computational Fluid Dynamics, Control Node, Evolutionary Algorithm, Fitness, Generation, High Performance Computing 4 5 2023 2023-05-04 COLLEGE NANME COLLEGE CODE Swansea University Evans, Ben J. and Walton, S. Master of Research MSc by Research 2023-10-27T15:50:24.3180132 2023-05-19T09:16:01.2321976 Faculty of Science and Engineering School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Aerospace Engineering HANNAH DITCHBURN 1 63505__27533__2572b223cab8432e9b1bcf3f7676c143.pdf 2023_Ditchburn_HR.final.63505.pdf 2023-05-19T09:18:44.0594454 Output 4508967 application/pdf E-Thesis – open access true Copyright: The Author, Hannah R. Ditchburn, 2023. true eng
title The Behaviour of Evolutionary Algorithms for the CFD-Driven Design Optimisation of Aerofoils
spellingShingle The Behaviour of Evolutionary Algorithms for the CFD-Driven Design Optimisation of Aerofoils
HANNAH DITCHBURN
title_short The Behaviour of Evolutionary Algorithms for the CFD-Driven Design Optimisation of Aerofoils
title_full The Behaviour of Evolutionary Algorithms for the CFD-Driven Design Optimisation of Aerofoils
title_fullStr The Behaviour of Evolutionary Algorithms for the CFD-Driven Design Optimisation of Aerofoils
title_full_unstemmed The Behaviour of Evolutionary Algorithms for the CFD-Driven Design Optimisation of Aerofoils
title_sort The Behaviour of Evolutionary Algorithms for the CFD-Driven Design Optimisation of Aerofoils
author_id_str_mv 20a53704cc83c616702a9891619e3e92
author_id_fullname_str_mv 20a53704cc83c616702a9891619e3e92_***_HANNAH DITCHBURN
author HANNAH DITCHBURN
author2 HANNAH DITCHBURN
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publishDate 2023
institution Swansea University
college_str Faculty of Science and Engineering
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hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Aerospace Engineering{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Aerospace Engineering
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description This thesis utilises aerodynamic shape optimisation software AerOpt and FLITE2D, to explore the behaviour of three Evolutionary Algorithms, Differential Evolution (DE), Modified Cuckoo Search (MCS), and Particle Swarm Optimisation (PSO), to optimise a 2D nonsymmetric aerofoil, providing an evaluation of their aerodynamic optimising capabilities.The aerofoil used in test cases is the NACA21120, where a variation of control node approaches are utilised to alter the aerofoil’s geometry. In the first set of test cases, a control node is placed on the upper surface to allow the thickness to be altered, and in the second set of cases, six control nodes are arranged along the boundary of the aerofoil, to examine the overall shape change.A mesh convergence study helped to determine the best mesh settings for the given problem. Each algorithm is tested in a subsonic, transonic, and supersonic flow regime to ensure the test cases fulfil the CFD aspect of the research. All flow regimes were treated as viscous with the relevant Reynolds number applied. To provide an analysis on how tuning the input parameters affects the algorithm’s behaviour, the number of agents were inputted were varied from 10 to 50 to 99. The generations number was set to 99, and the fitness objective was to optimise for the lift-drag ratio (L/D), throughout all optimisations.The first set of results (one control node) found that fitness improvements were largest in the transonic cases, increasing the L/D by an average percentage of 213%. The aerofoil’s L/D at Mach 0.5 was improved by an average of 80%, and Mach 1.5 by 33%. Each algorithm showed a similar trend in which the control node was positioned at the final generation in the design space, this varied depending on the Mach number being optimised for, either resulting in an increase or decrease in the aerofoil thickness. Varying the number of agents inputted, had a more significant effect on MCS, whereas DE and PSO showed more consistent results regardless of the number of inputted agents. Generally, PSO displayed fastest convergence of all the agents, shortly followed by DE, followed by MCS.The second set of results (six control nodes) were optimised for identical input parameters but for simplicity, at a single flow regime, Mach 1.5. Differing from the first set of results showing similar control node placement within the design space, the second set of results showed the algorithm’s position some of the control nodes in different locations within the design space. Despite the similar fitness improvement values seen between DE and PSO, the final geometries were observed to be somewhat varied, where DE reduced the thickness of the trailing edge, but PSO increased it. MCS displayed similar geometry change to PSO but with more conservative control node movement.
published_date 2023-05-04T15:50:22Z
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