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Engineering Design Optimisation Using Computational Fluid Dynamics and Human-AI Collaboration / JAKUB VINCALEK

Swansea University Author: JAKUB VINCALEK

DOI (Published version): 10.23889/SUThesis.67069

Abstract

The primary contributions of this thesis are a comparison of a ducted winglet to two other geometries and a user study which investigated the relationship between engineers and an optimisation algorithm. With an ever increasing emphasis on sustainable and renewable energy production, new technology...

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Published: Swansea University, Wales, UK 2024
Institution: Swansea University
Degree level: Doctoral
Degree name: Ph.D
Supervisor: Walton, S. and Evans, B.
URI: https://cronfa.swan.ac.uk/Record/cronfa67069
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first_indexed 2024-07-11T11:27:28Z
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spelling v2 67069 2024-07-11 Engineering Design Optimisation Using Computational Fluid Dynamics and Human-AI Collaboration 48423aa25d77fa20b4a993d476f8ffdb JAKUB VINCALEK JAKUB VINCALEK true false 2024-07-11 The primary contributions of this thesis are a comparison of a ducted winglet to two other geometries and a user study which investigated the relationship between engineers and an optimisation algorithm. With an ever increasing emphasis on sustainable and renewable energy production, new technology can increase the efficiency of existing infrastructure such as wind turbines. Aerodynamic devices known as winglets can be fitted to the end of wind turbine blades to increase efficiency and reduce negative downstream effects. A patented winglet introduces a duct that channels freestream air from the bottom of the winglet to the top. The effectiveness of this design is simulated using computational fluid dynamics and compared to two alternative designs across a range of angles of attack. The patented winglet had lower induced drag across a range of angles of attack. Following the comparison, optimisation methods including evolutionary algorithms are employed to further increase the efficiency of the winglet. These algorithms can be used when engineers would otherwise rely on intuition, preconceptions, and theory to try and find optimal designs. The manner in which engineers utilise and engage with an evolutionary algorithm known as MAP-Elites is evaluated through a user study. 12 participants were given 20 minutes each to design a car that could travel over an inclined course with obstacles as far as possible. Their behaviour was compared to survey answers they provided before and after designing cars. Participants who engaged with the MAP-Elites algorithm outperformed baseline designs created by the computer and participants who did not engage with the algorithm. Participants were more likely to use the MAP-Elites algorithm even if they were unaware they were using it or if they had stated that they did not trust optimisation algorithms. Shortening the optimisation cycle is explored during the optimisation of the ducted winglet. The effects of increasing uncertainty in the results are explored through two studies on the robustness of evolutionary algorithms and a study on 2-dimensional aerofoils. The studies show that the optimisation cycle can be reduced to a certain extent while maintaining the original ranking of the designs. E-Thesis Swansea University, Wales, UK Wind turbine, Optimization, HCI, CFD, AI, Aerodynamics, Evolutionary algorithms 14 6 2024 2024-06-14 10.23889/SUThesis.67069 A selection of content is redacted or is partially redacted from this thesis to protect sensitive and personal information. COLLEGE NANME COLLEGE CODE Swansea University Walton, S. and Evans, B. Doctoral Ph.D EPSRC EPSRC 2024-07-11T12:42:11.8343325 2024-07-11T12:18:40.5936490 Faculty of Science and Engineering School of Mathematics and Computer Science - Computer Science JAKUB VINCALEK 1 67069__30880__a6e5aaca917e4938b3be705d72ef29e5.pdf 2024_Vincalek_J.final.67069.pdf 2024-07-11T12:26:24.1042843 Output 83816024 application/pdf E-Thesis – open access true Copyright: The Author, Jakub Vincalek, 2024 true eng
title Engineering Design Optimisation Using Computational Fluid Dynamics and Human-AI Collaboration
spellingShingle Engineering Design Optimisation Using Computational Fluid Dynamics and Human-AI Collaboration
JAKUB VINCALEK
title_short Engineering Design Optimisation Using Computational Fluid Dynamics and Human-AI Collaboration
title_full Engineering Design Optimisation Using Computational Fluid Dynamics and Human-AI Collaboration
title_fullStr Engineering Design Optimisation Using Computational Fluid Dynamics and Human-AI Collaboration
title_full_unstemmed Engineering Design Optimisation Using Computational Fluid Dynamics and Human-AI Collaboration
title_sort Engineering Design Optimisation Using Computational Fluid Dynamics and Human-AI Collaboration
author_id_str_mv 48423aa25d77fa20b4a993d476f8ffdb
author_id_fullname_str_mv 48423aa25d77fa20b4a993d476f8ffdb_***_JAKUB VINCALEK
author JAKUB VINCALEK
author2 JAKUB VINCALEK
format E-Thesis
publishDate 2024
institution Swansea University
doi_str_mv 10.23889/SUThesis.67069
college_str Faculty of Science and Engineering
hierarchytype
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 Mathematics and Computer Science - Computer Science{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Mathematics and Computer Science - Computer Science
document_store_str 1
active_str 0
description The primary contributions of this thesis are a comparison of a ducted winglet to two other geometries and a user study which investigated the relationship between engineers and an optimisation algorithm. With an ever increasing emphasis on sustainable and renewable energy production, new technology can increase the efficiency of existing infrastructure such as wind turbines. Aerodynamic devices known as winglets can be fitted to the end of wind turbine blades to increase efficiency and reduce negative downstream effects. A patented winglet introduces a duct that channels freestream air from the bottom of the winglet to the top. The effectiveness of this design is simulated using computational fluid dynamics and compared to two alternative designs across a range of angles of attack. The patented winglet had lower induced drag across a range of angles of attack. Following the comparison, optimisation methods including evolutionary algorithms are employed to further increase the efficiency of the winglet. These algorithms can be used when engineers would otherwise rely on intuition, preconceptions, and theory to try and find optimal designs. The manner in which engineers utilise and engage with an evolutionary algorithm known as MAP-Elites is evaluated through a user study. 12 participants were given 20 minutes each to design a car that could travel over an inclined course with obstacles as far as possible. Their behaviour was compared to survey answers they provided before and after designing cars. Participants who engaged with the MAP-Elites algorithm outperformed baseline designs created by the computer and participants who did not engage with the algorithm. Participants were more likely to use the MAP-Elites algorithm even if they were unaware they were using it or if they had stated that they did not trust optimisation algorithms. Shortening the optimisation cycle is explored during the optimisation of the ducted winglet. The effects of increasing uncertainty in the results are explored through two studies on the robustness of evolutionary algorithms and a study on 2-dimensional aerofoils. The studies show that the optimisation cycle can be reduced to a certain extent while maintaining the original ranking of the designs.
published_date 2024-06-14T12:42:11Z
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score 11.016079

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