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Simulation driven machine learning methods to optimise design of physical experiments and enhance data analysis for testing of fusion energy heat exchanger components / RHYDIAN LEWIS

Swansea University Author: RHYDIAN LEWIS

DOI (Published version): 10.23889/SUthesis.66041

Abstract

Plasma facing components (PFCs) must be designed to routinely withstand the harsh environment of a fusion device, where temperatures at the core of the plasma exceed 150,000,000 °C. The heat by induction to verify extremes (HIVE) experimental facility was established to replicate the thermal loads a...

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Published: Swansea University, Wales, UK 2024
Institution: Swansea University
Degree level: Doctoral
Degree name: Ph.D
Supervisor: Evans, Ll.
URI: https://cronfa.swan.ac.uk/Record/cronfa66041
first_indexed 2024-04-11T14:07:24Z
last_indexed 2024-11-25T14:17:23Z
id cronfa66041
recordtype RisThesis
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spelling 2024-06-21T11:51:27.0224323 v2 66041 2024-04-11 Simulation driven machine learning methods to optimise design of physical experiments and enhance data analysis for testing of fusion energy heat exchanger components 5136a978884f9d245d56d7ad4b9ff68b RHYDIAN LEWIS RHYDIAN LEWIS true false 2024-04-11 Plasma facing components (PFCs) must be designed to routinely withstand the harsh environment of a fusion device, where temperatures at the core of the plasma exceed 150,000,000 °C. The heat by induction to verify extremes (HIVE) experimental facility was established to replicate the thermal loads a PFC is subjected to during normal operation of a fusion device.To maximise its impact on the design of PFCs, HIVE must deliver smarter testing and improved component insight. Currently, the experimental parameters required to deliver a certain response to the component are decided at the point of testing through a combination of previous experience, intuition, and trial & error, which is both time-consuming and unreliable. To assess a PFC’s suitability, knowledge of its mechanical performance while operating at high temperatures is desirable, however HIVE only records pointwise temperature measurements on the component’s surface using thermocouples. Currently, HIVE has no method of inferring a component’s mechanical response using the temperature measure-ments.Both the challenges of smarter testing and improved component insight can be achieved through the identification of inverse solutions. A popular approach to solving engineering inverse problems is surrogate assisted optimisation, where a machine learning model is trained using finite element (FE) simulation data. Much of the work in literature use single value surrogate models on quite simplistic problems, however HIVE is a real-world, multi-physics problem which requires full field (FF) surrogate models to solve its multitude of inverse problems.The development of a method which can easily construct FE data driven FF surrogates would be invaluable for a variety of tasks in engineering, as well as solving inverse problems. In this work, it demonstrates that it can provide a much more robust and comprehensive method of characterising a PFC’s strengths and limitations, enabling more informed decisions to be made during its design cycle. E-Thesis Swansea University, Wales, UK Fusion Energy, Machine Learning, Surrogate Model, Simulation 26 1 2024 2024-01-26 10.23889/SUthesis.66041 Part of this thesis has been redacted to protect personal information COLLEGE NANME COLLEGE CODE Swansea University Evans, Ll. Doctoral Ph.D EPSRC, RCUK Energy Programme, EUROfusion 2024-06-21T11:51:27.0224323 2024-04-11T14:59:45.3651811 Faculty of Science and Engineering School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Aerospace Engineering RHYDIAN LEWIS 1 66041__30718__c42396ef101e42a4a742490f5aca5432.pdf 2024_Lewis_Rhydian.final.66041.pdf 2024-06-21T11:50:25.5574082 Output 17601144 application/pdf E-Thesis – open access true Copyright: The Author, Rhydian Lewis, 2023 true eng
title Simulation driven machine learning methods to optimise design of physical experiments and enhance data analysis for testing of fusion energy heat exchanger components
spellingShingle Simulation driven machine learning methods to optimise design of physical experiments and enhance data analysis for testing of fusion energy heat exchanger components
RHYDIAN LEWIS
title_short Simulation driven machine learning methods to optimise design of physical experiments and enhance data analysis for testing of fusion energy heat exchanger components
title_full Simulation driven machine learning methods to optimise design of physical experiments and enhance data analysis for testing of fusion energy heat exchanger components
title_fullStr Simulation driven machine learning methods to optimise design of physical experiments and enhance data analysis for testing of fusion energy heat exchanger components
title_full_unstemmed Simulation driven machine learning methods to optimise design of physical experiments and enhance data analysis for testing of fusion energy heat exchanger components
title_sort Simulation driven machine learning methods to optimise design of physical experiments and enhance data analysis for testing of fusion energy heat exchanger components
author_id_str_mv 5136a978884f9d245d56d7ad4b9ff68b
author_id_fullname_str_mv 5136a978884f9d245d56d7ad4b9ff68b_***_RHYDIAN LEWIS
author RHYDIAN LEWIS
author2 RHYDIAN LEWIS
format E-Thesis
publishDate 2024
institution Swansea University
doi_str_mv 10.23889/SUthesis.66041
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 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
document_store_str 1
active_str 0
description Plasma facing components (PFCs) must be designed to routinely withstand the harsh environment of a fusion device, where temperatures at the core of the plasma exceed 150,000,000 °C. The heat by induction to verify extremes (HIVE) experimental facility was established to replicate the thermal loads a PFC is subjected to during normal operation of a fusion device.To maximise its impact on the design of PFCs, HIVE must deliver smarter testing and improved component insight. Currently, the experimental parameters required to deliver a certain response to the component are decided at the point of testing through a combination of previous experience, intuition, and trial & error, which is both time-consuming and unreliable. To assess a PFC’s suitability, knowledge of its mechanical performance while operating at high temperatures is desirable, however HIVE only records pointwise temperature measurements on the component’s surface using thermocouples. Currently, HIVE has no method of inferring a component’s mechanical response using the temperature measure-ments.Both the challenges of smarter testing and improved component insight can be achieved through the identification of inverse solutions. A popular approach to solving engineering inverse problems is surrogate assisted optimisation, where a machine learning model is trained using finite element (FE) simulation data. Much of the work in literature use single value surrogate models on quite simplistic problems, however HIVE is a real-world, multi-physics problem which requires full field (FF) surrogate models to solve its multitude of inverse problems.The development of a method which can easily construct FE data driven FF surrogates would be invaluable for a variety of tasks in engineering, as well as solving inverse problems. In this work, it demonstrates that it can provide a much more robust and comprehensive method of characterising a PFC’s strengths and limitations, enabling more informed decisions to be made during its design cycle.
published_date 2024-01-26T05:33:56Z
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score 11.04748

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