A digital twin of fusion energy components with physics-informed neural networks

SHARMA, PRAKHAR

doi:10.23889/SUThesis.68811

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A digital twin of fusion energy components with physics-informed neural networks / PRAKHAR SHARMA

Swansea University Author: PRAKHAR SHARMA

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Copyright: The Author, Prakhar Sharma, 2024 Distributed under the terms of a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0).
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DOI (Published version): 10.23889/SUThesis.68811

Abstract

A fusion reactor presents a highly challenging operational environment, with engineering components exposed to extreme conditions. Temperatures range from 100 million °C at the centre of the plasma to -269 °C in the cryopump, all within a few metres. In addition to these temperature extremes, compon...

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Published:	Swansea University, Wales, UK 2024
Institution:	Swansea University
Degree level:	Doctoral
Degree name:	Ph.D
Supervisor:	Nithiarasu, P., and Baxter, M.
URI:	https://cronfa.swan.ac.uk/Record/cronfa68811

first_indexed	2025-02-06T11:35:10Z
last_indexed	2025-02-07T05:56:36Z
id	cronfa68811
recordtype	RisThesis
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These extreme conditions were achieved for short periods at JET, formerly the world’s largest fusion device located at Culham Centre for Fusion Energy, UK, before it ceased operations. But one of the greatest engineering challenges of the 21st century will be to construct a machine that can operate under these extremes routinely and produce commercially viable energy. To create a fusion reactor, relevant components must undergo rigorous testing before they can be deployed. Initially, testing at the HIVE facility focused on the sample under test (SUT) under thermal loads alone. However, with the development of testing facilities such as CHIMERA, it will soon be possible to test the SUT under both thermal and magnetic loads. Given the challenges associated with testing, any additional information to understand these components would be extremely useful. This project investigates the potential for creating a foundational digital twin capable of replicating reality from sparse data inputs. A digital twin enables real-time monitoring, provides predictive insights, and supports decision-making to optimise experimental campaigns and ensure safety. In this context, the thesis investigates the suitability of physics-informed neural networks (PINNs) for solving inverse problems and extends their application to develop a foundational physics-informed digital twin platform for replicating physical experiments. PINNs are ideal for scenarios with limited data availability, as they do not require extensive pre-training. They provide a straightforward approach to solving inverse problems without complex algorithms, making them ideal for enhancing testing processes in experimental facilities. This study demonstrates that PINNs can accurately reconstruct thermal ﬁelds from sparse data and solve inverse problems with challenging boundary conditions (BCs). The developed PINN- based digital twin reconstructed steady-state temperature distributions of the HIVE sample under varying thermal loads, enabling detailed analysis of the temperature ﬁeld and the corresponding thermal conductivity variation. 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spelling	2025-02-06T12:06:08.0062236 v2 68811 2025-02-06 A digital twin of fusion energy components with physics-informed neural networks 2d7efb264cd2528790694fcac875724e PRAKHAR SHARMA PRAKHAR SHARMA true false 2025-02-06 A fusion reactor presents a highly challenging operational environment, with engineering components exposed to extreme conditions. Temperatures range from 100 million °C at the centre of the plasma to -269 °C in the cryopump, all within a few metres. In addition to these temperature extremes, components must withstand intense electromagnetic loads and irradiationdamage. These extreme conditions were achieved for short periods at JET, formerly the world’s largest fusion device located at Culham Centre for Fusion Energy, UK, before it ceased operations. But one of the greatest engineering challenges of the 21st century will be to construct a machine that can operate under these extremes routinely and produce commercially viable energy. To create a fusion reactor, relevant components must undergo rigorous testing before they can be deployed. Initially, testing at the HIVE facility focused on the sample under test (SUT) under thermal loads alone. However, with the development of testing facilities such as CHIMERA, it will soon be possible to test the SUT under both thermal and magnetic loads. Given the challenges associated with testing, any additional information to understand these components would be extremely useful. This project investigates the potential for creating a foundational digital twin capable of replicating reality from sparse data inputs. A digital twin enables real-time monitoring, provides predictive insights, and supports decision-making to optimise experimental campaigns and ensure safety. In this context, the thesis investigates the suitability of physics-informed neural networks (PINNs) for solving inverse problems and extends their application to develop a foundational physics-informed digital twin platform for replicating physical experiments. PINNs are ideal for scenarios with limited data availability, as they do not require extensive pre-training. They provide a straightforward approach to solving inverse problems without complex algorithms, making them ideal for enhancing testing processes in experimental facilities. This study demonstrates that PINNs can accurately reconstruct thermal ﬁelds from sparse data and solve inverse problems with challenging boundary conditions (BCs). The developed PINN- based digital twin reconstructed steady-state temperature distributions of the HIVE sample under varying thermal loads, enabling detailed analysis of the temperature ﬁeld and the corresponding thermal conductivity variation. These ﬁndings establish PINNs as a promising tool for inverse modelling with limited data. E-Thesis Swansea University, Wales, UK Mechanical engineering 23 12 2024 2024-12-23 10.23889/SUThesis.68811 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 Nithiarasu, P., and Baxter, M. Doctoral Ph.D EPSRC, UKAEA EPSRC, UKAEA 2025-02-06T12:06:08.0062236 2025-02-06T11:26:01.5015608 Faculty of Science and Engineering School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Mechanical Engineering PRAKHAR SHARMA 1 68811__33518__986109903c2e4263951cb7b26312a1b9.pdf 2024_Sharma_P.final.68811.pdf 2025-02-06T11:32:19.6302626 Output 27883452 application/pdf E-Thesis – open access true Copyright: The Author, Prakhar Sharma, 2024 Distributed under the terms of a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0). true eng https://creativecommons.org/licenses/by-nc-nd/4.0/
title	A digital twin of fusion energy components with physics-informed neural networks
spellingShingle	A digital twin of fusion energy components with physics-informed neural networks PRAKHAR SHARMA
title_short	A digital twin of fusion energy components with physics-informed neural networks
title_full	A digital twin of fusion energy components with physics-informed neural networks
title_fullStr	A digital twin of fusion energy components with physics-informed neural networks
title_full_unstemmed	A digital twin of fusion energy components with physics-informed neural networks
title_sort	A digital twin of fusion energy components with physics-informed neural networks
author_id_str_mv	2d7efb264cd2528790694fcac875724e
author_id_fullname_str_mv	2d7efb264cd2528790694fcac875724e_***_PRAKHAR SHARMA
author	PRAKHAR SHARMA
author2	PRAKHAR SHARMA
format	E-Thesis
publishDate	2024
institution	Swansea University
doi_str_mv	10.23889/SUThesis.68811
college_str	Faculty of Science and Engineering
hierarchytype
hierarchy_top_id	facultyofscienceandengineering
hierarchy_top_title	Faculty of Science and Engineering
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hierarchy_parent_title	Faculty of Science and Engineering
department_str	School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Mechanical Engineering{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Mechanical Engineering
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description	A fusion reactor presents a highly challenging operational environment, with engineering components exposed to extreme conditions. Temperatures range from 100 million °C at the centre of the plasma to -269 °C in the cryopump, all within a few metres. In addition to these temperature extremes, components must withstand intense electromagnetic loads and irradiationdamage. These extreme conditions were achieved for short periods at JET, formerly the world’s largest fusion device located at Culham Centre for Fusion Energy, UK, before it ceased operations. But one of the greatest engineering challenges of the 21st century will be to construct a machine that can operate under these extremes routinely and produce commercially viable energy. To create a fusion reactor, relevant components must undergo rigorous testing before they can be deployed. Initially, testing at the HIVE facility focused on the sample under test (SUT) under thermal loads alone. However, with the development of testing facilities such as CHIMERA, it will soon be possible to test the SUT under both thermal and magnetic loads. Given the challenges associated with testing, any additional information to understand these components would be extremely useful. This project investigates the potential for creating a foundational digital twin capable of replicating reality from sparse data inputs. A digital twin enables real-time monitoring, provides predictive insights, and supports decision-making to optimise experimental campaigns and ensure safety. In this context, the thesis investigates the suitability of physics-informed neural networks (PINNs) for solving inverse problems and extends their application to develop a foundational physics-informed digital twin platform for replicating physical experiments. PINNs are ideal for scenarios with limited data availability, as they do not require extensive pre-training. They provide a straightforward approach to solving inverse problems without complex algorithms, making them ideal for enhancing testing processes in experimental facilities. This study demonstrates that PINNs can accurately reconstruct thermal ﬁelds from sparse data and solve inverse problems with challenging boundary conditions (BCs). The developed PINN- based digital twin reconstructed steady-state temperature distributions of the HIVE sample under varying thermal loads, enabling detailed analysis of the temperature ﬁeld and the corresponding thermal conductivity variation. These ﬁndings establish PINNs as a promising tool for inverse modelling with limited data.
published_date	2024-12-23T19:01:44Z
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score	11.058631

A digital twin of fusion energy components with physics-informed neural networks / PRAKHAR SHARMA

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