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Reduced order modelling using neural networks for predictive modelling of 3d-magneto-mechanical problems with application to magnetic resonance imaging scanners

S. Miah, YASHWANTH SOORIYAKANTHAN, P. D. Ledger Orcid Logo, Antonio Gil Orcid Logo, M. Mallett

Engineering with Computers, Volume: 39, Issue: 6, Pages: 4103 - 4127

Swansea University Authors: YASHWANTH SOORIYAKANTHAN, Antonio Gil Orcid Logo

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DOI (Published version): 10.1007/s00366-023-01870-3

Abstract

The design of magnets for Magnetic Resonance Imaging (MRI) scanners requires the numerical simulation of a coupled magneto-mechanical system where the effects that different material parameters and in-service loading conditions have on both imaging and MRI performance are key to aid with the design...

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Published in: Engineering with Computers
ISSN: 0177-0667 1435-5663
Published: Springer Science and Business Media LLC 2023
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URI: https://cronfa.swan.ac.uk/Record/cronfa63764
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To correctly capture the complex physics, and to obtain accurate solutions, finite element simulations with dense meshes and high order elements are needed. Reduced Order Model approaches, based on the established Proper Orthogonal Decomposition (POD) approach, are attractive as they can rapidly predict the numerical simulations needed under changing parameters or conditions. However, the projected (PODP) approach has an invasive computationalimplementation, whilst the interpolated (PODI) approach presents challenges when the dimension of the space of parameters to be investigated becomes large. As an alternative, we investigate a POD technique based on using a Neural Network regression, which is not as invasive as PODP, but has superior approximation properties compared to PODI. We apply this to the coupled magneto-mechanical system to understand three pressing industrial problems: firstly, the accurate and rapid computation of the resonant frequencies associated with this coupled magneto-mechanical system,secondly, the effects of magnet motion on the Ohmic power and kinetic energy curves, and, thirdly, the prediction of the uncertainty in Ohmic power and kinetic energy curves as a function of exciting frequency for uncertain material parameters.</abstract><type>Journal Article</type><journal>Engineering with Computers</journal><volume>39</volume><journalNumber>6</journalNumber><paginationStart>4103</paginationStart><paginationEnd>4127</paginationEnd><publisher>Springer Science and Business Media LLC</publisher><placeOfPublication/><isbnPrint/><isbnElectronic/><issnPrint>0177-0667</issnPrint><issnElectronic>1435-5663</issnElectronic><keywords>Magneto-mechanical coupling; Magnetic resonance imaging; Neural networks; Machine learning; Proper orthogonal decomposition; Reduced order model</keywords><publishedDay>1</publishedDay><publishedMonth>12</publishedMonth><publishedYear>2023</publishedYear><publishedDate>2023-12-01</publishedDate><doi>10.1007/s00366-023-01870-3</doi><url/><notes/><college>COLLEGE NANME</college><CollegeCode>COLLEGE CODE</CollegeCode><institution>Swansea University</institution><apcterm>Another institution paid the OA fee</apcterm><funders>The authors are grateful for the financial support received from EPSRC CASE Award Studentships in partnership with Siemens Healthineers, which are supporting S. 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spelling v2 63764 2023-07-03 Reduced order modelling using neural networks for predictive modelling of 3d-magneto-mechanical problems with application to magnetic resonance imaging scanners d9bf224cc5f4f485b3f366d3a23db490 YASHWANTH SOORIYAKANTHAN YASHWANTH SOORIYAKANTHAN true false 1f5666865d1c6de9469f8b7d0d6d30e2 0000-0001-7753-1414 Antonio Gil Antonio Gil true false 2023-07-03 The design of magnets for Magnetic Resonance Imaging (MRI) scanners requires the numerical simulation of a coupled magneto-mechanical system where the effects that different material parameters and in-service loading conditions have on both imaging and MRI performance are key to aid with the design and the manufacturing process. To correctly capture the complex physics, and to obtain accurate solutions, finite element simulations with dense meshes and high order elements are needed. Reduced Order Model approaches, based on the established Proper Orthogonal Decomposition (POD) approach, are attractive as they can rapidly predict the numerical simulations needed under changing parameters or conditions. However, the projected (PODP) approach has an invasive computationalimplementation, whilst the interpolated (PODI) approach presents challenges when the dimension of the space of parameters to be investigated becomes large. As an alternative, we investigate a POD technique based on using a Neural Network regression, which is not as invasive as PODP, but has superior approximation properties compared to PODI. We apply this to the coupled magneto-mechanical system to understand three pressing industrial problems: firstly, the accurate and rapid computation of the resonant frequencies associated with this coupled magneto-mechanical system,secondly, the effects of magnet motion on the Ohmic power and kinetic energy curves, and, thirdly, the prediction of the uncertainty in Ohmic power and kinetic energy curves as a function of exciting frequency for uncertain material parameters. Journal Article Engineering with Computers 39 6 4103 4127 Springer Science and Business Media LLC 0177-0667 1435-5663 Magneto-mechanical coupling; Magnetic resonance imaging; Neural networks; Machine learning; Proper orthogonal decomposition; Reduced order model 1 12 2023 2023-12-01 10.1007/s00366-023-01870-3 COLLEGE NANME COLLEGE CODE Swansea University Another institution paid the OA fee The authors are grateful for the financial support received from EPSRC CASE Award Studentships in partnership with Siemens Healthineers, which are supporting S. Miah and Y. Sooriyakanthan. 2024-06-06T13:30:09.1547593 2023-07-03T10:44:50.3427776 Faculty of Science and Engineering School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Civil Engineering S. Miah 1 YASHWANTH SOORIYAKANTHAN 2 P. D. Ledger 0000-0002-2587-7023 3 Antonio Gil 0000-0001-7753-1414 4 M. Mallett 5 63764__28454__f69b6d40245146c7b001593dae28e1ae.pdf 63764.pdf 2023-09-05T12:21:24.1157143 Output 3992840 application/pdf Version of Record true © The Author(s) 2023. This article is licensed under a Creative Commons Attribution 4.0 International License. true eng http://creativecommons.org/licenses/by/4.0/
title Reduced order modelling using neural networks for predictive modelling of 3d-magneto-mechanical problems with application to magnetic resonance imaging scanners
spellingShingle Reduced order modelling using neural networks for predictive modelling of 3d-magneto-mechanical problems with application to magnetic resonance imaging scanners
YASHWANTH SOORIYAKANTHAN
Antonio Gil
title_short Reduced order modelling using neural networks for predictive modelling of 3d-magneto-mechanical problems with application to magnetic resonance imaging scanners
title_full Reduced order modelling using neural networks for predictive modelling of 3d-magneto-mechanical problems with application to magnetic resonance imaging scanners
title_fullStr Reduced order modelling using neural networks for predictive modelling of 3d-magneto-mechanical problems with application to magnetic resonance imaging scanners
title_full_unstemmed Reduced order modelling using neural networks for predictive modelling of 3d-magneto-mechanical problems with application to magnetic resonance imaging scanners
title_sort Reduced order modelling using neural networks for predictive modelling of 3d-magneto-mechanical problems with application to magnetic resonance imaging scanners
author_id_str_mv d9bf224cc5f4f485b3f366d3a23db490
1f5666865d1c6de9469f8b7d0d6d30e2
author_id_fullname_str_mv d9bf224cc5f4f485b3f366d3a23db490_***_YASHWANTH SOORIYAKANTHAN
1f5666865d1c6de9469f8b7d0d6d30e2_***_Antonio Gil
author YASHWANTH SOORIYAKANTHAN
Antonio Gil
author2 S. Miah
YASHWANTH SOORIYAKANTHAN
P. D. Ledger
Antonio Gil
M. Mallett
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container_title Engineering with Computers
container_volume 39
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container_start_page 4103
publishDate 2023
institution Swansea University
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doi_str_mv 10.1007/s00366-023-01870-3
publisher Springer Science and Business Media LLC
college_str Faculty of Science and Engineering
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hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Civil Engineering{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Civil Engineering
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description The design of magnets for Magnetic Resonance Imaging (MRI) scanners requires the numerical simulation of a coupled magneto-mechanical system where the effects that different material parameters and in-service loading conditions have on both imaging and MRI performance are key to aid with the design and the manufacturing process. To correctly capture the complex physics, and to obtain accurate solutions, finite element simulations with dense meshes and high order elements are needed. Reduced Order Model approaches, based on the established Proper Orthogonal Decomposition (POD) approach, are attractive as they can rapidly predict the numerical simulations needed under changing parameters or conditions. However, the projected (PODP) approach has an invasive computationalimplementation, whilst the interpolated (PODI) approach presents challenges when the dimension of the space of parameters to be investigated becomes large. As an alternative, we investigate a POD technique based on using a Neural Network regression, which is not as invasive as PODP, but has superior approximation properties compared to PODI. We apply this to the coupled magneto-mechanical system to understand three pressing industrial problems: firstly, the accurate and rapid computation of the resonant frequencies associated with this coupled magneto-mechanical system,secondly, the effects of magnet motion on the Ohmic power and kinetic energy curves, and, thirdly, the prediction of the uncertainty in Ohmic power and kinetic energy curves as a function of exciting frequency for uncertain material parameters.
published_date 2023-12-01T13:30:10Z
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score 11.037056

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