No Cover Image

Journal article 47 views 11 downloads

Integrated environment for machine learning-aided heat transfer optimisation in internal flows: hammerhead software

Daniela Segura Galeana Orcid Logo, A Liptak Orcid Logo, Antonio Gil Orcid Logo, Michael G. Edwards, A Dubas, A Davis Orcid Logo

Machine Learning: Science and Technology, Volume: 7, Issue: 1, Start page: 015011

Swansea University Authors: Daniela Segura Galeana Orcid Logo, Antonio Gil Orcid Logo, Michael G. Edwards

  • 71237.VOR.pdf

    PDF | Version of Record

    © 2026 The Author(s). Original content from this work may be used under the terms of the Creative Commons Attribution 4.0 license.

    Download (3.84MB)

Check full text

DOI (Published version): 10.1088/2632-2153/ae3053

Abstract

The optimisation of the cooling system performance in a tokamak reactor requires the accurate analysis of the thermo-hydraulic interactions under complex flow regimes. Although computational fluid dynamics (CFD) provides high-fidelity insight at a lower cost than experimental testing, optimisation r...

Full description

Published in: Machine Learning: Science and Technology
ISSN: 2632-2153
Published: IOP Publishing Ltd 2026
Online Access: Check full text

URI: https://cronfa.swan.ac.uk/Record/cronfa71237
first_indexed 2026-01-13T13:41:00Z
last_indexed 2026-02-07T05:28:47Z
id cronfa71237
recordtype SURis
fullrecord <?xml version="1.0"?><rfc1807><datestamp>2026-02-06T16:24:34.2265097</datestamp><bib-version>v2</bib-version><id>71237</id><entry>2026-01-13</entry><title>Integrated environment for machine learning-aided heat transfer optimisation in internal flows: hammerhead software</title><swanseaauthors><author><sid>7b371aa96fdb3b381c8576f2ceec0de3</sid><ORCID>0009-0007-0758-0262</ORCID><firstname>Daniela</firstname><surname>Segura Galeana</surname><name>Daniela Segura Galeana</name><active>true</active><ethesisStudent>false</ethesisStudent></author><author><sid>1f5666865d1c6de9469f8b7d0d6d30e2</sid><ORCID>0000-0001-7753-1414</ORCID><firstname>Antonio</firstname><surname>Gil</surname><name>Antonio Gil</name><active>true</active><ethesisStudent>false</ethesisStudent></author><author><sid>8903caf3d43fca03602a72ed31d17c59</sid><firstname>Michael G.</firstname><surname>Edwards</surname><name>Michael G. Edwards</name><active>true</active><ethesisStudent>false</ethesisStudent></author></swanseaauthors><date>2026-01-13</date><deptcode>ACEM</deptcode><abstract>The optimisation of the cooling system performance in a tokamak reactor requires the accurate analysis of the thermo-hydraulic interactions under complex flow regimes. Although computational fluid dynamics (CFD) provides high-fidelity insight at a lower cost than experimental testing, optimisation remains computationally prohibitive due to the large number of simulations required. To address this, we introduce Hammerhead, the first open-source, transparent and fully integrated machine learning (ML)-CFD framework with automated high-fidelity database generation and modular multi-surrogate modelling for optimisation in pipe flow heat and mass transfer problems. The software provides seamless Python-OpenFOAM integration for automatic high-fidelity model database building and multi-surrogate model comparison from 3 ML algorithms with modular architecture within Python infrastructure. For the high-fidelity database, the software constructs a parametric space using up to four shape parameters to deform pipe wall geometries and perform the conjugate heat transfer simulations. The surrogate models are constructed on the basis of radial basis function interpolation, feed-forward neural networks, and Gaussian processes, and trained on the high-fidelity database to approximate thermo-hydraulic responses efficiently. The framework provides a comparative environment for systematically assessing surrogate model accuracy and reliability within the same optimisation workflow. The approach offers a practical tool that increases the feasibility of design optimisation on small datasets for cooling system applications. On a database of up to 400 cases for a 2D domain, a geometry with thermo-hydraulic performance enhancement of over 500% with respect to that of a smooth surface pipe at Re&#x2009;=&#x2009;1000 was found with the aid of surrogate modelling, validated through high-fidelity simulation.</abstract><type>Journal Article</type><journal>Machine Learning: Science and Technology</journal><volume>7</volume><journalNumber>1</journalNumber><paginationStart>015011</paginationStart><paginationEnd/><publisher>IOP Publishing Ltd</publisher><placeOfPublication/><isbnPrint/><isbnElectronic/><issnPrint/><issnElectronic>2632-2153</issnElectronic><keywords>machine learning, computational fluid dynamics, neural networks, heat transfer, Gaussian processes</keywords><publishedDay>1</publishedDay><publishedMonth>2</publishedMonth><publishedYear>2026</publishedYear><publishedDate>2026-02-01</publishedDate><doi>10.1088/2632-2153/ae3053</doi><url/><notes/><college>COLLEGE NANME</college><department>Aerospace, Civil, Electrical, and Mechanical Engineering</department><CollegeCode>COLLEGE CODE</CollegeCode><DepartmentCode>ACEM</DepartmentCode><institution>Swansea University</institution><apcterm>SU Library paid the OA fee (TA Institutional Deal)</apcterm><funders>The research was developed through the ESPRC DTP ADDED project with the financial support of RCUK Energy Programme, Grant Number EP/T012250/1, and ERDF via the Welsh Government.</funders><projectreference>EP/T012250/1</projectreference><lastEdited>2026-02-06T16:24:34.2265097</lastEdited><Created>2026-01-13T13:33:15.6061941</Created><path><level id="1">Faculty of Science and Engineering</level><level id="2">School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Civil Engineering</level></path><authors><author><firstname>Daniela</firstname><surname>Segura Galeana</surname><orcid>0009-0007-0758-0262</orcid><order>1</order></author><author><firstname>A</firstname><surname>Liptak</surname><orcid>0000-0003-1597-4193</orcid><order>2</order></author><author><firstname>Antonio</firstname><surname>Gil</surname><orcid>0000-0001-7753-1414</orcid><order>3</order></author><author><firstname>Michael G.</firstname><surname>Edwards</surname><order>4</order></author><author><firstname>A</firstname><surname>Dubas</surname><order>5</order></author><author><firstname>A</firstname><surname>Davis</surname><orcid>0000-0003-4397-0712</orcid><order>6</order></author></authors><documents><document><filename>71237__36208__a2cc10276547442d9bef559256a68a38.pdf</filename><originalFilename>71237.VOR.pdf</originalFilename><uploaded>2026-02-06T16:20:36.3825204</uploaded><type>Output</type><contentLength>4026636</contentLength><contentType>application/pdf</contentType><version>Version of Record</version><cronfaStatus>true</cronfaStatus><documentNotes>&#xA9; 2026 The Author(s). Original content from this work may be used under the terms of the Creative Commons Attribution 4.0 license.</documentNotes><copyrightCorrect>true</copyrightCorrect><language>eng</language><licence>https://creativecommons.org/licenses/by/4.0/</licence></document></documents><OutputDurs/></rfc1807>
spelling 2026-02-06T16:24:34.2265097 v2 71237 2026-01-13 Integrated environment for machine learning-aided heat transfer optimisation in internal flows: hammerhead software 7b371aa96fdb3b381c8576f2ceec0de3 0009-0007-0758-0262 Daniela Segura Galeana Daniela Segura Galeana true false 1f5666865d1c6de9469f8b7d0d6d30e2 0000-0001-7753-1414 Antonio Gil Antonio Gil true false 8903caf3d43fca03602a72ed31d17c59 Michael G. Edwards Michael G. Edwards true false 2026-01-13 ACEM The optimisation of the cooling system performance in a tokamak reactor requires the accurate analysis of the thermo-hydraulic interactions under complex flow regimes. Although computational fluid dynamics (CFD) provides high-fidelity insight at a lower cost than experimental testing, optimisation remains computationally prohibitive due to the large number of simulations required. To address this, we introduce Hammerhead, the first open-source, transparent and fully integrated machine learning (ML)-CFD framework with automated high-fidelity database generation and modular multi-surrogate modelling for optimisation in pipe flow heat and mass transfer problems. The software provides seamless Python-OpenFOAM integration for automatic high-fidelity model database building and multi-surrogate model comparison from 3 ML algorithms with modular architecture within Python infrastructure. For the high-fidelity database, the software constructs a parametric space using up to four shape parameters to deform pipe wall geometries and perform the conjugate heat transfer simulations. The surrogate models are constructed on the basis of radial basis function interpolation, feed-forward neural networks, and Gaussian processes, and trained on the high-fidelity database to approximate thermo-hydraulic responses efficiently. The framework provides a comparative environment for systematically assessing surrogate model accuracy and reliability within the same optimisation workflow. The approach offers a practical tool that increases the feasibility of design optimisation on small datasets for cooling system applications. On a database of up to 400 cases for a 2D domain, a geometry with thermo-hydraulic performance enhancement of over 500% with respect to that of a smooth surface pipe at Re = 1000 was found with the aid of surrogate modelling, validated through high-fidelity simulation. Journal Article Machine Learning: Science and Technology 7 1 015011 IOP Publishing Ltd 2632-2153 machine learning, computational fluid dynamics, neural networks, heat transfer, Gaussian processes 1 2 2026 2026-02-01 10.1088/2632-2153/ae3053 COLLEGE NANME Aerospace, Civil, Electrical, and Mechanical Engineering COLLEGE CODE ACEM Swansea University SU Library paid the OA fee (TA Institutional Deal) The research was developed through the ESPRC DTP ADDED project with the financial support of RCUK Energy Programme, Grant Number EP/T012250/1, and ERDF via the Welsh Government. EP/T012250/1 2026-02-06T16:24:34.2265097 2026-01-13T13:33:15.6061941 Faculty of Science and Engineering School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Civil Engineering Daniela Segura Galeana 0009-0007-0758-0262 1 A Liptak 0000-0003-1597-4193 2 Antonio Gil 0000-0001-7753-1414 3 Michael G. Edwards 4 A Dubas 5 A Davis 0000-0003-4397-0712 6 71237__36208__a2cc10276547442d9bef559256a68a38.pdf 71237.VOR.pdf 2026-02-06T16:20:36.3825204 Output 4026636 application/pdf Version of Record true © 2026 The Author(s). Original content from this work may be used under the terms of the Creative Commons Attribution 4.0 license. true eng https://creativecommons.org/licenses/by/4.0/
title Integrated environment for machine learning-aided heat transfer optimisation in internal flows: hammerhead software
spellingShingle Integrated environment for machine learning-aided heat transfer optimisation in internal flows: hammerhead software
Daniela Segura Galeana
Antonio Gil
Michael G. Edwards
title_short Integrated environment for machine learning-aided heat transfer optimisation in internal flows: hammerhead software
title_full Integrated environment for machine learning-aided heat transfer optimisation in internal flows: hammerhead software
title_fullStr Integrated environment for machine learning-aided heat transfer optimisation in internal flows: hammerhead software
title_full_unstemmed Integrated environment for machine learning-aided heat transfer optimisation in internal flows: hammerhead software
title_sort Integrated environment for machine learning-aided heat transfer optimisation in internal flows: hammerhead software
author_id_str_mv 7b371aa96fdb3b381c8576f2ceec0de3
1f5666865d1c6de9469f8b7d0d6d30e2
8903caf3d43fca03602a72ed31d17c59
author_id_fullname_str_mv 7b371aa96fdb3b381c8576f2ceec0de3_***_Daniela Segura Galeana
1f5666865d1c6de9469f8b7d0d6d30e2_***_Antonio Gil
8903caf3d43fca03602a72ed31d17c59_***_Michael G. Edwards
author Daniela Segura Galeana
Antonio Gil
Michael G. Edwards
author2 Daniela Segura Galeana
A Liptak
Antonio Gil
Michael G. Edwards
A Dubas
A Davis
format Journal article
container_title Machine Learning: Science and Technology
container_volume 7
container_issue 1
container_start_page 015011
publishDate 2026
institution Swansea University
issn 2632-2153
doi_str_mv 10.1088/2632-2153/ae3053
publisher IOP Publishing Ltd
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 - Civil Engineering{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Civil Engineering
document_store_str 1
active_str 0
description The optimisation of the cooling system performance in a tokamak reactor requires the accurate analysis of the thermo-hydraulic interactions under complex flow regimes. Although computational fluid dynamics (CFD) provides high-fidelity insight at a lower cost than experimental testing, optimisation remains computationally prohibitive due to the large number of simulations required. To address this, we introduce Hammerhead, the first open-source, transparent and fully integrated machine learning (ML)-CFD framework with automated high-fidelity database generation and modular multi-surrogate modelling for optimisation in pipe flow heat and mass transfer problems. The software provides seamless Python-OpenFOAM integration for automatic high-fidelity model database building and multi-surrogate model comparison from 3 ML algorithms with modular architecture within Python infrastructure. For the high-fidelity database, the software constructs a parametric space using up to four shape parameters to deform pipe wall geometries and perform the conjugate heat transfer simulations. The surrogate models are constructed on the basis of radial basis function interpolation, feed-forward neural networks, and Gaussian processes, and trained on the high-fidelity database to approximate thermo-hydraulic responses efficiently. The framework provides a comparative environment for systematically assessing surrogate model accuracy and reliability within the same optimisation workflow. The approach offers a practical tool that increases the feasibility of design optimisation on small datasets for cooling system applications. On a database of up to 400 cases for a 2D domain, a geometry with thermo-hydraulic performance enhancement of over 500% with respect to that of a smooth surface pipe at Re = 1000 was found with the aid of surrogate modelling, validated through high-fidelity simulation.
published_date 2026-02-01T05:34:48Z
_version_ 1856987084454100992
score 11.096295

Similar Items