Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/29218
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dc.contributor.authorWidgington, JJ-
dc.contributor.authorWang, F-
dc.contributor.authorIvanov, A-
dc.contributor.authorKarayiannis, TG-
dc.coverage.spatialBirmingham University-
dc.date.accessioned2024-06-18T11:14:16Z-
dc.date.available2024-06-18T11:14:16Z-
dc.date.issued2024-09-09-
dc.identifierORCiD: Atanas Ivanov https://orcid.org/0000-0001-8041-4323-
dc.identifierORCD: Fang Wang https://orcid.org/0000-0003-1987-9150-
dc.identifierORCiD: Tassos G. Karayiannis https://orcid.org/0000-0002-5225-960X-
dc.identifierUKHTC2024-009-
dc.identifier.citationWidgington, J.J. et al. (2024) 'A novel feed-forward neural network for flow boiling pattern prediction', Proceedings of the 18th UK Heat Transfer Conference, Birmingham, UK, 9 -11 September, UKHTC2024-009, pp. 1 - 3. Available at: https://more.bham.ac.uk/ukhtc-2024/wp-content/uploads/sites/80/2024/09/UKHTC-2024_paper_9.pdf (accessed: 9 September 2024).en_US
dc.identifier.urihttps://bura.brunel.ac.uk/handle/2438/29218-
dc.description.abstractMicroscale flow boiling presents a promising solution to emerging cooling requirements in many applications. Predicting flow boiling patterns could play a key role in the development of new engineering design tools for predicting heat transfer rates and pressure drops. A novel feed-forward neural network architecture was developed to classify flow boiling patterns in the microscale, in which each transition boundary was considered with its own Forward Neural Network within the overall architecture. The network was then compared to new flow boiling pattern data using HFE-7100 for heat fluxes and mass fluxes between 3.2-132.4 kW/m² and 100-1000 kg/m²s, respectively.en_US
dc.description.sponsorshipEPSRC through grant EP/P004709/1en_US
dc.format.extent1 - 3-
dc.format.mediumElectronic-
dc.language.isoenen_US
dc.publisherUKHTCen_US
dc.relation.urihttps://more.bham.ac.uk/ukhtc-2024/wp-content/uploads/sites/80/2024/09/UKHTC-2024_paper_9.pdf-
dc.rightsAttribution 4.0 International-
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/-
dc.source18th UK Heat Transfer Conference-
dc.source18th UK Heat Transfer Conference-
dc.titleA novel feed-forward neural network for flow boiling pattern predictionen_US
dc.typeConference Paperen_US
dc.date.dateAccepted2024-06-17-
pubs.finish-date2024-09-11-
pubs.finish-date2024-09-11-
pubs.publication-statusPublished online-
pubs.start-date2024-09-09-
pubs.start-date2024-09-09-
dc.rights.licensehttps://creativecommons.org/licenses/by/4.0/legalcode.en-
dc.rights.holderThe Author(s)-
Appears in Collections:Dept of Computer Science Research Papers
Dept of Mechanical and Aerospace Engineering Research Papers

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