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Heuristic Global Optimization for Thermal Model Reduction and Correlation in Aerospace Applications

2025-06-20Journal article Open access
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datacite.subject.fosEngenharia e Tecnologia::Engenharia Mecânica
datacite.subject.sdg09:Indústria, Inovação e Infraestruturas
datacite.subject.sdg13:Ação Climática
dc.contributor.authorCastanheira, João Pedro Conceição
dc.contributor.authorArribas, Beltran
dc.contributor.authorMelício, Rui
dc.contributor.authorGordo, Paulo Romeu Seabra
dc.contributor.authorSilva, André Resende Rodrigues da
dc.date.accessioned2025-12-29T09:58:06Z
dc.date.available2025-12-29T09:58:06Z
dc.date.issued2025-06-20
dc.description.abstractThis study addresses the challenge of accurately correlating detailed and reduced thermal models in aerospace applications by using heuristic global optimization methods. In the context of increasingly complex thermal systems, traditional manual correlation methods are usually a time-consuming task. This research employs a series of numerical simulations using methods such as Genetic Algorithms, Cultural Algorithms, and Artificial Immune Systems, with an emphasis on parameter tuning to optimize the reduced thermal model correlation. Results indicate that these heuristic methods can achieve high-accuracy correlations, with transient simulations exhibiting temperature differences below 3 °C, thereby validating the hypothesis that heuristic methods can effectively navigate complex parameter optimizations. Moreover, a comparative analysis of fitness function performance across various optimization methods underscores both the potential and computational challenges inherent in these approaches. The findings suggest that while heuristic global optimization provides a robust framework for thermal model reduction and correlation, further refinement—particularly in scaling to larger, more complex models and adaptive parameter tuning—is necessary. Overall, this work contributes to the theoretical understanding and practical application of advanced optimization strategies in aerospace thermal analysis, paving the way for improved predictive reliability and more efficient engineering processes.eng
dc.identifier.citationCastanheira, J.P.; Arribas, B.N.; Melicio, R.; Gordo, P.; Silva, A.R.R. Heuristic Global Optimization for Thermal Model Reduction and Correlation in Aerospace Applications. Appl. Sci. 2025, 15, 7002. https://doi.org/10.3390/app15137002
dc.identifier.doi10.3390/app15137002
dc.identifier.issn2076-3417
dc.identifier.urihttp://hdl.handle.net/10400.6/19621
dc.language.isoeng
dc.peerreviewedyes
dc.publisherMDPI
dc.relationAssociate Laboratory of Energy, Transports and Aeronautics
dc.relationAssociate Laboratory of Energy, Transports and Aeronautics UIDP/50022/2020
dc.relationLaboratório de Instrumentação e Física Experimental de Partículas
dc.relation.hasversionhttps://www.mdpi.com/2076-3417/15/13/7002
dc.relation.ispartofseriesArtificial Intelligence in Aerospace Engineering
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectHeuristic Global Optimization
dc.subjectThermal Model Reduction
dc.subjectThermal Model Correlation
dc.subjectGenetic Algorithms
dc.subjectArtificial Intelligence
dc.subjectAerospace Thermal Analysis
dc.subjectOptimization Parameter Tuning
dc.subjectDecision Support Algorithms
dc.titleHeuristic Global Optimization for Thermal Model Reduction and Correlation in Aerospace Applicationseng
dc.typejournal article
dspace.entity.typePublication
oaire.awardTitleAssociate Laboratory of Energy, Transports and Aeronautics
oaire.awardTitleAssociate Laboratory of Energy, Transports and Aeronautics UIDP/50022/2020
oaire.awardTitleLaboratório de Instrumentação e Física Experimental de Partículas
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F50022%2F2020/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDP%2F50022%2F2020/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F50007%2F2020/PT
oaire.citation.endPage32
oaire.citation.issue13
oaire.citation.startPage1
oaire.citation.titleApplied Sciences
oaire.citation.volume15
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStream6817 - DCRRNI ID
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85
person.familyNameCastanheira
person.familyNameMelício
person.familyNameGordo
person.familyNameSilva
person.givenNameJoão Pedro Conceição
person.givenNameRui
person.givenNamePaulo Romeu Seabra
person.givenNameAndré Resende Rodrigues da
person.identifier3275804
person.identifierJ-4185-2012
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person.identifier.ciencia-id251C-CF88-3C0C
person.identifier.ciencia-id8219-4B2B-E1C7
person.identifier.orcid0000-0003-4117-3621
person.identifier.orcid0000-0001-6337-9458
person.identifier.orcid0000-0002-1081-2729
person.identifier.orcid0000-0001-6861-8446
person.identifier.orcid0000-0002-4901-7140
person.identifier.scopus-author-id11440407500
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
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