Machine Learning Analysis of Thermal Performance Indicator of Heat Exchangers with Delta Wing Vortex Generators

Basit Öğe Kaydı
dc.authoridAziz, Muhammad/0000-0003-2433-8500|Aksöz, Zafer Yavuz/0000-0002-2852-1807|Gunay, M. Erdem/0000-0003-1282-718X
dc.authorwosidAziz, Muhammad/ISU-9993-2023
dc.authorwosidAksöz, Zafer Yavuz/IRZ-0692-2023
dc.contributor.authorAksoez, Zafer Yavuz
dc.contributor.authorGuenay, M. Erdem
dc.contributor.authorAziz, Muhammad
dc.contributor.authorTunc, K. M. Murat
dc.date.accessioned2024-07-18T20:50:42Z
dc.date.available2024-07-18T20:50:42Z
dc.date.issued2024
dc.departmentİstanbul Bilgi Üniversitesien_US
dc.description.abstractIn this work, the design features of delta wing vortex generators (DWVGs) on the thermo-hydraulic performance of heat exchangers are investigated using machine learning. Reynolds numbers, attack angle, length, wing-to-width ratio, and relative pitch ratio of DWVGs were used as descriptor variables, with Nusselt numbers, friction factors, and performance evaluation criterion (PEC) serving as target variables. Decision tree classification revealed the pathways leading to high or low values of the performance variables. Among many of those pathways, it was found that high Reynolds numbers (between 8160 and 9800) and high attack angles (greater than or equal to 47.5 degrees) lead to high Nusselt numbers. On the other hand, an attack angle between 41 degrees and 60 degrees, a Reynolds number less than 8510, and a wing-to-width ratio greater than or equal to 0.4 causes a high friction factor. Finally, the PEC is likely to enhance when the Reynolds number is higher than or equal to 10,300 and the attack angle is between 47.5 degrees and 60 degrees. In addition to the decision tree analysis, SHapley Additive exPlanations (SHAP) analysis (a part of explainable machine learning) was also applied to reveal the importance of design features and their positive and negative effects on the target variables. For example, for a Nusselt number as the target variable, the Reynolds number was found to be the most influential variable, followed by the attack angle and the relative pitch ratio, all of which had a positive impact on the target. It was then concluded that machine learning methods could help provide strong insights into the configuration design features of heat exchangers in DWVGs to improve their efficiency and save energy.en_US
dc.identifier.doi10.3390/en17061380
dc.identifier.issn1996-1073
dc.identifier.issue6en_US
dc.identifier.scopus2-s2.0-85188703939en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.3390/en17061380
dc.identifier.urihttps://hdl.handle.net/11411/8178
dc.identifier.volume17en_US
dc.identifier.wosWOS:001191533000001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherMdpien_US
dc.relation.ispartofEnergiesen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectShapley Additive Explanationsen_US
dc.subjectDecision Treeen_US
dc.subjectHeat Transfer Enhancementen_US
dc.subjectVortex Generatorsen_US
dc.subjectTransfer Enhancementen_US
dc.subjectNumerical-Simulationen_US
dc.subjectCircular Tubeen_US
dc.subjectFlat-Plateen_US
dc.subjectFluid-Flowen_US
dc.subjectChannelen_US
dc.subjectOptimizationen_US
dc.subjectBehavioren_US
dc.subjectDesignen_US
dc.titleMachine Learning Analysis of Thermal Performance Indicator of Heat Exchangers with Delta Wing Vortex Generatorsen_US
dc.typeArticleen_US

Dosyalar

Koleksiyon

Web of Science Indexed Publications
Scopus Indexed Publications