Analysis of bubble departure and lift-off boiling model using computational intelligence techniques and hybrid algorithms

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dc.authoridJilte, Ravindra/0000-0003-3383-8179en_US
dc.authoridAsif, Mohammad/0000-0003-3196-0074en_US
dc.authoridAgbulut, Umit/0000-0002-6635-6494en_US
dc.authoridQuadros, Jaimon Dennis/0000-0002-5848-3389en_US
dc.authorscopusid56241215200en_US
dc.authorscopusid57218708397en_US
dc.authorscopusid57202959651en_US
dc.authorscopusid57196825693en_US
dc.authorscopusid57211839179en_US
dc.authorscopusid55065002500en_US
dc.authorscopusid56505514100en_US
dc.authorwosidJilte, Ravindra/AAA-6931-2020en_US
dc.authorwosidAkhtar, Mohammad Nishat/S-7313-2018en_US
dc.authorwosidAsif, Mohammad/C-6332-2009en_US
dc.contributor.authorQuadros, Jaimon Dennis
dc.contributor.authorMogul, Yakub Iqbal
dc.contributor.authorAgbulut, Umit
dc.contributor.authorGurel, Ali Etem
dc.contributor.authorKhan, Sher Afghan
dc.contributor.authorAkhtar, Mohammad Nishat
dc.contributor.authorJilte, R. D.
dc.date.accessioned2024-08-23T16:04:43Z
dc.date.available2024-08-23T16:04:43Z
dc.date.issued2024en_US
dc.departmentDüzce Üniversitesien_US
dc.description.abstractThe bubble departure and lift-off boiling (BDL) model was studied using computational intelligence techniques and hybrid algorithms. Quite a few studies have predicted the relationship between wall heat fluxes and wall temperature in the form of flow boiling curves. The output wall temperature is a performance indicator that depends on many operating parameters. The current study, therefore, analyses the predictability of the wall temperature in terms of operating pressure, bulk flow velocity, and wall heat flux, based on the BDL model developed by Zenginer, which included two suppression factors namely, flow-induced and subcooling factors, respectively. The soft computing techniques used for prediction were - the artificial neural network (ANN), and the Fuzzy Mamdani model, and the hybrid algorithms were adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network trained particle swarm optimization (ANN-PSO). In addition, the ANN-PSO conducted a parametric analysis to evaluate the best model configuration by considering various factors. The comparison of all four techniques showed that the ANFIS model exhibited the prediction performance for wall temperature. Moreover, the results obtained from the ANFIS model have been compared with the different flow boiling curves from the literature and observed that the curve fitted well for higher bulk flow velocities with an MSE and R2 was found to be 0.85 % and 0.9933, respectively.en_US
dc.description.sponsorshipKing Saud University, Riyadh, Saudi Arabia [RSP2023R42]en_US
dc.description.sponsorshipThe financial support from the Researchers Supporting Project (RSP2023R42) , King Saud University, Riyadh, Saudi Arabia is appreciated.en_US
dc.identifier.doi10.1016/j.ijthermalsci.2023.108810
dc.identifier.issn1290-0729
dc.identifier.issn1778-4166
dc.identifier.scopus2-s2.0-85178141954en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1016/j.ijthermalsci.2023.108810
dc.identifier.urihttps://hdl.handle.net/20.500.12684/14332
dc.identifier.volume197en_US
dc.identifier.wosWOS:001132196900001en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherElsevier France-Editions Scientifiques Medicales Elsevieren_US
dc.relation.ispartofInternational Journal of Thermal Sciencesen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectBDL modelen_US
dc.subjectWall temperatureen_US
dc.subjectANNen_US
dc.subjectFMMen_US
dc.subjectANFISen_US
dc.subjectANN-PSOen_US
dc.subjectArtificial Neural-Networken_US
dc.subjectHeat-Transfer Coefficienten_US
dc.subjectDetachment Diametersen_US
dc.subjectUnified Modelen_US
dc.subjectPredictionen_US
dc.subjectOptimizationen_US
dc.subjectPressureen_US
dc.subjectSystemsen_US
dc.subjectFluidsen_US
dc.subjectWateren_US
dc.titleAnalysis of bubble departure and lift-off boiling model using computational intelligence techniques and hybrid algorithmsen_US
dc.typeArticleen_US

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