Neural network and thermodynamic optimization of magnetized hybrid nanofluid dissipative radiative convective flow with energy activation

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dc.authorid0000-0001-9321-972X
dc.authorid0000-0001-9297-8134
dc.authorid0000-0003-0614-8711
dc.contributor.authorFerdows, M.
dc.contributor.authorAhmed, Muktadir
dc.contributor.authorBhuiyan, Miraj Ahmed
dc.contributor.authorBeg, O. Anwar
dc.contributor.authorÇolak, Andaç Batur
dc.contributor.authorLeonard, H. J.
dc.date.accessioned2024-10-12T19:42:56Z
dc.date.available2024-10-12T19:42:56Z
dc.date.issued2024
dc.departmentİstanbul Ticaret Üniversitesi, Bilişim Teknolojileri Uygulama ve Araştırma Merkezien_US
dc.description.abstractThis article, motivated by hybrid magnetic coating manufacturing developments, utilizes a neural network-based computational program to study the dynamics of hybrid magnetic nanofluids with entropy generation. A new physico-chemo-mathematical model has been presented to simulate the hybrid magnetic nano-coating flow along a stretching surface to a porous medium with viscous heating. A Rosseland flux model is used for radiation heat transfer and Darcy's model for the isotropic porous medium. The stretching sheet is porous, and wall suction or injection are possible. A robust neural network has been deployed to optimize the physical parameters controlling the transport characteristics of hybrid nanofluids. Specifically, two hybrid nanoparticle combinations are addressed, namely graphite oxide (GO)-molybdenum disulfide (MoS2) and copper (Cu)-silicon dioxide (SiO2), both with engine oil as the base fluid. The dimensional boundary layer model is transformed via suitable scaling variables from a partial differential system into a dimensionless non-linear coupled ordinary differential system. The transformed boundary value problem is solved numerically with the BVP4C subroutine in the symbolic software MATLAB, which achieves exceptional accuracy. Validation with previous simpler studies is conducted and a good correlation is obtained. The neural network optimization analysis incorporates Bayesian regularization as the training algorithm. The Bejan entropy generation minimization (EGM) analysis shows that with increasing radiation parameter R-d, both entropy generation rate and Bejan number are increased. Furthermore, an elevation in Brinkman number Br leads to an upsurge in entropy generation rate and a downtrend in the Bejan number. The numerical solution of the boundary value problem reveals that with an increment in nanoparticle solid volume fraction phi(2), magnetic parameter M, inverse permeability parameter epsilon, surface injection parameter (s<0), Eckert number Ec and radiation parameter R-d and with a decrement in suction parameter (s>0) and Prandtl number Pr, there is a strong enhancement in temperature magnitude and thermal boundary layer thickness. With greater nanoparticle solid volume fraction phi(2), magnetic parameter M, inverse permeability parameter epsilon, suction parameter s and a reduction in thermal buoyancy parameter lambda, strong flow deceleration is induced, and momentum boundary layer thickness is increased. The skin friction coefficient is substantially boosted with lower values of magnetic parameter M, inverse permeability parameter epsilon, suction parameter s and higher values of thermal buoyancy parameter lambda. There is a significant decrement also computed in Nusselt number with a greater radiation parameter R-d. The simulations provide a good benchmark for future extensions that may consider non-Newtonian behavior.en_US
dc.description.sponsorshipUniversity of Dhaka; University Grants Commission of Bangladesh Grant [2022-2023]en_US
dc.description.sponsorshipWe are grateful to University of Dhaka and University Grants Commission of Bangladesh Grant 2022-2023 for providing the financial support for this work.en_US
dc.identifier.citationFerdows, M., Ahmed, M., Bhuiyan, M. A., Bég, O. A., Çolak, A. B., & Leonard, H. J. (2024). Neural Network And Thermodynamic Optimization Of Magnetized Hybrid Nanofluid Dissipative Radiative Convective Flow With Energy Activation. Numerical Heat Transfer, Part A: Applications, 1-35.
dc.identifier.doi10.1080/10407782.2024.2329312
dc.identifier.issn1040-7782
dc.identifier.issn1521-0634
dc.identifier.urihttps://doi.org/10.1080/10407782.2024.2329312
dc.identifier.urihttps://hdl.handle.net/11467/8674
dc.identifier.wosWOS:001189703300001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.language.isoenen_US
dc.publisherTaylor & Francis Incen_US
dc.relation.ispartofNumerical Heat Transfer Part A-Applicationsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectBayesian Regularizationen_US
dc.subjectBejan Numberen_US
dc.subjectBoundary Layersen_US
dc.subjectBrinkman Numberen_US
dc.subjectCu-SiO2/engine Oilhybrid Nanofluiden_US
dc.subjectCoatingen_US
dc.subjectEntropy Generationen_US
dc.subjectHybrid Magnetic Nanofluidsen_US
dc.subjectNeural Networken_US
dc.subjectThermodynamic Optimizationen_US
dc.subjectWall Mass Fluxen_US
dc.subjectMATLAB BVP4Cen_US
dc.subjectRadiative Heat Fluxen_US
dc.titleNeural network and thermodynamic optimization of magnetized hybrid nanofluid dissipative radiative convective flow with energy activationen_US
dc.typeArticleen_US

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