Neural network-based investigation of MHD radiative flow of ternary hybrid nanofluid in porous media

Basit Öğe Kaydı
dc.authorid0000-0003-0149-9053
dc.contributor.authorUllah, Kashif
dc.contributor.authorUllah, Hakeem
dc.contributor.authorFiza, Mehreen
dc.contributor.authorJan, Aasim Ullah
dc.contributor.authorAkguel, Ali
dc.contributor.authorHendy, A. S.
dc.contributor.authorKhan, Ilyas
dc.date.accessioned2026-03-14T11:48:49Z
dc.date.available2026-03-14T11:48:49Z
dc.date.issued2025
dc.departmentSiirt Üniversitesi
dc.description.abstractApplications: Scientific research in many disciplines, such as aerospace engineering, industrial cooling, energy systems, biomedicine, geophysics for oil recovery, and geothermal energy extraction, can greatly benefit from the magnetohydrodynamic radiative flow of a ternary hybrid nanofluid (MHD-RFTHN) over a rotating disk in a porous media. Artificial recurrent neural network enhanced with Bayesian regularization (ARNN-BR) is also used in advanced technologies like neurological disorder detection, speech recognition, mechanism translation, and automatic text translation. Novelty: This work proposed a supervised learning approach based on ARNN-BR method for obtaining numerical solutions of MHD-RFTHN by considering the rotating disk in a porous medium. It investigates. the thermophysical characteristics of ternary nanomaterials (Cu,Mn Zn Fe2O4, and Ag) dispersed in pure water as the base fluid. Compared to traditional numerical methods, ARNN-BR increases accuracy and stability as well as lowers computational cost. Intelligent computing facilitates enhanced prediction of fluid flow behavior in response to variations of numerous physical parameters while aptly capturing the influences of porosity, MHD, thermal radiation, and suction or injection for a more comprehensive analysis. Methodology: The system of governing partial differential equations (PDEs) is transformed into a dimensionless ordinary differential equation (ODEs) using similarity transformations and then solved using the ARNN-BR technique. The proposed method is trained on numerical data generated by varying multiple physical parameters, including the magnetic parameter, nonlinear thermal radiation, porous parameter, suction or injection parameter, volume fraction, temperature ratio parameter, and Prandtl number. The ARNN-BR technique has been validated using performance, regression index evaluation, error histogram analysis, autocorrelation investigations, and convergence curve analysis. With a minimal mean square error ranging from 10-10 to 10-3, the results demonstrate the method's high accuracy and effectiveness. Findings: The increase in porosity parameter decreased the radial velocity. One significant finding is that the temperature profile of the ternary hybrid nanofluid (THNF) can be improved by increased nanoparticle volume
dc.description.sponsorshipPrincess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia [PNURSP2025R738]
dc.description.sponsorshipThe authors express their gratitude to Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2025R738) , Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.
dc.identifier.doi10.1016/j.jrras.2025.101448
dc.identifier.issn1687-8507
dc.identifier.issue2
dc.identifier.urihttps://doi.org/10.1016/j.jrras.2025.101448
dc.identifier.urihttps://hdl.handle.net/20.500.12604/8966
dc.identifier.volume18
dc.identifier.wosWOS:001456453400001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.language.isoen
dc.publisherElsevier
dc.relation.ispartofJournal of Radiation Research and Applied Sciences
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WoS_20260310
dc.subjectIndia
dc.subjectThnf
dc.subjectArnn-Br
dc.subjectRotating Disk
dc.subjectNonlinear Thermal Radiation
dc.subjectPorous Media
dc.titleNeural network-based investigation of MHD radiative flow of ternary hybrid nanofluid in porous media
dc.typeArticle

Dosyalar

Koleksiyon

WOS İndeksli Yayınlar Koleksiyonu