Numerical analysis of MHD ternary nanofluid flow past a permeable stretching/shrinking sheet with velocity slip
Document Type
Article
Publication Date
3-1-2025
Abstract
In the applications of polymer film production and lubrication systems, ternary hybrid nanofluids can significantly enhance product performance by improving thermal conduction. This study focuses on investigating a nanofluid consisting of alumina (Al2O3), zincite (ZnO), and magnetite (Fe3O4) dispersed in water to optimize its thermal and flow properties. By integrating magnetohydrodynamic (MHD) flow dynamics over a stretching/shrinking sheet with velocity slip and employing both analytical solutions and numerical analysis through MATLAB's BVP4C, the research investigates critical parameters such as suction/injection, magnetic strength, and slip effects on surface properties and thermal transfer. The results show that increased nanoparticle hybridity, suction/injection, and magnetic strength enhance skin friction and the local Nusselt number in the primary solution, while the secondary solution shows reductions. Velocity accelerates with increased suction/injection and magnetic parameters, while temperature decreases with higher velocity slip, suction/injection, and magnetic strength. Stability analysis highlights the robustness of the primary solution and the complexities of secondary solutions, with ternary and binary nanofluids outperforming mono nanofluids under shrinking sheet conditions (lambda = - 6) and achieving thermal efficiency gains of 0.43% and 0.2%, respectively. This study's novelty lies in determining the critical conditions for one, two, or no solutions and integrating analytical and numerical methods for comprehensive insights. The contribution of this work extends to optimizing heat transfer in advanced industrial applications using ternary hybrid nanofluids.
Keywords
Ternary hybrid nanofluid, MHD, Stretching/shrinking sheet, Velocity slip
Divisions
Science,MathematicalSciences
Funders
Hechi University Scientific Research Project, China,Universiti Teknologi Malaysia (Q.J130000.3854.21H91),Ministry of Higher Education (MoHE) Malaysia via the Fundamental Research Grant Scheme (FRGS) (FRGS/1/2023/STG06/UM/02/14),Key Laboratory of AI and Information Processing (Hechi University), Education Department of Guangxi Zhuang Autonomous Region (2022YLXK001)
Publication Title
Alexandria Engineering Journal
Volume
116
Publisher
Elsevier
Publisher Location
RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS