Answers are grounded in this paper and cite their sources.
Numerical simulation reveals enhanced heat transfer and magnetic retardation in Cu–Al2O3/H2O hybrid nanofluids over nonlinear surfaces, suggesting superior utility for advanced industrial cooling.
The study looks at how heating with Newtonian forces affects the magnetohydrodynamic (MHD) flow of water-based copper-aluminum oxide (Cu–Al 2 O 3 /H 2 O) hybrid nanofluids on a nonlinear surface which is stretching and shrinking. People are interested in hybrid nanofluids because they have better thermal qualities that make them useful for analysis and prediction of heat transfer in the real-life applications. Mathematical model constructed to explore the behavior of hybrid nanofluid Cu–Al 2 O 3 /H 2 O flow over a surface with MHD, Newtonian heating effects and nonlinear stretching. The model is transformed to ordinary differential equations (ODEs) by using similarity transformations. A computational finite difference technique with the three-stage Lobatto IIIa formula was implemented through MATLAB software to explore the hybrid nanofluids. The mixed nanofluids are better at moving heat than single component nanofluids. The Newtonian heating boundary condition leads to detectable thermal changes when used instead of standard constant temperature models. The research data demonstrates that stronger magnetic fields decrease velocity profiles through Lorentz force resistance and non-linear stretching rates control fluid speed and boundary layer width. The thermal efficiency of hybrid nanofluids gets enhanced by raising the amount of Cu nanoparticles which establishes them as a promising choice for thermal management applications across various industries such as polymer extrusion and aeronautical engineering as well as biomedical cooling systems. This research delivers vital information about hybrid nanofluids' behavior during Newtonian heating and MHD conditions that supports intelligent cooling system and efficient heat transfer technology development.
No takes yet. Share an insight, caveat, or question.
Thottempudi et al. (2025) studied this question.
Synapse has enriched 10 related papers. Consider them for comparative context: