The purpose of this study is to assess the heat-transfer performance and flow behavior of a newly formulated ternary hybrid nanofluid (Co–Cu–Ag in a polymer base) under the combined effects of an external magnetic field and internal heat source/sink, as it passes through a permeable pipe whose walls undergo unsteady expansion and contraction. By applying similarity transformations and solving the resulting nonlinear ordinary differential equations via the semi-analytical subdomain method (SDM) – validated against a Runge–Kutta–Fehlberg shooting technique – the work quantifies how key parameters (wall dilation rate, permeability Reynolds number, nanoparticle volume fraction, magnetic interaction and heat source/sink strength) influence tangential velocity, temperature distribution and local Nusselt number, demonstrating marked enhancements over conventional and binary hybrid nanofluids.
This research investigates the efficacy of a novel nanofluid, termed a ternary hybrid nanofluid, in improving heat transfer. The study uses a blend of mathematical modeling and semi-analytical techniques to examine the impact of an applied magnetic field and heat sink/source within fluid flow through an expanding-contracting porous pipe. The walls exhibit similar permeability, and a combination of (Co, Cu, Ag) tri-hybrid nanoparticles dispersed in a polymer liquid serves as the base fluid to model flow behavior. Researchers employed the similarity transformation technique to simplify complex nonlinear partial differential equations into more manageable ordinary differential equations. The SDM, known for its partially analytical nature, has been utilized to address the resulting system. Furthermore, residual function plots have been used to validate the accuracy of the results. To further ensure the compatibility of the numerical approach, incorporating a shooting method along with the Runge–Kutta–Fehlberg algorithm was employed. The manuscript incorporates graphs and tables to illustrate the influence of key physical parameters such as expansion-contraction, permeability, Reynolds number, heat sink/source, nanoparticle volume fraction and magnetic field on tangential velocity, temperature and local heat transfer rate. Notably, the ternary hybrid nanofluid exhibited substantial enhancements in heat transfer rate compared to both hybrid and conventional nanofluids. Additionally, the article’s conclusion thoroughly analyzes the findings and their implications.
Contraction decelerates the flow in the lower half and accelerates it in the upper half, with expansion reversing this trend. Injection increases the lower-section velocity while decreasing it in the upper section. Combined contraction/injection and expansion/injection shift initial and final velocities according to nanoparticle volume fraction and Hartmann number. Heat sinks elevate temperature, whereas heat sources lower it, with nanoparticle concentration modulating these effects. Increasing the heat sink magnitude reduces temperature; increasing the heat source raises it. Expansion and heat source/sink intensify heat transfer rates, while larger injection parameters, wall dilation rates, and nanoparticle fractions diminish the Nusselt number. Ternary hybrids outperform mono and binary fluids in variability and heat-transfer enhancement.
The study’s semi-analytical SDM assumes laminar, Newtonian flow with constant thermophysical properties and uniform 1?% nanoparticle loading, neglecting particle aggregation, Brownian motion, thermal radiation and non-Newtonian effects. Wall permeability and magnetic field are held constant, and only equal Co–Cu–Ag fractions are considered, limiting generality. Additionally, validation is numerical rather than experimental. Nevertheless, the results provide a valuable predictive framework for optimizing magnetohydrodynamics (MHD) heat-transfer performance in expandable/contractible pipes and inform the design of advanced cooling systems; future work should explore turbulent regimes, variable property models, stability analyses and laboratory confirmation to broaden applicability.
The demonstrated superior heat-transfer and flow control using Co–Cu–Ag ternary hybrid nanofluids enables more compact, efficient cooling in MHD pumps, heat exchangers and electronic thermal management systems, particularly where variable geometry (expanding/contracting pipes) is employed. Equal-fraction ternary blends offer tunable viscosity and conductivity, supporting optimized lubricant delivery in polymer-based channels and enhanced solar-thermal collectors. The semi-analytical framework facilitates rapid parametric studies for design engineers, reducing prototype iterations. Integrating magnetic field and heat-sink/source tuning can lead to adaptive thermal devices in aerospace, biomedical and renewable-energy applications.
The enhanced thermal performance of ternary hybrid nanofluids can reduce energy consumption in industrial cooling and HVAC systems, lowering greenhouse gas emissions and operational costs. In biomedical devices – such as responsive drug-delivery conduits and artificial organs – precise flow control via expandable/contractible channels may improve patient outcomes and safety. Adaptive MHD systems using these fluids could enable more reliable thermal management in renewable-energy and aerospace applications, supporting sustainable technology deployment and resilience to climate impacts. Wider adoption may stimulate skilled-jobs growth in advanced manufacturing and nanotechnology sectors, promoting economic development and technological equity.
This research introduces a novel ternary hybrid nanofluid (Co–Cu–Ag) suspended in a polymer base into the context of unsteady MHD flows through a permeable pipe with dynamic wall motion, combined with heat source/sink effects. The semi-analytical Subdomain Method paired with an RKF-shooting validation offers a new solution paradigm for nonlinear ODEs in variable-geometry conduits. By directly comparing ternary, binary and mono nanofluids under identical conditions, it uniquely quantifies the incremental benefits of ternary blends. This original integration paves the way for data-driven optimization in adaptive thermal systems and advanced fluid-engineering applications.
