Coupled effect of magnetic field and nanoparticle aggregation on heat transfer and entropy generation in a porous radial fin embedded with water-based multi-walled carbon nanotube nanofluid

This study presents a novel analysis of the coupling effects of magnetic field and nanoparticle aggregation on heat flux and entropy generation in porous radial fin embedded in a water-based MWCNT nanofluid. Unlike conventional analysis, the proposed model explicitly accounts for aggregated and non-aggregated nanoparticle states to capture their effect on thermophysical properties. The governing nonlinear energy model, incorporating the effects of nanoparticle aggregation, magnetic field, thermal conductivity, and viscous dissipation, is transformed into a dimensionless framework, and the resulting boundary value problem is solved numerically using high-accuracy Lobatto-IIIA technique. A comprehensive parametric analysis reveals that heat flux and entropy generation varies significantly across various governing parameters and operating conditions. The non-aggregated nanofluid model consistently outperforms the aggregated case, exhibiting a heat flux enhancement ranging from 14 to 63%, along with a maximum reduction in entropy generation of 48.45%. An average increase in heat flux of 38.23% and 49.5% is observed with increasing Nc and G parameters, while an enhancement of 20.2%, 50.36%, 23.06%, and 25.33% are obtained with decreasing Nr, θ ₐ θ a , m₂ m 2 , and H , highlighting the strong sensitivity of the model to thermal and magnetic parameters. Similarly, a notable reduction in entropy generation, ranging from 2.12 to 48.45%, is observed for non-aggregated model with decreasing θ ₐ θ a , m₂ m 2 , and H , while a reduction in the range of 20.15–39.64% is achieved with increasing Nc and G parameters. The observed enhancement in the thermal performance arises from improved nanoparticle dispersion and increased thermal conductivity induced by magnetic field. These findings emphasize that ensuring stable and uniform nanoparticle dispersion along with proper tuning of governing parameters are critical for reducing entropy generation and achieving superior heat transfer performance in fin-based thermal systems. It also offers valuable insights into the design and optimization of thermal management systems.