Turbulent heat transfer in dilute ferrofluids under the influence of magnetic fields

Purpose The purpose of this study is to investigate whether dilute ferrofluids subjected to static magnetic fields can enhance convective heat transfer in turbulent channel flow. Although nanoparticle suspensions are known to increase thermal conductivity, high particle concentrations significantly raise viscosity and pumping requirements. Using ferrofluids with low particle loadings in combination with magnetic forcing has been proposed as an alternative mechanism to promote mixing without incurring excessive flow resistance. Design/methodology/approach Large eddy simulations of turbulent channel flow at a friction Reynolds number Ret = 395 are conducted for homogeneous dilute ferrofluids exposed to static magnetic fields. The ferrofluid magnetization is modeled using both a saturation-based nonlinear constitutive law and a commonly applied linearized approximation. Magnetic field strengths range from 1 mT to 3 T, allowing assessment of magnetically induced flow modification and its impact on momentum and heat transport. Findings For the physically realistic saturation-based magnetization, static magnetic fields cause a slight suppression of near-wall turbulence, resulting in marginally reduced heat transfer performance. This effect is linked to the saturation of particle magnetization at elevated field intensities. In contrast, the linearized magnetization model produces pronounced secondary flow structures that artificially enhance heat transfer, demonstrating that nonphysical model assumptions can lead to misleading conclusions regarding ferrofluid-based thermal enhancement in turbulent regimes. Originality/value This study clarifies that static magnetic fields do not enhance heat transfer for dilute ferrofluids in turbulent flows and helps researchers and engineers assess the practical limits of magnetically assisted thermal management. Furthermore, this study emphasizes the necessity of using realistic magnetization models for accurate predictions.