Thermal Performance of Ferrofluids Under Magnetic Fields: A Comprehensive Review

The increasing need for efficient thermal management systems has led to the development of nanofluids, particularly magnetic nanofluids, as advanced heat transfer media. Much research has been carried out to investigate thermal management; However, existing reviews remain fragmented, often addressing thermal conductivity and convective heat transfer separately while overlooking the combined influence of magnetic field parameters. Moreover, waveform characteristics such as duty cycle remain largely unexplored, and no unified framework links microstructural dynamics with macroscopic performance. This review addresses these gaps by providing a comprehensive and integrated analysis of Fe₃O₄–water nanofluids, covering synthesis, stabilization, and thermal performance under magnetic fields. Synthesis techniques, including co-precipitation, thermal decomposition, and mechanical milling, are systematically evaluated in terms of particle size, production efficiency, and stability characteristics, with emphasis on surfactant-assisted dispersion, micelle formation, and zeta potential. Thermal conductivity enhancement is analysed using experimental methods such as the transient hot wire technique and theoretical models, including Maxwell, Hamilton–Crosser, and Bruggemann, alongside ANN-based approaches, which demonstrate superior predictive accuracy at increased volume fractions (>2 vol%), where classical models underestimate conductivity by approximately 5–15%. The effects of static and oscillating magnetic fields on thermal conductivity and convective heat transfer are critically examined, showing that oscillating fields enhance performance through particle agitation and thermal boundary layer disruption, achieving improvements in heat transfer coefficient of up to 37.5% within an optimal frequency range of 5–100 Hz, depending on flow conditions. Notably, this review identifies magnetic field duty cycle as a novel and previously unexplored tuning parameter and establishes a relationship between optimum field frequency and Reynolds number, providing a design-oriented framework for adaptive thermal control. Key limitations include the focus on Fe₃O₄–water systems, lack of standardized experimental protocols, and limited industrial-scale validation. Future directions include AI-driven magnetic field optimization, duty cycle control, and large-scale experimental implementation for next-generation heat transfer systems.