As electrical devices become smaller, it is essential to maintain operating temperature for safety and durability. Therefore, there are efforts to improve heat transfer performance under various conditions, such as using extended surfaces and nanofluids. Among them, cooling methods using ferrofluid are drawing the attention of many researchers. This fluid can control the movement of the fluid in magnetic fields. In this study, the heat transfer performance of a fin-tube heat exchanger, using ferrofluid as a coolant, was analyzed when external magnetic fields were applied. Permanent magnets were placed outside the heat exchanger. When the magnetic fields were applied, a change in the thermal boundary layer was observed. It also formed vortexes, which affected the formation of flow patterns. The vortex causes energy exchanges in the flow field, activating thermal diffusion and improving heat transfer. A numerical analysis was used to observe the cooling performance of heat exchangers, as the strength and number of the external magnetic fields were varying. VGs (vortex generators) were also installed to create vortex fields. A convective heat transfer coefficient was calculated to determine the heat transfer rate. In addition, the comparative analysis was performed with graphical results using contours of temperature and velocity.
In this study, experiments, simulations, and optimization were performed to evaluate heat transfer performance of ferrofluids. Ferrofluids are colloidal suspensions containing magnetic-nano particles with a diameter of 5 to 15 nm in a base fluid such as oil or water. Recently, as many devices are miniaturized, the design of heat dissipation systems are being diversified to consider cost and safety, and it is becoming important to separate an ancillary device for cooling from main unit. In ferrofluids, the behavior and vortex of magnetic-nano particles are actively generated by an external magnetic field, and the cooling system can be designed in a simplified manner by using this characteristic. The main design parameter is the arrangement of permanent magnets, and the output variable is the temperature inside the magnetic nanofluid. The permanent magnet can be moved up and down, and the temperature inside the magnetic nanofluid was measured at various locations. A predictive model was created using a design of experiments (DOE) and response surface method (RSM) using selected design and temperature variables. Based on the generated regression model, an optimization was applied to find a permanent magnet arrangement that maximizes heat transfer performance. Through the optimization technique used in this study, economic efficiency in terms of time and cost was obtained by reducing the number of experiments.
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