The microchannel heat sink (MCHS) has become the most relevant micro-heat exchanger for a small area in need of an effective high heat removal system. However, heat dissipation from the microchips where the MCHS is utilized -the microprocessor and microcontroller -are getting higher with the sizes getting smaller. A more effective coolant is needed to address the increasing heat load from the microchip and nanofluid, nanosized particles dispersed in a base fluid, is among those explored. This paper reports a new potential use of non-metallic nanofluid, boron nitride nanotube (BNN) that is capable of improving the overall performance of a rectangular MCHS. A heuristic method, multi-objective genetic algorithm (MOGA), is employed to simultaneously minimize the thermal resistance and pressured drop of a boron nitride nanotubes (BNN) nanofluid-cooled MCHS to obtain optimized dimensions at various weight concentrations. The method is capable in achieving conflicting objectives, minimization of the thermal resistance and the pressure drop; an increase in the former decreases the latter and vice versa. In addition, experimental thermophysical properties of the BNN nanofluid are used to provide reliability to the optimization outcomes in identifying the best BNN concentration for cooling of a MCHS at 50°C. The optimization results showed that as the thermal resistance decreases, the pressure drop decreases.
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