No abstract

This paper reports the influence of surfactant Triton X-100 on boron nitride nanotubes (BNNTs) nanofluid in non-optimized and optimized microchannel heat sink (MCHS) at 30⁰ C and 50⁰ C. The MCHS performance was evaluated in terms of thermal resistance and pressure drop, utilizing experimental thermophysical properties of distilled water, a mixture of distilled water and surfactant Triton X-100 as base fluid, and nanofluid BNNTs at weight concentration of 0.001% into MCHS models which further optimized with the Multiple Objective Particle Swarm Optimization (MOPSO) technique. It is found that the surfactant at 30⁰ C improves MCHS thermal capabilities without nanotubes by 0.8% even after optimizing MCHS according to the fluid properties. Conversely, surfactant Triton X-100 reduces pressure drop greatly with any change in thermal resistance at 50⁰ C and paired cooperatively with BNNTs nanofluid 0.001wt.% -mitigating pressure drop increment caused by the nanofluid resulting an overall performance improvement by 1.25% and 1.97% for thermal resistance and pressure drop respectively in MCHS systems and reduced to 1.3% and 3.2% after optimization. Optimized MCHS dimensions given by MOPSO could be manufactured and additionally gave wider solutions for large reduction of pressure drop up to 80% for economic MCHS with a drawback of higher thermal resistance.

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.

This paper reports on the different modeling approach of the total thermal resistance in a microchannel heat sink (MCHS); with wall resistance and the frequently used fin model, in comparison with experimental results. For a single stack MCHS, the wall model caused more than 10% difference but it can be extended to a stacked MCHS while the fin model could not, due to the adiabatic top condition. The wall resistance model is idealized, assuming a 100% efficient convective heat transfer while in the fin model 70% was the maximum. Meanwhile, stacking showed that at a constant flow rate, the thermal resistance could be reduced by 3% for a double stack, while increasing beyond that will decrease the thermal performance of the MCHS. The study showed the limits of models used and possible stacking of a MCHS for improved heat removal capability.

No abstract