Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
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Liquid cooling electronics using microchannels integrated in the chips is an attractive alternative to bulky aluminum heat sinks. Cooling can be further enhanced using nanofluids. The goals of this study are to evaluate heat transfer in a nanofluid heat sink with developing laminar flow forced convection, taking into account the pumping power penalty. The proposed model uses semiempirical correlations to calculate effective nanofluid thermophysical properties, which are then incorporated into heat transfer and friction factor correlations in literature for single-phase flows. The model predicts the thermal resistance and pumping power as a function of four design variables that include the channel diameter, velocity, number of channels, and nanoparticle fraction. The parameters are optimized with minimum thermal resistance as the objective function and fixed specified value of pumping power as the constraint. For a given value of pumping power, the benefit of nanoparticle addition is evaluated by independently optimizing the heat sink, first with nanofluid and then with water. Comparing the minimized thermal resistances revealed only a small benefit since nanoparticle addition increases the pumping power that can alternately be diverted towards an increased velocity in a pure water heat sink. The benefit further diminishes with increase in available pumping power.