The use of nanofluids as coolant fluid in the microchannel heat sink (MCHS) is an effective technique for the improvement of the thermal performance in electronic devices. A comparative study is performed with the help of a multi-objective genetic algorithm (MOGA) to find the optimal geometric variables of the MCHS and also to choose the appropriate nanofluid and its optimal characteristics. For that purpose, four practical nanofluids, including Al2O3-water, Cu-water, SiO2-water, and carbon nanotube (CNT) -water are thoroughly investigated. Simultaneously minimization of the total thermal resistance (Rtot) and also pumping power consumption (POW) are taken into account as the goal of the optimization, and the MOGA is employed to achieve the optimal solution. To check the accuracy of the thermal resistance modeling and assessing the optimization algorithm, several case studies with a different number of optimization variables are defined to investigate the capability of the algorithm in finding the optimal microchannel design variables and choosing the suitable nanofluid. The optimization variables consist of the channel aspect and wall ratios, base thickness, the volume nanoparticle concentration, the nanoparticle diameter, and finally, the volume flow rate. Compared to other nanofluids, CNT provides better thermal performance. Increasing the concentration of nanoparticles enhances thermal performance, which can also be achieved through the reduction of nanoparticle diameter. Results of the considered case studies also show that with consideration of more design variables through the optimization procedure, the possibility of better thermal performance is achievable.&nbsp;The present study aims to upgrade the thermal performance of the MCHS by extending a variety of design parameters in a multi-objective optimization problem which will be carried out by the genetic algorithm. This in-depth investigation is conducted here for the first time to shed light on the role of the above-mentioned parameters in the thermal improvement of the MCHS. For that purpose, one-dimensional analytical modeling is performed to analyze the heat transfer of the MCHS. Afterward, an optimization study is fully pursued by the MOGA to find the Pareto front of the optimization analysis. The results of the multi-objective optimization were summarized along the Pareto front, where the final results were selected by the definition of the thermal performance design point as the optimal domain. The primary conclusions of the study can be listed as:1. Comparing the results of the first case with the pure water, indicated that using nanofluids as the coolant fluid considerably improves the thermal performance of the MCHS.2. The CNT as a coolant fluid in the MCHS provided better thermal performance in comparison with other nanofluids in all three cases, whereas other nanofluids had approximately the same thermal performance.3. The results of the considered cases revealed that the increase in the number of optimization variables led to achieving better thermal performance.&nbsp;&nbsp;4. Increasing the concentration of nanoparticles for CNT leads to an increase in the number of optimal solutions, improving the thermal performance of nanofluid-cooled MCHS.5. Reducing the nanoparticle diameter of CNT led to improve the thermal performance of the nanofluid-cooled MCHS.6. The lower value of the flow rate (here 2 m3/s) did not improve the thermal performance because none of the results of the Pareto front for this flow rate are within the optimal domain. Among all the considered flow rates, the value of 4 m3/s has the most optimal results.&nbsp;

Multi-Objective Thermal Optimization of a Nanofluid-Cooled Rectangular Microchannel Heat Sink Using Genetic Algorithm: A Comparative Study

Paper Proceedings: https://asmedigitalcollection.asme.org/IMECE/IMECE2021/volume/85543

ASME 2021 International Mechanical Engineering Congress and Exposition (IMECE2021)

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