Multi-objective hydrothermal performance optimization of a microchannel heat sink equipped with delta winglet vortex generators using NSGA-II genetic algorithm

Microchannel heat sinks provide the solution to the ever-increasing heat flux generated from micro-electric components. In this study, performance optimization of a microchannel heat sink with delta winglet vortex generators was carried out based on the data obtained from numerical CFD simulations. A total of 192 design points were generated by altering the fluid velocity in terms of Reynolds number (Re), winglet width (Wd), length (Ld), and the angle of attachment (β) of the winglet. The Artificial Neural Network (ANN) model coupled with Non-dominated Sorting Genetic Algorithm NSGA-II was used simultaneously to minimize the friction factor and increase the Nusselt number. The ANN model predicted the output values within the error limit of 10%. The Pareto optimal front generated by the algorithm contains the input parameters in the range 982 < Re < 988, 177 μm < Ld < 233 μm, 10 μm < Wd < 25 μm, 57° < β < 64°. Decision-making methods TOPSIS, LINMAP, and Shannon entropy were employed to calculate the optimal solution from the data set and the obtained points showcased 70%, 120% and 158% surge in Nusselt number while an increase of friction factor by 35%, 109%, and 140% respectively is reported. The Performance Evaluation Criteria (PEC) values obtained from the best solutions were 1.52, 1.72, and 1.92, respectively. Furthermore, the accuracy of the optimal solutions was verified numerically. The flow and thermal field of the microchannel are also analyzed, and results showed that the angle of attachment and width of the winglet played a crucial role in the overall performance.