Optimizing microchannel heat sinks with rhomboid vortex generators: An artificial neural network approach and its application in superconducting synchronous condensers

Microchannel heat sinks (MCHSs) have demonstrated their significance in various industrial applications due to their efficient cooling capabilities. Particularly in power systems, they emerge as a potential cooling solution for critical equipment such as superconducting synchronous condensers (SSCs), which is crucial for addressing the increasing challenges of power density and thermal management. This study proposes an optimization model for MCHSs based on an artificial neural network (ANN). By altering the horizontal distance (dh), vertical distance (dv), and placement angle (theta) of the rhomboid vortex generators (RVGs), the ANN model is utilized to determine the Nusselt number (Nu) and pressure drop (Delta P) for each MCHS optimization scheme. These results are then compared with numerical simulation outcomes to achieve the objectives of both ideal thermal design (ITD) and ideal overall design (IOD). The findings indicate that the thermal performance of MCHSs is most significantly influenced by the placement angle theta. Compared to the design in the referenced literature, the thermal performance of MCHSs was improved by 37.8 % and 38.9 % with the ITD and IOD designs, respectively. Furthermore, thermal behavior numerical calculations were conducted on an SSC integrated with the optimized