An experimental and optimization of heat transfer between two-disk systems

This research focuses on experimentally investigating and optimizing heat transfer between two parallel disks, a prevalent system extensively utilized in engineering devices and various machinery. Analyzing heat transfer in this configuration is of paramount interest to researchers and engineers. An experimental setup was built to explore heat transfer between two parallel disks. Local Nusselt number and average Nusselt number were calculated to analyze heat transfer characteristics. The study delved into the effects of vital parameters such as the gap ratio, Reynolds number, and heat flux on heat transfer between two parallel disks. The analysis revealed that the Nusselt number increases with an increase in the gap between two disks up to a certain level, beyond which an inverse effect is observed. Moreover, the Nusselt number demonstrates a positive correlation with the Reynolds number. An in-depth analysis of local and average Nusselt numbers indicated that heat flux initially has a positive effect, followed by an adverse effect after reaching a certain level. To ascertain the optimum solution, three different techniques were employed: Response Surface Method (RSM), Cuckoo Search Algorithm (CS), and Genetic Algorithm (GA). The predicted optimum values for the gap ratio, Reynolds number, and heat flux using GA, CS, and RSM were as follows: gap ratio (17.36, 17.36, and 17.36), Reynolds number (100, 100, and 98.2), and heat flux (689.36, 694.5, and 682.449), respectively. Correspondingly, the resulting average Nusselt numbers were projected to be 48.59, 48.36, and 48.5271. To validate the obtained results, experiments were conducted and compared with the predicted values. The comparison among these techniques indicated that all results fell within an acceptable margin of error. Specifically, RSM exhibited an error of 1.917%, CS showed an error of 0.962%, and GA displayed an error of 0.8931%.