Optimisation studies on performance enhancement of spray cooling - machine learning approach

Performance Optimization of spray cooling heat transfer systems is identified as a significant step to improve the process efficiency, and the application of machine learning tools is the recent development to enhance the same. This study aims to maximize the heat transfer coefficient for spray cooling at low heat flux levels. The effects of nozzle inclination angle, water pressure, and spray height on heat transfer coefficient were studied. Taguchi L27 orthogonal array technique was used to perform the experiments. A maximum heat transfer coefficient of 181.4 kW/m2K was obtained at a nozzle inclination angle of 60o, spray height of 4 cm, and water pressure of 15 psi. Analysis of variance was performed to find the significance of each variable and its interactions. The results show that for the maximum heat transfer coefficient (181.4 kW/m2K), the optimum levels of the independent variables were A3H1P3, i.e., the highest level of nozzle inclination angle (60o), the lowest level of spray height (4cm), and the highest level of water pressure (15 psi). The support vector machine outperformed the Random Forest algorithm and Multiple Regression analysis regarding prediction accuracy with a maximum error of 0.15%, and root mean squared error of 0.01.