Estimating thermal performance of thermosyphons by artificial neural networks - Pedro L.O. Machado; Thomas S. Pereira; Marcio G. Trindade; Felipe M. Biglia; Paulo H.D. Santos; Yara S. Tadano; Hugo Siqueira; Thiago Antonini Alves (Alexandria Engineering Journal, 2023)
The overheating of electronic devices has become very common with the advancement of technology, requiring the development of new alternatives for thermal control. Due to their excellent heat transfer ability, no external power is needed and they are adaptable to different geometries and applications. Thermosyphons are an excellent alternative for this thermal control. The thermal performance of thermosyphons is usually evaluated by their thermal resistance, and several variables were investigated to understand their influence on this parameter, such as working fluid, filling ratio, and slope. In that way, the fact that the thermal resistance depends on several variables makes its prediction complex and time-consuming. To overcome this issue, artificial intelligence-based methods, such as Artificial Neural Networks (ANNs), could be used. In this sense, an experimental investigation of the thermal performance of thermosyphon under different filling ratios (20 to 100%), slopes (45 and 90°), and heat loads (5 to 45 W) was made. The experimental data were then used as the database for different ANNs to predict the thermal resistance of a thermosyphon. For the experimental investigation, a thermosyphon of copper tube was built of 9.45 mm and 7.75 mm outer and inner diameter and a length of 500 mm. Its regions, evaporator, adiabatic section, and condenser, had 80, 20, and 100 mm, respectively. Distilled water was used as the working fluid. The evaporator was heated due to the Joule’s effect resulting from power dissipation in an electric ribbon wrapped in its length. The condenser was cooled with a 5 m/s air-forced convection. Regarding the use of ANNs, Unorganized Machines (UMs), composed of Extreme Learning Machines (ELM) and Echo State Networks (ESN), were proposed. As a means of comparison, the Radial Basis Function Network, and the Multilayer Perceptron (MLP), the most widely known neural architecture in the literature, were also applied. To estimate the thermal resistance of thermosyphon, the filling ratio, slope, and heat load were considered as inputs, and a total of 67 samples were used. Experimental results indicated that the best thermal performance occurs at a filling ratio of 40%, while the slope of 45° presented a better performance than 90°. The computational results revealed that the UMs could overcome the other methods, especially the ESN. The difference between the predicted and the experimental values was up to 25% for almost all cases. As a matter of reducing the experimental tests, applying ANN was essential.