Application of Artificial Neural Networks in Predicting the Thermal Performance of Heat Pipes

The loss of energy by heat is a common problem in almost all areas of industry, and heat pipes are essential to increase efficiency and reduce energy waste. However, in many cases, they have complex theoretical equations with high percentages of error, limiting their development and causing dependence on empirical methods that generate a waste of time and material, resulting in significant expenses and reducing the viability of their use. Thus, Artificial Neural Networks (ANNs) can be an excellent option to facilitate the construction and development of heat pipes without knowledge of the complex theory behind the problem. This investigation uses experimental data from previous studies to evaluate the ability of three different ANNs to predict the thermal performance of heat pipes with different capillary structures, each of them in various configurations of the slope, filling ratio, and heat load. The goal is to investigate results in as many different scenarios as possible to clearly understand the networks’ capacity for modeling heat pipes and their operating parameters. We chose two classic ANNs (the most used, Multilayer Perceptron (MLP) network, and the Radial Basis Function (RBF) network) and the Extreme Learning Machine (ELM), which has not yet been applied to heat pipes studies. The ELM is an Unorganized Machine with a fast training process and a simple codification. The ANN results were very close to the experimental ones, showing that ANNs can successfully simulate the thermal performance of heat pipes. Based on the RMSE (error metric being reduced during the training step), the ELM presented the best results (RMSE = 0.384), followed by MLP (RMSE = 0.409), proving their capacity to generalize the problem. These results show the importance of applying different ANNs to evaluate the system deeply. Using ANNs in developing heat pipes is an excellent option for accelerating and improving the project phase, reducing material loss, time, and other resources.