Predicting the thermal performance of pulsating heat pipes using artificial neural networks

The present work proposes a promising approach to predict the thermal performance of Pulsating Heat Pipes (PHPs) using Artificial Neural Networks (ANN). The available database corresponds to 1097 experimental records with nine distinct geometries conceived in the frame of the Clean Sky2 project PHP2. According to the transient data, for 12.5% of cases, PHPs failed to operate in a pulsating mode and instead exhibited conduction behavior. Thus, the proposed approach consists of separating the database into 'Conduction' and 'Pulsation' and training three ANN models. First, a classification model is constructed to predict the operating mode. The F1-score, used to examine the accuracy of this model, resulted in 96.35 % for the pulsation category. Then, a regression model is created for each mode to predict the thermal resistance. The mean relative error of regression models resulted in an acceptable error of 11.48% and 40.93% for pulsating and conduction modes, respectively.