Predicting of thermal resistances of closed vertical meandering pulsating heat pipe using artificial neural network model

Abstract

It is very important to develop a reliable method for the application of the pulsating heat pipe (PHP), however, the currently proposed heat transfer correlations or theoretical models still have some apparent shortcomings, such as great deviation and poor flexibility. Considering the advantages of artificial neural network (ANN) in analyzing complex systems, a fully connected feed forward ANN model was used to predict the thermal resistance of a closed vertical meandering PHP with water. The Number of turns, filling ratio, heat flux, inner diameter and the length ratio of evaporation section were selected as the input parameters. By applying a trial-and-error method, the neuron number in the hidden layer was optimized to be 10. A total of 221 points of experimental data under different working conditions collected from the published literature were applied to build the ANN model. The results showed a good agreement between the experimental data and the ANN model with the MSE and correlation coefficient of 0.0025 and 0.9962, respectively. Furthermore, the influence of the heat flux on the relative deviation was also investigated. It suggested that the ANN model could have better prediction results when the heat flux was within the range of 6500–14,500 W/m2.