Development of a novel artificial neural network model for closed pulsating heat pipe with water and aqueous solutions

Abstract

Aqueous solutions are increasingly introduced as the working fluids of pulsating heat pipe (PHP), to balance and optimise certain thermo-physical properties of pure water. In this paper, a first attempt is conducted to predict the heat transfer performance of closed PHP with pure water and available aqueous solutions based on a fully connected feed-forward artificial neural network (ANN). The notable dimensionless numbers, Kutateladze number (Ku), Bond number (Bo), Morton number (Mo), Jackob number (Ja), Prandtl number (Pr), Laplace number (La), d/L e and number of turns (N) were selected as the inputs of the ANN model. It was found that the prediction of proposed ANN model posed a good agreement with experimental data with the MSE and correlation coefficient of 0.026 and 0.979, respectively. For 81.02% of the collected data, the absolute deviation of prediction for thermal resistance was within 25%. For 95% of collected data, the prediction of thermal resistance and temperature difference between the evaporation and condensation section fell within ±0.37 K/W, and ±17.1 K, respectively. The evaluation of evaporation and condensation section temperatures was also presented based on the proposed ANN model.