Pulsating heat pipes (PHPs) are compact and efficient devices in heat transfer which are applicable for
several purposes. The thermal resistance of PHPs depends on several parameters. In the present study,
four models including multilayer perceptron (MLP), radial bias function (RBF), conjugated hybrid of particle
swarm optimization and adaptive neuro-fuzzy inference system (CHPSO ANFIS) are applied to predict
the thermal resistance of pulsating heat pipes filled with ethanol. The obtained results indicated that
the radial bias function (RBF) model had the highest accuracy among the applied models and can predict
the thermal resistance of the PHPs precisely. The R-squared and root mean squared error (RMSE) values
for this model were 0.9892 and 0.0650, respectively.
