Thermal performance of pulsating heat pipes (PHPs) is dependent to several factors. Inner and outer
diameter of tube, filling ratio, thermal conductivity, heat input, inclination angle, and length of each
section are the most influential factors in the design process of PHPs. Since water is a conventional
working fluid for PHPs, thermal resistance and effective thermal conductivity of PHPs filled with water
are modeled by applying aGMDH(group method of data handling) neural network. The input data of
the GMDH model are collected from other experimental investigations to predict the physical properties
including thermal resistance and effective thermal conductivity of PHPs filled with water as
working fluid. The accuracy of the introduced models are examined through the R2 tests and resulted
in 0.9779 and 0.9906 for thermal resistance and effective thermal conductivity, respectively.
