Applying GMDH neural network to estimate the thermal resistance and thermal conductivity of pulsating heat pipes

Abstract

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 a GMDH (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 R-2 tests and resulted in 0.9779 and 0.9906 for thermal resistance and effective thermal conductivity, respectively.