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Title: Applying GMDH neural network to estimate the thermal resistance and thermal conductivity of pulsating heat pipes
Authors: Ahmadi, MH
Sadeghzadeh, M
Raffiee, AH
Chau, KW 
Issue Date: 2019
Source: Engineering applications of computational fluid mechanics, 1 Jan. 2019, v. 13, no. 1, p. 327-336
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.
Keywords: Pulsating heat pipe
Thermal resistance
Effective thermal conductivity
Publisher: Taylor & Francis
Journal: Engineering applications of computational fluid mechanics 
ISSN: 1994-2060
EISSN: 1997-003X
DOI: 10.1080/19942060.2019.1582109
Rights: © 2019 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
The following publication Mohammad Hossein Ahmadi, Milad Sadeghzadeh, Amir Hossein Raffiee &Kwok-wing Chau (2019) Applying GMDH neural network to estimate the thermal resistance andthermal conductivity of pulsating heat pipes, Engineering Applications of Computational FluidMechanics, 13:1, 327-336 is available at
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