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Title: Modeling thermal conductivity of ethylene glycol-based nanofluids using multivariate adaptive regression splines and group method of data handling artificial neural network
Authors: Alotaibi, S
Amooie, MA
Ahmadi, MH
Nabipour, N
Chau, KW 
Issue Date: 2020
Source: Engineering applications of computational fluid mechanics, 2020, v. 14, no. 1, p. 379-390
Abstract: Augmenting the thermal conductivity (TC) of fluids makes them more favorable for thermal applications. In this regard, nanofluids are suggested for achieving improved heat transfer owing to their modified TC. The TC of the base fluid, the volume fraction and mean diameter of particles, and the temperature are the main elements influencing the TC of nanofluids. In this article, two approaches, namely multivariate adaptive regression splines (MARS) and group method of data handling (GMDH), are applied for forecasting the TC of ethylene glycol-based nanofluids containing SiC, Ag, CuO, , and MgO particles. Comparison of the data forecast by the models with experimental values shows a higher level of confidence in GMDH for modeling the TC of these nanofluids. The values determined using MARS and GMDH for modeling are 0.9745 and 0.9332, respectively. Moreover, the importance of the inputs is ranked as volume fraction, TC of the solid phase, temperature and particle dimensions.
Keywords: Nanofluid
Thermal conductivity
Artificial neural network
Publisher: Taylor & Francis
Journal: Engineering applications of computational fluid mechanics 
ISSN: 1994-2060
EISSN: 1997-003X
DOI: 10.1080/19942060.2020.1715843
Rights: © 2020 The Author(s).
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 Sorour Alotaibi, Mohammad Ali Amooie, Mohammad Hossein Ahmadi, NarjesNabipour & Kwok-wing Chau (2020) Modeling thermal conductivity of ethylene glycol-basednanofluids using multivariate adaptive regression splines and group method of data handlingartificial neural network, Engineering Applications of Computational Fluid Mechanics, 14:1,379-390 is available at
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