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|Title:||Application of ANFIS and LSSVM strategies for estimating thermal conductivity enhancement of metal and metal oxide based nanofluids||Authors:||Razavi, R
Least square support vector machine algorithm
Adaptive neuro-fuzzy inference system
|Issue Date:||2019||Publisher:||Taylor & Francis||Source:||Engineering applications of computational fluid mechanics, 1 Jan. 2019, v. 13, no. 1, p. 560-578 How to cite?||Journal:||Engineering applications of computational fluid mechanics||Abstract:||An extensive variety of chemical engineering processes include the transfer of heat energy. Since increasing the effective contact surface is known as one of the popular manners to improve the efficiency of heat transfer, the attention to the nanofluids has been attracted. Due to the difficulty and high cost of an experimental study, researchers have been attracted to fast computational methods. In this work, Adaptive neuro-fuzzy inference system and least square support vector machine algorithms have been applied as a comprehensive predictive tool to forecast the nanofluids thermal conductivity in terms of diameter, temperature, the thermal conductivity of the base fluid, the thermal conductivity of nanoparticle and volume fraction. To this end, a large and comprehensive experimental databank contains 1109 data points have been collected from reliable sources. The particle swarm optimization is utilized to reach the best structures of the proposed algorithms. A comprehensive statistical and graphical investigations are carried out to prove the accuracy and ability of proposed models. In addition, the comparisons outputs indicate that the least square support vector machine algorithm has the best performance among the existing correlations and Adaptive neuro-fuzzy inference system algorithms for forecasting thermal conductivity of different nanofluids.||URI:||http://hdl.handle.net/10397/81301||ISSN:||1994-2060||EISSN:||1997-003X||DOI:||10.1080/19942060.2019.1620130||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 (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
The following publication Razieh Razavi, Aida Sabaghmoghadam, Amin Bemani, Alireza Baghban, Kwok-wing Chau & Ely Salwana (2019) Application of ANFIS and LSSVM strategies for estimating thermal conductivity enhancement of metal and metal oxide based nanofluids, Engineering Applications of Computational Fluid Mechanics, 13:1, 560-578 is available at https://dx.doi.org/10.1080/19942060.2019.1620130
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Citations as of Oct 22, 2019
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