Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/81301
PIRA download icon_1.1View/Download Full Text
Title: Application of ANFIS and LSSVM strategies for estimating thermal conductivity enhancement of metal and metal oxide based nanofluids
Authors: Razavi, R
Sabaghmoghadam, A
Bemani, A
Baghban, A
Chau, KW 
Salwana, E
Issue Date: 2019
Source: Engineering applications of computational fluid mechanics, 1 Jan. 2019, v. 13, no. 1, p. 560-578
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.
Keywords: Nanofluid
Thermal conductivity
Least square support vector machine algorithm
Adaptive neuro-fuzzy inference system
Publisher: Taylor & Francis
Journal: Engineering applications of computational fluid mechanics 
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
Appears in Collections:Journal/Magazine Article

Files in This Item:
File Description SizeFormat 
Razavi_Application_ANFIS_LSSVM.pdf5.83 MBAdobe PDFView/Open
Open Access Information
Status open access
File Version Version of Record
Access
View full-text via PolyU eLinks SFX Query
Show full item record

Page views

77
Citations as of May 15, 2022

Downloads

78
Citations as of May 15, 2022

SCOPUSTM   
Citations

36
Citations as of May 12, 2022

WEB OF SCIENCETM
Citations

36
Citations as of May 12, 2022

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.