Application of ANFIS and LSSVM strategies for estimating thermal conductivity enhancement of metal and metal oxide based nanofluids. Issue 1 (1st January 2019)

• Record Type: Journal Article
• Authors: Razavi, Razieh; Sabaghmoghadam, Aida; Bemani, Amin; Baghban, Alireza; Chau, Kwok-wing; Salwana, Ely
• Is Part Of: Engineering applications of computational fluid mechanics. Volume 13:Issue 1(2018)
• Page Start: 560
• Page End: 578
• Publication Date: 2019-01-01
• Subjects: Nanofluid -- thermal conductivity -- least square support vector machine algorithm -- Adaptive neuro-fuzzy inference system; Computational fluid dynamics -- Periodicals; 620.10640285
• DOI: 10.1080/19942060.2019.1620130
• Languages: English
• ISSNs: 1994-2060

Abstract

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.