Towards experimental and modeling study of heat transfer performance of water- SiO2 nanofluid in quadrangular cross-section channels

Alireza Baghban, Jafar Sasanipour, Fathollah Pourfayaz, Mohammad Hossein Ahmadi, Alibakhsh Kasaeian, Ali J. Chamkha, Hakan F. Oztop, Kwok wing Chau

Research output: Journal article publicationJournal articleAcademic researchpeer-review

16 Citations (Scopus)

Abstract

Nanofluids have found extended applications in different industrial and engineering systems nowadays. This study aims to investigate the accurate prediction of SiO2 nanofluid effect on the heat transfer performance, specifically convective heat transfer coefficient (H), of a quadrangular cross-section channel by considering affecting fluid flow specifications factors of Re, Pr, and concentration of nanoparticles (x) in the employing working fluid. An experimental setup is used to prepare a database consisting of 270 data points on the H, of SiO2 nanofluids. These data are then applied to develop predictive models based on three intelligent algorithms, namely multi-layer perceptron (MLP), adaptive neuro-fuzzy inference system (ANFIS), and least squares support vector machine (LSSVM), respectively. Graphical and statistical error criterions are carried out to evaluate the credibility of the proposed approaches. The LSSVM method had the precise performance regarding the mean squared error (MSE) and the coefficient of determination (R2) of 59.7 and 0.9992, respectively. A sensitivity analysis is also carried out to assess the impact of different parameters on the H demonstrating that the Prandtl number has the highest impact with a relevancy factor (r) of 0.524.

Original languageEnglish
Pages (from-to)453-469
Number of pages17
JournalEngineering Applications of Computational Fluid Mechanics
Volume13
Issue number1
DOIs
Publication statusPublished - 1 Jan 2019

Keywords

  • Convective heat transfer coefficient
  • cross-section channels
  • intelligent approaches
  • nanofluid
  • optimization

ASJC Scopus subject areas

  • Computer Science(all)
  • Modelling and Simulation

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