Thermal conductivity and dynamic viscosity modeling of Fe2O3/water nanofluid by applying various connectionist approaches

Mohammad Hossein Ahmadi, Afshin Tatar, Parinaz Seifaddini, Mahyar Ghazvini, Roghayeh Ghasempour, Mikhail A. Sheremet

Research output: Contribution to journalArticle

26 Citations (Scopus)

Abstract

Thermal conductivity and dynamic viscosity play key role in heat transfer capacity of nanofluids. In the present study, thermal conductivity and dynamic viscosity of Fe2O3/water are modeled by applying various artificial neural network algorithms. The applied algorithms are MLP, GA-RBF, LSSVM, and CHPSO ANFIS algorithms. The data for modeling procedure are extracted from several experimental studies. Obtained results by the different algorithms are compared and it was concluded that the highest R-squared values belonged to GA-RBF algorithm which were equal to 0.9962 and 0.9982 for thermal conductivity ratio and dynamic viscosity, respectively.

Original languageEnglish
Pages (from-to)1301-1322
Number of pages22
JournalNumerical Heat Transfer; Part A: Applications
Volume74
Issue number6
DOIs
Publication statusPublished - 17 Sep 2018

ASJC Scopus subject areas

  • Numerical Analysis
  • Condensed Matter Physics

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