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 journalArticlepeer-review

28 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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