Applicability of connectionist methods to predict dynamic viscosity of silver/water nanofluid by using ANN-MLP, MARS and MPR algorithms

Mohammad Hossein Ahmadi, Behnam Mohseni-Gharyehsafa, Mahmood Farzaneh-Gord, Ravindra D. Jilte, Ravinder Kumar, Kwok wing Chau

Research output: Journal article publicationJournal articleAcademic researchpeer-review

44 Citations (Scopus)


Dynamic viscosity considerably affects the heat transfer and flow of fluids. Due to improved thermophysical properties of fluids containing nanostructures, these types of fluids are widely employed in thermal mediums. The nanofluid's dynamic viscosity relies on different variables including size of solid phase, concentration and temperature. In the present study, three algorithms including multivariable polynomial regression (MPR), artificial neural network–multilayer perceptron (ANN-MLP) and multivariate adaptive regression splines (MARS) are applied to model the dynamic viscosity of silver (Ag)/water nanofluid. Recently published experimental investigations are employed for data extraction. The input variables considered in the modeling process to be the most important ones are the size of particles, fluid temperature and the concentration of Ag nanoparticles in the base fluid. The R 2 values for the studied models are 0.9998, 0.9997 and 0.9996 for the ANN-MLP, MARS and MPR algorithms, respectively. In addition, based on importance analysis, the temperature is highly effective and the dominant parameter for the dynamic viscosity of the nanofluid in comparison with size and concentration.

Original languageEnglish
Pages (from-to)220-228
Number of pages9
JournalEngineering Applications of Computational Fluid Mechanics
Issue number1
Publication statusPublished - 1 Jan 2019


  • artificial neural network
  • concentration
  • dynamic viscosity
  • multivariable polynomial regression (MPR)
  • multivariate adaptive regression splines (MARS)
  • nanofluid

ASJC Scopus subject areas

  • Computer Science(all)
  • Modelling and Simulation

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