Prediction of terminal velocity of solid spheres falling through Newtonian and non-Newtonian pseudoplastic power law fluid using artificial neural network

R. Rooki, F. Doulati Ardejani, A. Moradzadeh, V. C. Kelessidis, M. Nourozi

Research output: Contribution to journalArticle

14 Citations (Scopus)

Abstract

Prediction of the terminal velocity of solid spheres falling through Newtonian and non-Newtonian fluids is required in several applications like mineral processing, oil well drilling, geothermal drilling and transportation of non-Newtonian slurries. An artificial neural network (ANN) is proposed which predicts directly the terminal velocity of solid spheres falling through Newtonian and non-Newtonian power law liquids from the knowledge of the properties of the spherical particle (density and diameter) and of the surrounding liquid (density and rheological parameters). With a combination of non-Newtonian data with Newtonian data taken from published data giving a database of 88 sets, an artificial neural network is designed. Analysis of the predictions shows that the artificial neural network could be used with good engineering accuracy to directly predict the terminal velocity of solid spheres falling through Newtonian and non-Newtonian power law liquids covering an extended range of power law values from 1.0 down to 0.06.

Original languageEnglish
Pages (from-to)53-61
Number of pages9
JournalInternational Journal of Mineral Processing
Volume110-111
DOIs
Publication statusPublished - 18 Jul 2012
Externally publishedYes

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Keywords

  • Artificial neural network
  • Drilling cuttings transport
  • Mineral processing
  • Newtonian and power law fluid
  • Terminal velocity

ASJC Scopus subject areas

  • Geotechnical Engineering and Engineering Geology
  • Geochemistry and Petrology

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