Explicit calculation of the friction factor in pipeline flow of Bingham plastic fluids: A neural network approach

Shyam S. Sablani, Walid H. Shayya, Anvar Kacimov

Research output: Contribution to journalArticle

30 Citations (Scopus)

Abstract

An artificial neural network (ANN) approach was used in this paper to develop an explicit procedure for calculating the friction factor, f, under both laminar and turbulent flow conditions of Bingham plastic fluids in closed conduits and pipe networks. The procedure aims at reducing the computational efforts as well as eliminating the need for conducting complex and time-consuming iterative solutions of the governing implicit equations for calculating the friction factor, f. The ANN approach involved the establishment of an explicit relationship among the Reynolds number, Re, Hedstrom number, He, and the friction factor, f, under both laminar and turbulent flow conditions. Although, an analytical solution of the governing equation under the laminar flow regime was also feasible (such an equation is also provided in this paper), the ANN model is applicable under both laminar and turbulent flow conditions where the analytical approach will have major limitations (especially when considering the implicit equation that govern the turbulent flow regime).

Original languageEnglish
Pages (from-to)99-106
Number of pages8
JournalChemical Engineering Science
Volume58
Issue number1
DOIs
Publication statusPublished - Jan 2003

Fingerprint

Pipe flow
Laminar flow
Turbulent flow
Friction
Plastics
Neural networks
Fluids
Reynolds number
Pipe

Keywords

  • Fluid mechanics
  • Food processing
  • Hydraulic analysis
  • Modeling
  • Non-iterative procedure
  • Non-Newtonian fluids

ASJC Scopus subject areas

  • Chemical Engineering(all)

Cite this

Explicit calculation of the friction factor in pipeline flow of Bingham plastic fluids : A neural network approach. / Sablani, Shyam S.; Shayya, Walid H.; Kacimov, Anvar.

In: Chemical Engineering Science, Vol. 58, No. 1, 01.2003, p. 99-106.

Research output: Contribution to journalArticle

@article{3067dcb9dc1a414b8575be9778ec1175,
title = "Explicit calculation of the friction factor in pipeline flow of Bingham plastic fluids: A neural network approach",
abstract = "An artificial neural network (ANN) approach was used in this paper to develop an explicit procedure for calculating the friction factor, f, under both laminar and turbulent flow conditions of Bingham plastic fluids in closed conduits and pipe networks. The procedure aims at reducing the computational efforts as well as eliminating the need for conducting complex and time-consuming iterative solutions of the governing implicit equations for calculating the friction factor, f. The ANN approach involved the establishment of an explicit relationship among the Reynolds number, Re, Hedstrom number, He, and the friction factor, f, under both laminar and turbulent flow conditions. Although, an analytical solution of the governing equation under the laminar flow regime was also feasible (such an equation is also provided in this paper), the ANN model is applicable under both laminar and turbulent flow conditions where the analytical approach will have major limitations (especially when considering the implicit equation that govern the turbulent flow regime).",
keywords = "Fluid mechanics, Food processing, Hydraulic analysis, Modeling, Non-iterative procedure, Non-Newtonian fluids",
author = "Sablani, {Shyam S.} and Shayya, {Walid H.} and Anvar Kacimov",
year = "2003",
month = "1",
doi = "10.1016/S0009-2509(02)00440-2",
language = "English",
volume = "58",
pages = "99--106",
journal = "Chemical Engineering Science",
issn = "0009-2509",
publisher = "Elsevier BV",
number = "1",

}

TY - JOUR

T1 - Explicit calculation of the friction factor in pipeline flow of Bingham plastic fluids

T2 - A neural network approach

AU - Sablani, Shyam S.

AU - Shayya, Walid H.

AU - Kacimov, Anvar

PY - 2003/1

Y1 - 2003/1

N2 - An artificial neural network (ANN) approach was used in this paper to develop an explicit procedure for calculating the friction factor, f, under both laminar and turbulent flow conditions of Bingham plastic fluids in closed conduits and pipe networks. The procedure aims at reducing the computational efforts as well as eliminating the need for conducting complex and time-consuming iterative solutions of the governing implicit equations for calculating the friction factor, f. The ANN approach involved the establishment of an explicit relationship among the Reynolds number, Re, Hedstrom number, He, and the friction factor, f, under both laminar and turbulent flow conditions. Although, an analytical solution of the governing equation under the laminar flow regime was also feasible (such an equation is also provided in this paper), the ANN model is applicable under both laminar and turbulent flow conditions where the analytical approach will have major limitations (especially when considering the implicit equation that govern the turbulent flow regime).

AB - An artificial neural network (ANN) approach was used in this paper to develop an explicit procedure for calculating the friction factor, f, under both laminar and turbulent flow conditions of Bingham plastic fluids in closed conduits and pipe networks. The procedure aims at reducing the computational efforts as well as eliminating the need for conducting complex and time-consuming iterative solutions of the governing implicit equations for calculating the friction factor, f. The ANN approach involved the establishment of an explicit relationship among the Reynolds number, Re, Hedstrom number, He, and the friction factor, f, under both laminar and turbulent flow conditions. Although, an analytical solution of the governing equation under the laminar flow regime was also feasible (such an equation is also provided in this paper), the ANN model is applicable under both laminar and turbulent flow conditions where the analytical approach will have major limitations (especially when considering the implicit equation that govern the turbulent flow regime).

KW - Fluid mechanics

KW - Food processing

KW - Hydraulic analysis

KW - Modeling

KW - Non-iterative procedure

KW - Non-Newtonian fluids

UR - http://www.scopus.com/inward/record.url?scp=0037221814&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0037221814&partnerID=8YFLogxK

U2 - 10.1016/S0009-2509(02)00440-2

DO - 10.1016/S0009-2509(02)00440-2

M3 - Article

AN - SCOPUS:0037221814

VL - 58

SP - 99

EP - 106

JO - Chemical Engineering Science

JF - Chemical Engineering Science

SN - 0009-2509

IS - 1

ER -