A study of friction factor formulation in pipes using artificial intelligence techniques and explicit equations

Farzin SALMASI; Rahman KHATIBI; Mohammad Ali GHORBANI

Article Details

A study of friction factor formulation in pipes using artificial intelligence techniques and explicit equations

Authors:

Farzin SALMASI | Rahman KHATIBI | Mohammad Ali GHORBANI

Publisher:

TUBITAK

Journal:

Turkish Journal of Engineering and Environmental Sciences 2012 Vol.36 Issue 2, pp.121-138

Document Type:

Article

Publication Date:

1/2012

Document Language:

Turkish

Document ID (Org No.):

2000744619

Frequency:

Tri-annual

eISSN:

1303-6157

pISSN:

1300-0160

Abstract:

 

The hydraulic design and analysis of flow conditions in pipe  networks are dependent upon estimating the friction factor, f.  The  performance of its explicit formulations and those of artificial  intelligence (AI) techniques are studied in this paper.  The AI  techniques used here include artificial neural networks (ANNs) and  genetic programming (GP); both use the same data generated numerically  by systematically changing the values of Reynolds numbers, Re, and  relative roughness, e/D, and solving the Colebrook-White  equation for the value of f by using the successive substitution  method.  The tests included the transformation of Re and e/D  using a logarithmic scale.  This study shows that some of the explicit  formulations for friction factor induce undue errors, but a number of  them have good accuracy.  The ANN formulation for the solving of the  friction factor in the Colebrook-White equation is less successful  than that by GP. The implementation of GP offers another explicit  formulation for the friction factor; the performance of GP in terms of  R2 (0.997) and the root-mean-square error (0.013) is good, but its  numerically obtained values are slightly perturbed.

 

The hydraulic design and analysis of flow conditions in pipe  networks are dependent upon estimating the friction factor, f.  The  performance of its explicit formulations and those of artificial  intelligence (AI) techniques are studied in this paper.  The AI  techniques used here include artificial neural networks (ANNs) and  genetic programming (GP); both use the same data generated numerically  by systematically changing the values of Reynolds numbers, Re, and  relative roughness, e/D, and solving the Colebrook-White  equation for the value of f by using the successive substitution  method.  The tests included the transformation of Re and e/D  using a logarithmic scale.  This study shows that some of the explicit  formulations for friction factor induce undue errors, but a number of  them have good accuracy.  The ANN formulation for the solving of the  friction factor in the Colebrook-White equation is less successful  than that by GP. The implementation of GP offers another explicit  formulation for the friction factor; the performance of GP in terms of  R2 (0.997) and the root-mean-square error (0.013) is good, but its  numerically obtained values are slightly perturbed.

Article Details

A study of friction factor formulation in pipes using artificial intelligence techniques and explicit equations

Authors: Farzin SALMASI | Rahman KHATIBI | Mohammad Ali GHORBANI

Publisher: TUBITAK

Journal: Turkish Journal of Engineering and Environmental Sciences 2012 Vol.36 Issue 2, pp.121-138

Document Type: Article

Publication Date: 1/2012

Document Language: Turkish

Document ID (Org No.): 2000744619

Frequency: Tri-annual

eISSN: 1303-6157

pISSN: 1300-0160

Issues

Authors:

Farzin SALMASI | Rahman KHATIBI | Mohammad Ali GHORBANI

Publisher:

TUBITAK

Journal:

Turkish Journal of Engineering and Environmental Sciences 2012 Vol.36 Issue 2, pp.121-138

Document Type:

Article

Publication Date:

1/2012

Document Language:

Turkish

Document ID (Org No.):

2000744619

Frequency:

Tri-annual

eISSN:

1303-6157

pISSN:

1300-0160