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Intelligent Flow Friction Estimation

Nowadays, the Colebrook equation is used as a mostly accepted relation for the calculation of fluid flow friction factor. However, the Colebrook equation is implicit with respect to the friction factor (λ). In the present study, a noniterative approach using Artificial Neural Network (ANN) was devel...

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Detalles Bibliográficos
Autores principales: Brkić, Dejan, Ćojbašić, Žarko
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi Publishing Corporation 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4834174/
https://www.ncbi.nlm.nih.gov/pubmed/27127498
http://dx.doi.org/10.1155/2016/5242596
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author Brkić, Dejan
Ćojbašić, Žarko
author_facet Brkić, Dejan
Ćojbašić, Žarko
author_sort Brkić, Dejan
collection PubMed
description Nowadays, the Colebrook equation is used as a mostly accepted relation for the calculation of fluid flow friction factor. However, the Colebrook equation is implicit with respect to the friction factor (λ). In the present study, a noniterative approach using Artificial Neural Network (ANN) was developed to calculate the friction factor. To configure the ANN model, the input parameters of the Reynolds Number (Re) and the relative roughness of pipe (ε/D) were transformed to logarithmic scales. The 90,000 sets of data were fed to the ANN model involving three layers: input, hidden, and output layers with, 2, 50, and 1 neurons, respectively. This configuration was capable of predicting the values of friction factor in the Colebrook equation for any given values of the Reynolds number (Re) and the relative roughness (ε/D) ranging between 5000 and 10(8) and between 10(−7) and 0.1, respectively. The proposed ANN demonstrates the relative error up to 0.07% which had the high accuracy compared with the vast majority of the precise explicit approximations of the Colebrook equation.
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spelling pubmed-48341742016-04-28 Intelligent Flow Friction Estimation Brkić, Dejan Ćojbašić, Žarko Comput Intell Neurosci Research Article Nowadays, the Colebrook equation is used as a mostly accepted relation for the calculation of fluid flow friction factor. However, the Colebrook equation is implicit with respect to the friction factor (λ). In the present study, a noniterative approach using Artificial Neural Network (ANN) was developed to calculate the friction factor. To configure the ANN model, the input parameters of the Reynolds Number (Re) and the relative roughness of pipe (ε/D) were transformed to logarithmic scales. The 90,000 sets of data were fed to the ANN model involving three layers: input, hidden, and output layers with, 2, 50, and 1 neurons, respectively. This configuration was capable of predicting the values of friction factor in the Colebrook equation for any given values of the Reynolds number (Re) and the relative roughness (ε/D) ranging between 5000 and 10(8) and between 10(−7) and 0.1, respectively. The proposed ANN demonstrates the relative error up to 0.07% which had the high accuracy compared with the vast majority of the precise explicit approximations of the Colebrook equation. Hindawi Publishing Corporation 2016 2016-04-03 /pmc/articles/PMC4834174/ /pubmed/27127498 http://dx.doi.org/10.1155/2016/5242596 Text en Copyright © 2016 D. Brkić and Ž. Ćojbašić. https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Brkić, Dejan
Ćojbašić, Žarko
Intelligent Flow Friction Estimation
title Intelligent Flow Friction Estimation
title_full Intelligent Flow Friction Estimation
title_fullStr Intelligent Flow Friction Estimation
title_full_unstemmed Intelligent Flow Friction Estimation
title_short Intelligent Flow Friction Estimation
title_sort intelligent flow friction estimation
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4834174/
https://www.ncbi.nlm.nih.gov/pubmed/27127498
http://dx.doi.org/10.1155/2016/5242596
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