Next Article in Journal
Effect of Wall Flexibility on the Deformation during Flow in a Stenosed Coronary Artery
Next Article in Special Issue
High Wavenumber Coherent Structures in Low Re APG-Boundary-Layer Transition Flow—A Numerical Study
Previous Article in Journal / Special Issue
Resolution and Energy Dissipation Characteristics of Implicit LES and Explicit Filtering Models for Compressible Turbulence

## Printed Edition

A printed edition of this Special Issue is available at MDPI Books....
Open AccessArticle

# Evolutionary Optimization of Colebrook’s Turbulent Flow Friction Approximations

by 1,* and
1
European Commission, Joint Research Centre, 21027 Ispra VA, Italy
2
Faculty of Mechanical Engineering, University of Niš, 18000 Niš, Serbia
*
Authors to whom correspondence should be addressed.
Fluids 2017, 2(2), 15; https://doi.org/10.3390/fluids2020015
Received: 1 March 2017 / Revised: 28 March 2017 / Accepted: 1 April 2017 / Published: 6 April 2017
(This article belongs to the Special Issue Turbulence: Numerical Analysis, Modelling and Simulation)
This paper presents evolutionary optimization of explicit approximations of the empirical Colebrook’s equation that is used for the calculation of the turbulent friction factor (λ), i.e., for the calculation of turbulent hydraulic resistance in hydraulically smooth and rough pipes including the transient zone between them. The empirical Colebrook’s equation relates the unknown flow friction factor (λ) with the known Reynolds number (R) and the known relative roughness of the inner pipe surface (ε/D). It is implicit in the unknown friction factor (λ). The implicit Colebrook’s equation cannot be rearranged to derive the friction factor (λ) directly, and therefore, it can be solved only iteratively [λ = f(λ, R, ε/D)] or using its explicit approximations [λ ≈ f(R, ε/D)], which introduce certain error compared with the iterative solution. The optimization of explicit approximations of Colebrook’s equation is performed with the aim to improve their accuracy, and the proposed optimization strategy is demonstrated on a large number of explicit approximations published up to date where numerical values of the parameters in various existing approximations are changed (optimized) using genetic algorithms to reduce maximal relative error. After that improvement, the computational burden stays unchanged while the accuracy of approximations increases in some of the cases very significantly. View Full-Text
Show Figures

Figure 1

MDPI and ACS Style

Brkić, D.; Ćojbašić, Ž. Evolutionary Optimization of Colebrook’s Turbulent Flow Friction Approximations. Fluids 2017, 2, 15. https://doi.org/10.3390/fluids2020015

AMA Style

Brkić D, Ćojbašić Ž. Evolutionary Optimization of Colebrook’s Turbulent Flow Friction Approximations. Fluids. 2017; 2(2):15. https://doi.org/10.3390/fluids2020015

Chicago/Turabian Style

Brkić, Dejan; Ćojbašić, Žarko. 2017. "Evolutionary Optimization of Colebrook’s Turbulent Flow Friction Approximations" Fluids 2, no. 2: 15. https://doi.org/10.3390/fluids2020015

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

1