Sampling the Darcy Friction Factor Using Halton, Hammersley, Sobol, and Korobov Sequences: Data Points from the Colebrook Relation
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27740%2F25%3A10259029" target="_blank" >RIV/61989100:27740/25:10259029 - isvavai.cz</a>
Result on the web
<a href="https://www.mdpi.com/2306-5729/10/11/193" target="_blank" >https://www.mdpi.com/2306-5729/10/11/193</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.3390/data10110193" target="_blank" >10.3390/data10110193</a>
Alternative languages
Result language
angličtina
Original language name
Sampling the Darcy Friction Factor Using Halton, Hammersley, Sobol, and Korobov Sequences: Data Points from the Colebrook Relation
Original language description
When the Colebrook equation is used in its original implicit form, the unknown pipe flow friction factor can only be obtained through time-consuming and computationally demanding iterative calculations. The empirical Colebrook equation relates the unknown Darcy friction factor to a known Reynolds number and a known relative roughness of a pipe's inner surface. It is widely used in engineering. To simplify computations, a variety of explicit approximations have been developed, the accuracy of which must be carefully evaluated. For this purpose, this Data Descriptor gives a sufficient number of pipe flow friction factor values that are computed using a highly accurate iterative algorithm to solve the implicit Colebrook equation. These values serve as reference data, spanning the range relevant to engineering applications, and provide benchmarks for evaluating the accuracy of the approximations. The sampling points within the datasets are distributed in a way that minimizes gaps in the data. In this study, a Python Version v1 script was used to generate quasi-random samples, including Halton, Hammersley, Sobol, and deterministic lattice-based Korobov samples, which produce smaller gaps than purely random samples generated for comparison purposes. Using these sequences, a total of 2(20) = 1,048,576 data points were generated, and the corresponding datasets are provided in in the zenodo repositoryWhen a smaller subset of points is needed, the required number of initial points from these sequences can be used directly.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10700 - Other natural sciences
Result continuities
Project
<a href="/en/project/EH23_021%2F0008759" target="_blank" >EH23_021/0008759: Increasing the resilience of power grids in the context of decarbonisation, decentralisation and sustainable socio-economic development</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Data
ISSN
2306-5729
e-ISSN
2306-5729
Volume of the periodical
10
Issue of the periodical within the volume
11
Country of publishing house
CH - SWITZERLAND
Number of pages
13
Pages from-to
193
UT code for WoS article
001625940100001
EID of the result in the Scopus database
2-s2.0-105023667350