
The Colebrook-White equation is a fundamental formula in fluid mechanics, widely used to determine the friction factor in turbulent flow through pipes. It is crucial for calculating head losses in pipelines and for the design and optimization of hydraulic systems. In this dataset, the friction factor (f) is obtained via numerical solution using the Newton-Raphson method, with a truncation accuracy of 1e-6. The first and second columns of the provided dataset pertain to the Reynolds number (Re) and relative roughness (ε/D) variables, respectively. The data were generated across specified ranges [4500-2250000] with constant step of 4500 for Re and [0.00005, 0.0005, 0.005, 0.05, and 0.5] for ε/D. The third column presents of the f values calculated using the Colebrook-White equation, which is given as: 1/(\sqrt(f)) = -2*log((\epsilon/D)/3.7 + 2.51/(Re*\sqrt(f))) This dataset has multiple applications: it facilitates evaluating empirical formulas approximating the Colebrook-White equation and provides a valuable foundation for developing machine learning models to accurately predict the friction factor. Beyond aiding hydraulic system design, this dataset is a useful resource for researchers and engineers aiming to optimize pipeline flow, model turbulent pipe flow behavior, and advance computational methods in fluid dynamics.
Machine Learning, Darcy-Weisbach, data, Data-Driven Models, pipe network, Colebrook-White, newton-raphson, Friction Factor, Empirical Equations
Machine Learning, Darcy-Weisbach, data, Data-Driven Models, pipe network, Colebrook-White, newton-raphson, Friction Factor, Empirical Equations
| citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 0 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
