Some explicit formulations of Colebrook-White friction factor considering accuracy vs. computational speed

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@Article{Giustolisi:2011:JH,
  author =       "O. Giustolisi and L. Berardi and T. M. Walski",
  title =        "Some explicit formulations of {Colebrook-White}
                 friction factor considering accuracy vs. computational
                 speed",
  journal =      "Journal of Hydroinformatics",
  year =         "2011",
  volume =       "13",
  number =       "3",
  pages =        "401--418",
  keywords =     "genetic algorithms, genetic programming, Colebrook
                 White formula, computational speed, evolutionary
                 polynomial regression, friction factor, pipe flow",
  ISSN =         "1464-7141",
  DOI =          "doi:10.2166/hydro.2010.098",
  size =         "18 pages",
  abstract =     "The Colebrook-White formulation of the friction factor
                 is implicit and requires some iterations to be solved
                 given a correct initial search value and a target
                 accuracy. Some new explicit formulations to efficiently
                 calculate the Colebrook White friction factor are
                 presented herein. The aim of this investigation is
                 twofold: (i) to preserve the accuracy of estimates
                 while (ii) reducing the computational burden (i.e.
                 speed). On the one hand, the computational
                 effectiveness is important when the intensive
                 calculation of the friction factor (e.g. large-size
                 water distribution networks (WDN) in optimisation
                 problems, flooding software, etc.) is required together
                 with its derivative. On the other hand, the accuracy of
                 the developing formula should be realistically chosen
                 considering the remaining uncertainties surrounding the
                 model where the friction factor is used. In the
                 following, three strategies for friction factor mapping
                 are proposed which were achieved by using the
                 Evolutionary Polynomial Regression (EPR). The result is
                 the encapsulation of some pieces of the friction factor
                 implicit formulae within pseudo-polynomial
                 structures.",
  notes =        "IWA Publishing",
}

Genetic Programming entries for Orazio Giustolisi Luigi Berardi Thomas M Walski

Citations