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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
Article
Which Friction Factor Model Is the Best? A Comparative Analysis of Model Selection Criteria
Author(s)
Ahmed H. Kamel1, Ali S. Shaqlaih2 and Arslan Rozyyev1
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DOI:10.17265/1934-8975/2018.03.006
Affiliation(s)
1. University of Texas of the Permian Basin Odessa, Texas 79762, USA
2. University of North Texas at Dallas, Dallas, Texas 75241, USA
ABSTRACT
The
ongoing research for model choice and selection has generated a plethora of
approaches. With such a wealth of methods, it can be difficult for a researcher
to know what model selection approach is the proper way to proceed to select
the appropriate model for prediction. The authors present an evaluation of
various model selection criteria from decision-theoretic perspective using
experimental data to define and recommend a criterion to select the best model.
In this analysis, six of the most common selection criteria, nineteen friction
factor correlations, and eight sets of experimental data are employed. The
results show that while the use of the traditional correlation coefficient, R2 is inappropriate, root mean square error, RMSE can be used to rank models, but
does not give much insight on their accuracy. Other criteria such as
correlation ratio, mean absolute error, and standard deviation are also
evaluated. The AIC (Akaike Information
Criterion) has shown its superiority to other selection
criteria. The authors propose AIC as an alternative to use when fitting
experimental data or evaluating existing correlations. Indeed, the AIC method
is an information theory based, theoretically sound and stable. The paper
presents a detailed discussion of the model selection criteria, their pros and
cons, and how they can be utilized to allow proper comparison of different
models for the best model to be inferred based on sound mathematical theory. In
conclusion, model selection is an interesting problem and an innovative
strategy to help alleviate similar challenges faced by the professionals in the
oil and gas industry is introduced.
KEYWORDS
Friction factor, information theory, model selection, turbulent flow, straight tubing.
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