Power law and composite power law friction factor correlations for laminar and turbulent gas–liquid flow in horizontal pipelines

García, Francisco; Garcia, Reinaldo; Padrino, Juan C.; Mata, Clara; Trallero, J.L.; Joseph, Daniel D.

doi:10.1016/s0301-9322(03)00139-3

Cited by 103 publications

(48 citation statements)

References 15 publications

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“…Therefore, validity and efficiency is a crucial play when deciding upon which criteria to be used. The six model selection criteria employed in this study are adopted according to strong recommendations [30][31][32][33][34][35][36]. This study will detail the six selection criteria and then select the best criterion based on sound mathematical theory.…”

Section: Model Selection Criteriamentioning

confidence: 99%

“…Moreover, as arithmetic means, the presence of outliers will influence RMSE [30][31][32][33][34][35][36][37][38].…”

Section: Rmse (Root Mean Square Error)mentioning

confidence: 99%

“…It measures a specific type of error called random error. It is often used to compare real-world data against a model to test it and it has a wide application in all fields [31][32][33][34][35][36].…”

Section: Standard Deviation Of Marementioning

confidence: 99%

See 2 more Smart Citations

Which Friction Factor Model Is the Best? A Comparative Analysis of Model Selection Criteria

Kamel¹,

Shaqlaih²,

Rozyyev³

2018

JEPE

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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, R 2 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.

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Section: Model Selection Criteriamentioning

confidence: 99%

“…Moreover, as arithmetic means, the presence of outliers will influence RMSE [30][31][32][33][34][35][36][37][38].…”

Section: Rmse (Root Mean Square Error)mentioning

confidence: 99%

See 1 more Smart Citation

Which Friction Factor Model Is the Best? A Comparative Analysis of Model Selection Criteria

Kamel¹,

Shaqlaih²,

Rozyyev³

2018

JEPE

View full text Add to dashboard Cite

show abstract

“…In order to compare predicted friction factor (f M,pred ) with experimental data (f M,exp ), eight commonly used statistical parameters were implemented based on the literature recommendations (García et al, 2003(García et al, , 2005(García et al, , 2007Genić et al, 2011;Gregory and Fogarasi, 1985;Ouyang, 1998;Xiao et al, 1990). The parameters involved in the models comparison with their details interpretations are listed in Table S2 in Supplementary material.…”

Section: Statistical Analysis and Models Computationsmentioning

confidence: 99%

A comparative study of friction factor correlations for high concentrate slurry flow in smooth pipes

Assefa

Kaushal

2015

Journal of Hydrology and Hydromechanics

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A number of correlations for friction factor determinations in smooth pipes have been proposed in the past decades. The accuracy and applicability of these friction factor formulas should be examined. Based on this notion the paper is designed to provide a comparative study of friction factor correlations in smooth pipes for all flow regimes of Bingham fluids. Nine models were chosen. The comparisons of the selected equations with the existing experimental results, which are available in the literature, were expressed through MARE, MRE + , MRE -, RMSE, Ѳ, and S. The statistical comparisons were also carried out using MSC and AIC. The analyses show that the Wilson-Thomas (1985) and Morrison (2013) models are best fit models to the experimental data for the Reynolds number up to 40000. Within this range, both models can be used alternately. But beyond this Re value the discrepancy of the Wilson-Thomas model is higher than the Morrison model. In view of the fact that the Morrison model requires fewer calculations and parameters as well as a single equation is used to compute the friction factor for all flow regimes, it is the authors' advice to use this model for friction factor estimation for the flow of Bingham fluids in smooth pipes as an alternative to the Moody chart and other implicit formulae.

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“…Two power law correlations for sediment transport have been studied by Wang et al (2003) and for friction factors in turbulent gas-liquid flows by Garcia et al (2003Garcia et al ( , 2005. Correlations of families of bi-power laws depending on a third parameter have been constructed by Viana et al (2003) who correlated data for the rise velocity of Taylor bubbles in round vertical pipes as a family of bi-power laws of the Froude number vs. Reynolds number indexed by the Eotvos number.…”

Section: Classical Logistic Dose Response Curvementioning

confidence: 99%

Friction factor correlations for laminar, transition and turbulent flow in smooth pipes

Joseph

Yang

2010

Physica D: Nonlinear Phenomena

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In this paper we derive an accurate composite friction factor vs. Reynolds number correlation formula for laminar, transition and turbulent flow in smooth pipes. The correlation is given as a rational fraction of rational fractions of power laws which is systematically generated by smoothly connecting linear splines in log-log coordinates with a logistic dose curve algorithm. This kind of correlation seeks the most accurate representation of the data independent of any input from theories arising from the researchers ideas about the underlying fluid mechanics. As such, these correlations provide an objective metric against which observations and other theoretical correlations may be applied. Our correlation is as accurate, or more accurate, than other correlations in the range of Reynolds numbers in which the correlations overlap. However, our formula is not restricted to certain ranges of Reynolds number but instead applies uniformly to all smooth pipe flow data for which data is available. The properties of the classical logistic dose response curve are reviewed and extended to problems described by multiple branches of power laws. This extended method of fitting which leads to rational fractions of power laws is applied to data Marusic and Perry 1995 for the velocity profile in a boundary layer on a flat plate with an adverse pressure gradient, to data of Nikuradse 1932 andMcKeon et al. 2004 on friction factors for flow in smooth pipes and to the data of Nikuradse 1933 for effectively smooth pipes.

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Power law and composite power law friction factor correlations for laminar and turbulent gas–liquid flow in horizontal pipelines

Cited by 103 publications

References 15 publications

Which Friction Factor Model Is the Best? A Comparative Analysis of Model Selection Criteria

Which Friction Factor Model Is the Best? A Comparative Analysis of Model Selection Criteria

A comparative study of friction factor correlations for high concentrate slurry flow in smooth pipes

Friction factor correlations for laminar, transition and turbulent flow in smooth pipes

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