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Record W1982437235 · doi:10.1115/1.1412462

Skin Friction Correlation in Open Channel Boundary Layers

2001· article· en· W1982437235 on OpenAlexaff
Mark F. Tachie, Donald J. Bergstrom, Ram Balachandar, Shyam Ramachandran

Bibliographic record

VenueJournal of Fluids Engineering · 2001
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Turbulent Flows
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsLaminar sublayerMechanicsTurbulencePitot tubeBoundary layerShear stressReynolds numberLaw of the wallOpen-channel flowWakeParasitic dragShear velocityTurbulence kinetic energyBoundary layer thicknessPhysicsGeometryMathematicsMaterials scienceFlow (mathematics)

Abstract

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Contributed by the Fluids Engineering Division of THE AMERICAN SOCIETY OF MECHANICAL ENGINEERS. Manuscript received by the Fluids Engineering Division February 17, 2000; revised manuscript received May 10, 2001. Associate Editor: J. Katz. In near-wall turbulence research, accurate determination of the wall shear stress, τw, (or skin friction coefficient Cf) is of critical importance due to its practical relevance and also because it determines the friction velocity uτ used by most boundary layer scaling laws. A number of techniques are available to determine the wall shear stress in turbulent boundary layers. If sufficient data are obtained in the linear viscous sublayer y+⩽4, the wall shear stress can be determined from the velocity gradient at the wall. However, it is often difficult to obtain adequate data within the linear sublayer in most experiments, especially if the Reynolds number is high and instruments such as pitot-tube and cross-wire probes are used. Alternatively, a reliable estimate of the wall shear stress can be obtained by fitting polynomial to the near-wall data up to y+⩽15 (George and Castillo 1, Tachie et al. 2). In a high Reynolds number flow, where a well-defined log-region exists, the Clauser plot technique is frequently used to determine the wall shear stress. For a turbulent boundary layer, leading edge geometry as well as freestream turbulence intensity can significantly modify the skin friction characteristics. For such conditions, a formulation that does not implicitly fix the strength of the wake but rather allows its value to be optimized while ensuring a reliable estimate of uτ was proposed by Finley et al. 3 and subsequently used by Granville 4 and Krogstad et al. 5. At sufficiently high Reynolds numbers, the wall shear stress can also be estimated from the peak value of the Reynolds shear stress profile near the wall. Finally, on smooth surfaces wall mounted probes can be used to measure τw directly. Using these methods, a number of correlations (e.g., Schultz-Grunow 6) have been developed to allow the prediction of skin friction for practical purposes. Most of the existing correlations consider moderate to high Reynolds numbers in canonical turbulent boundary layers, whereas the focus of the present work is low to moderately high Reynolds numbers in channel flows. More recently, Osaka et al. 7 reported extensive measurements in a smooth wall turbulent boundary layer for a Reynolds number range of 800⩽Reθ⩽6300, where Reθ is the Reynolds number based on boundary layer momentum thickness, θ. Direct wall shear measurements were made and the skin friction correlation derived from their measurements appears to be the most reliable in the literature for the range of Reθ they considered. Another widely referenced study of low Reynolds number boundary layer flows is that of Purtell et al. 8 who used the velocity gradient at the wall and momentum balance to infer the wall shear stress. In the present study, we are specifically concerned with open channel flows, which show some significant similarities with canonical boundary layers studied in mechanical engineering applications (Tachie et al. 9). In the hydraulic engineering community, it would appear that on occasion rather crude methods have been advocated for the prediction of skin friction. For example, Moody chart using the hydraulic diameter has been recommended for the prediction of the skin friction (ASCE Task Force 10). While this may be adequate for preliminary calculations, more reliable correlations are required. Recently, Schultz and Swain 11 reported a correlation for Cf on a smooth plate in a water tunnel at moderately high Reynolds numbers. Their correlation was typically 6-9 percent higher than that given by Coles 12, and did not consider low Reθ data. The present study proposes a new skin friction correlation for a smooth wall boundary layer in channel flows at low to moderately high Reynolds number. The experimental data used to develop the correlation were obtained from experiments conducted in different facilities under different conditions, and cover a relatively wide range of Reynolds number 100 2000. Furthermore, the difference between the water channel and wind tunnel data increases as Reθ increases. For example, compared to the Cf values predicted from the correlation of Osaka et al. 7, prediction from the present correlation is 10 and 15 percent higher at Reθ=1000 and 6000, respectively. The similarities and differences noted above can be explained as follows. The turbulence level in the wind tunnel experiments of Purtell et al. 8 and Osaka et al. 7 is an order of magnitude lower than the water channel data. One effect of high freestream turbulence intensity on the mean flow is to reduce the wake parameter (Π) which in turn increases Cf (e.g., Hoffman and Mohammadi 18, White 19). For the experiments conducted in open channel, the values of Π were less than 0.15. Measurements, as well as results obtained from direct numerical simulation for canonical turbulent boundary layers, showed that the values of Π are low at low Reynolds number but Π increases as the Reynolds number increases. Typical asymptotic values for Π in wind tunnel experiments varied from 0.55 to 0.62. Since values of the outer wake parameter at low Reθ in wind tunnel do not differ much from those obtained in the open channel experiments the good agreement between the data reported by Purtell et al. 8 and the open channel data at Reθ<1000 is to be expected even though the turbulence levels are distinctly different. On the other hand, the lower Cf values for the wind tunnel data of Purtell et al. 8 and the correlation developed by Osaka et al. 7 in comparison to the open channel data and the water channel data of Schultz and Swain 11 can be attributed to the much stronger wake components (Π) for the wind tunnel experiments. It is interesting to observe that although the data reported by Tachie et al. 17 were obtained in a wind tunnel, they are adequately described by the present correlation but not by the wind tunnel correlation of Osaka et al. 7. While the turbulence intensity for this experiment (i.e., Tachie et al. 17) is similar to those reported by Purtell et al. 8, the strength of the outer wake parameter was 0.1. This is significantly lower than values obtained in wind tunnel experiments at similar Reθ but comparable to open channel experiments as noted previously. This observation provides further evidence that the skin friction characteristics are strongly affected by the strength of the wake. Therefore, techniques such as that proposed by Finley et al. 3 that implicitly account for the role of the outer flow showed be used to determine the wall shear stress. A new skin friction correlation for open channel boundary layers was developed using skin friction data obtained from a variety of experiments, mostly in low Reynolds number open channel flows. The range of Reθ varied from 150 to 15,000, which covers most of the Reθ experiments available in the literature. The present correlation describes the existing data to within ±7 percent. It is observed that skin friction correlations developed for canonical turbulent boundary layers, e.g., Osaka et al. 7, only slightly under-predict the skin friction coefficient in open channel boundary layers at low Reynolds numbers, i.e., Reθ<2,000. The level of under-prediction increases at higher Reynolds numbers.

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How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.008
GPT teacher head0.206
Teacher spread0.198 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations12
Published2001
Admission routes1
Has abstractyes

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