1887
banner image
No data available.
Please log in to see this content.
You have no subscription access to this content.
No metrics data to plot.
The attempt to load metrics for this article has failed.
The attempt to plot a graph for these metrics has failed.
Accounting for uncertainty in the analysis of overlap layer mean velocity models
Rent:
Rent this article for
USD
10.1063/1.4733455
/content/aip/journal/pof2/24/7/10.1063/1.4733455
http://aip.metastore.ingenta.com/content/aip/journal/pof2/24/7/10.1063/1.4733455

Figures

Image of FIG. 1.
FIG. 1.

The VL50 subset of the Österlund18 zero-pressure-gradient turbulent boundary layer mean velocity measurements. There are 70 velocity profiles containing a total of 676 measurement points.

Image of FIG. 2.
FIG. 2.

The subset of the McKeon et al. 12 data from the Princeton Superpipe used here. There are 16 velocity profiles containing a total of 344 measurement points.

Image of FIG. 3.
FIG. 3.

Marginal prior PDFs for paramters in the logarithmic overlap models. These include the parameters in the classic, Buschmann(0), Buschmann(1), and Oberlack logarithmic laws.

Image of FIG. 4.
FIG. 4.

Marginal prior PDFs for parameters in the universal and Afzal power laws.

Image of FIG. 5.
FIG. 5.

Marginal prior PDFs for the Colebrook (C CR , left) and Nikuradse (C NR , right) roughness model parameters.

Image of FIG. 6.
FIG. 6.

Posterior and prior PDFs for κ and C in the classic logarithmic law for σγ = 0.005. The diagonal shows the marginal PDFs while the off-diagonal shows the samples in the κ-C plane.

Image of FIG. 7.
FIG. 7.

Posterior and prior PDFs for κ and C in the classic logarithmic law for σγ = 0.0025 and σγ = 0.01. The diagonal shows the marginal PDFs while the off-diagonal shows the samples in the κ-C plane.

Image of FIG. 8.
FIG. 8.

Posterior and prior PDFs for κ, C, and E in the Buschmann(0) logarithmic law for σγ = 0.005. The diagonal entries show the marginal PDFs while the off-diagonal entries show the samples projected onto two-dimensional planes in parameter space.

Image of FIG. 9.
FIG. 9.

Posterior and prior PDFs for κ, C, E, κ1, C 1, B 1, and E 1 in the Buschmann(1) logarithmic law for σγ = 0.005. The diagonal entries show the marginal PDFs while the off-diagonal entries show the samples projected onto two-dimensional planes in parameter space.

Image of FIG. 10.
FIG. 10.

Posterior and prior PDFs for A and α in the universal power law for σγ = 0.005. The diagonal entries show the marginal PDFs while the off-diagonal entries show the samples projected onto two-dimensional planes in parameter space.

Image of FIG. 11.
FIG. 11.

Posterior and prior PDFs for γ and κ and C in the Afzal power law for σγ = 0.005. The diagonal entries show the marginal PDFs while the off-diagonal entries show the samples projected onto two-dimensional planes in parameter space.

Image of FIG. 12.
FIG. 12.

Posterior and prior PDFs for κ and C in the classic logarithmic law obtained using McKeon's Superpipe data.12 The diagonal shows the marginal PDFs while the off-diagonal shows the samples in the κ-C plane.

Image of FIG. 13.
FIG. 13.

Posterior and prior PDFs for κ, C, and C CR in the classic logarithmic law with Colebrook roughness obtained using McKeon's Superpipe data.12 The diagonal entries show the marginal PDFs while the off-diagonal entries show the samples projected onto two-dimensional planes in parameters space.

Image of FIG. 14.
FIG. 14.

Roughness corrections: Computed from the data (Observed), calibrated Colebrook function (Colebrook (Cal)), and nominal Colebrook function (Colebrook (Nom)).

Image of FIG. 15.
FIG. 15.

Posterior and prior PDFs for κ, C, E, κ1, C 1, B 1, and E 1 in the Buschmann(1) logarithmic law obtained using McKeon's Superpipe data.12 The diagonal entries show the marginal PDFs while the off-diagonal entries show the samples projected onto two-dimensional planes in parameter space.

Image of FIG. 16.
FIG. 16.

Posterior and prior PDFs for κ, C, E, κ1, C 1, B 1, E 1, and C CR in the Buschmann(1) logarithmic law with Colebrook roughness obtained using McKeon's Superpipe data.12 The diagonal entries show the marginal PDFs while the off-diagonal entries show the samples projected onto two-dimensional planes in parameter space.

Tables

Generic image for table
Table I.

Summary of overlap layer models, indicating the defining equations and the calibration parameters.

Generic image for table
Table II.

Subsets of the Österlund data set that are used to calibrate and compare the overlap layer models.

Generic image for table
Table III.

Model posterior probability for σγ = 0.005. The bold entries indicate the models with the highest posterior plausibility for each data set.

Generic image for table
Table IV.

Model posterior probability for σγ = 0.0025. The bold entries indicate the models with the highest posterior probability for each data set.

Generic image for table
Table V.

Model posterior probability for σγ = 0.01. The bold entries indicate the models with the highest posterior probability for each data set.

Generic image for table
Table VI.

Model log-evidence log (πevid) computed using Princeton Superpipe data. The bold entry indicates the model with the highest evidence.

Generic image for table
Table VII.

Posterior model probability computed using Princeton Superpipe data. The bold entry indicates the model with the highest posterior probability.

Loading

Article metrics loading...

/content/aip/journal/pof2/24/7/10.1063/1.4733455
2012-07-17
2014-04-25
Loading

Full text loading...

This is a required field
Please enter a valid email address
752b84549af89a08dbdd7fdb8b9568b5 journal.articlezxybnytfddd
Scitation: Accounting for uncertainty in the analysis of overlap layer mean velocity models
http://aip.metastore.ingenta.com/content/aip/journal/pof2/24/7/10.1063/1.4733455
10.1063/1.4733455
SEARCH_EXPAND_ITEM