Abstract
In this paper, we investigate the applicability of the predictive wall shear-stress model, recently developed by Mathis et al. J. Fluid Mech. 715, 163–180, 2013, [17], to environmental flows where near-wall information is typically inaccessible. This wall-model, which embeds the scale interaction mechanisms of superposition and modulation, is able to reconstruct the instantaneous wall (bed) shear-stress fluctuations in turbulent boundary layers. The database considered here comes from field measurements using acoustic Doppler velocimeters carried out in a shallow tidal channel (Suisun Slough in North San Francisco Bay). The model is first applied to a selected subset of data sharing common properties with the canonical turbulent boundary layer. Statistics and energy content of these predictions are found to be consistent with laboratory predictions and DNS results. The model is then used on the whole dataset, whose some of them having properties far from the canonical case. Even for these situations, the model is able to preserve the overall Reynolds trend. This study shows the great capability of the model for environmental applications, which is the only one able to predict both the correct energetic content and probability density function.
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Acknowledgments
The Australian Research Council and the University of Melbourne McKenzie Fellowship program are gratefully acknowledged for their support. The field work was supported by the CALFED Bay Delta Authority Restoration Program (ERP02P22) and The Foundation for Young Australians Centenary Scholarship Award. Thanks to F. Parchaso and B. Richards from the U.S. Geological Survey for assistance with the field experiment.
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Mathis, R., Marusic, I., Cabrit, O., Jones, N.L., Ivey, G.N. (2016). Reconstruction of Wall Shear-Stress Fluctuations in a Shallow Tidal River. In: Stanislas, M., Jimenez, J., Marusic, I. (eds) Progress in Wall Turbulence 2. ERCOFTAC Series, vol 23. Springer, Cham. https://doi.org/10.1007/978-3-319-20388-1_22
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DOI: https://doi.org/10.1007/978-3-319-20388-1_22
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