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Direct numerical simulations of turbulent flows over superhydrophobic surfaces
Abstract
Direct numerical simulations (DNSs) are used to investigate the drag-reducing performance of superhydrophobic surfaces (SHSs) in turbulent channel flow. SHSs combine surface roughness with hydrophobicity and can, in some cases, support a shear-free air–water interface. Slip velocities, wall shear stresses and Reynolds stresses are considered for a variety of SHS microfeature geometry configurations at a friction Reynolds number of Reτ ≈ 180. For the largest microfeature spacing studied, an average slip velocity over 75% of the bulk velocity is obtained, and the wall shear stress reduction is found to be nearly 40%. The simulation results suggest that the mean velocity profile near the superhydrophobic wall continues to scale with the wall shear stress but is offset by a slip velocity that increases with increasing microfeature spacing.
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Cited by
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Microscale acoustofluidics: Microfluidics driven via acoustics and ultrasonics
Friend, J., & Yeo, L. Y. (2011) · Reviews of Modern Physics · doi:10.1103/revmodphys.83.647 · Cited by 964
Recent advances in the potential applications of bioinspired superhydrophobic materials
Darmanin, T., & Guittard, F. (2014) · J. Mater. Chem. A · doi:10.1039/c4ta02071e · Cited by 555
Metadata
- Authors
- MARTELL, M. B., PEROT, J. B., & ROTHSTEIN, J. P.
- Venue
- Journal of Fluid Mechanics
- Publisher
- Cambridge University Press (CUP)
- Published
- 2009-02-10
- Volume
- 620
- Pages
- 31-41
- ISSN
- 0022-1120 (ISSN-L)
- 1469-7645
- OpenAlex
- W1973612065
- Cited by
- 223
- References
- 0
- FWCI
- 10.12
- Type
- journal article
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: Direct numerical simulations of turbulent flows over superhydrophobic surfaces. https://app.cochat.ai/papers/10.1017/s0022112008004916
- The paper
- MARTELL, M. B., PEROT, J. B., & ROTHSTEIN, J. P. (2009). Direct numerical simulations of turbulent flows over superhydrophobic surfaces. Journal of Fluid Mechanics. https://doi.org/10.1017/s0022112008004916
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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