The DOI matches its Crossref record.
A theoretical prediction of friction drag reduction in turbulent flow by superhydrophobic surfaces
Abstract
We present a theoretical prediction for the drag reduction rate achieved by superhydrophobic surfaces in a turbulent channel flow. The predicted drag reduction rate is in good agreement with results obtained from direct numerical simulations at Reτ≃180 and 400. The present theory suggests that large drag reduction is possible also at Reynolds numbers of practical interest (Reτ∼105–106) by employing a hydrophobic surface, which induces a slip length on the order of ten wall units or more.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cited by
Superhydrophobic surfaces: from structural control to functional application
Zhang, X., Shi, F., Niu, J., Jiang, Y., & Wang, Z. (2008) · J. Mater. Chem. · doi:10.1039/b711226b · Cited by 1,768
Bioinspired Surfaces with Superwettability: New Insight on Theory, Design, and Applications
Wang, S., Liu, K., Yao, X., & Jiang, L. (2015) · Chemical Reviews · doi:10.1021/cr400083y · Cited by 1,646
Review of Fluid Slip over Superhydrophobic Surfaces and Its Dependence on the Contact Angle
Voronov, R. S., Papavassiliou, D. V., & Lee, L. L. (2008) · Industrial & Engineering Chemistry Research · doi:10.1021/ie0712941 · Cited by 294
Global field reconstruction from sparse sensors with Voronoi tessellation-assisted deep learning
Fukami, K., Maulik, R., Ramachandra, N., Fukagata, K., & Taira, K. (2021) · Nature Machine Intelligence · doi:10.1038/s42256-021-00402-2 · Cited by 253
Self-citation
Metadata
- Authors
- Fukagata, K., Kasagi, N., & Koumoutsakos, P.
- Venue
- Physics of Fluids
- Publisher
- AIP Publishing
- Published
- 2006-05-01
- Volume
- 18
- Issue
- 5
- ISSN
- 1070-6631 (ISSN-L)
- 1089-7666
- OpenAlex
- W2007582957
- Cited by
- 242
- References
- 0
- FWCI
- 6.95
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: A theoretical prediction of friction drag reduction in turbulent flow by superhydrophobic surfaces. https://app.cochat.ai/papers/10.1063/1.2205307
- The paper
- Fukagata, K., Kasagi, N., & Koumoutsakos, P. (2006). A theoretical prediction of friction drag reduction in turbulent flow by superhydrophobic surfaces. Physics of Fluids. https://doi.org/10.1063/1.2205307
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.