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Predicting the Drag of Rough Surfaces
Abstract
Reliable full-scale prediction of drag due to rough wall-bounded turbulent fluid flow remains a challenge. Currently, the uncertainty is at least 10%, with consequences, for example, on energy and transport applications exceeding billions of dollars per year. The crux of the difficulty is the large number of relevant roughness topographies and the high cost of testing each topography, but computational and experimental advances in the last decade or so have been lowering these barriers. In light of these advances, here we review the underpinnings and limits of relationships between roughness topography and drag behavior, focusing on canonical and fully turbulent incompressible flow over rigid roughness. These advances are beginning to spill over into multiphysical areas of roughness, such as heat transfer, and promise broad increases in predictive reliability.
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Cited by
Data-driven prediction of the equivalent sand-grain roughness
Ma, H., Li, Y., Yang, X., & Ye, L. (2023) · Scientific Reports · doi:10.1038/s41598-023-46564-4 · Cited by 13
Metadata
- Authors
- Chung, D., Hutchins, N., Schultz, M. P., & Flack, K. A.
- Venue
- Annual Review of Fluid Mechanics
- Publisher
- Annual Reviews
- Published
- 2021-01-05
- Volume
- 53
- Issue
- 1
- Pages
- 439-471
- ISSN
- 0066-4189 (ISSN-L)
- 1545-4479
- OpenAlex
- W3119700427
- Cited by
- 345
- References
- 0
- FWCI
- 5.21
- Type
- journal article
- Language
- en
- Open access
- closed
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- The paper
- Chung, D., Hutchins, N., Schultz, M. P., & Flack, K. A. (2021). Predicting the Drag of Rough Surfaces. Annual Review of Fluid Mechanics. https://doi.org/10.1146/annurev-fluid-062520-115127
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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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