Arfaie, A, Burns, AD, Dorrell, RM et al. (3 more authors) (2018) Optimisation of flow resistance and turbulent mixing over bed forms. Environmental Modelling & Software, 107. pp. 141-147. ISSN 1364-8152
Abstract
Previous work on the interplay between turbulent mixing and flow resistance for flows over periodic rib roughness elements is extended to consider the flow over idealized shapes representative of naturally occurring sedimentary bed forms. The primary motivation is to understand how bed form roughness affects the carrying capacity of sediment-bearing flows in environmental fluid dynamics applications, and in engineering applications involving the transport of particulate matter in pipelines. For all bed form shapes considered, it is found that flow resistance and turbulent mixing are strongly correlated, with maximum resistance coinciding with maximum mixing, as was previously found for the special case of rectangular roughness elements. Furthermore, it is found that the relation between flow resistance to eddy viscosity collapses to a single monotonically increasing linear function for all bed form shapes considered, indicating that the mixing characteristics of the flows are independent of the detailed morphology of individual roughness elements.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | (c) 2018, Published by Elsevier Ltd. This is an author produced version of a paper published in Environmental Modelling and Software. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | Turbulent flow; Roughness; CFD; Bed forms |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Environment (Leeds) > School of Earth and Environment (Leeds) > Institute for Applied Geosciences (IAG) (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 25 Jun 2018 12:54 |
Last Modified: | 15 Jun 2019 00:39 |
Status: | Published |
Publisher: | Elsevier |
Identification Number: | 10.1016/j.envsoft.2018.06.002 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:132461 |