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  • About
  • The Global ETD Search service is a free service for researchers to find electronic theses and dissertations. This service is provided by the Networked Digital Library of Theses and Dissertations.
    Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.
1

Predicting the vertical low suspended sediment concentration in vegetated flow using a random displacement model

Huai, W., Yang, L., Wang, W-J., Guo, Yakun, Wang, T., Cheng, Y. 05 September 2019 (has links)
Yes / Based on the Lagrangian approach, this study proposes a random displacement model (RDM) to predict the concentration of suspended sediment in vegetated steady open channel flow. Validation of the method was conducted by comparing the simulated results by using the RDM with available experimental measurements for uniform open-channel flows. The method is further validated with the classical Rouse formula. To simulate the important vertical dispersion caused by vegetation in the sediment-laden open channel flow, a new integrated sediment diffusion coefficient is introduced in this study, which is equal to a coefficient multiplying the turbulent diffusion coefficient. As such, the RDM approach for sandy flow with vegetation was established for predicting the suspended sediment concentration in low-sediment-concentration flow with both the emergent and submerged vegetation. The study shows that the value of for submerged vegetation flow is larger than that for emergent vegetation flow. The simulated result using the RDM is in good agreement with the available experimental data, indicating that the proposed sediment diffusion coefficient model can be accurately used to investigate the sediment concentration in vegetated steady open channel flow. / National Natural Science Foundation (No. 51439007, 11672213, and 11872285); Open Funding of State Key Laboratory of Water Resources and Hydropower Engineering Science (WRHES), Wuhan University (Project No: 2018HLG01)

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