首页|Prediction of bedload transport inside vegetation canopies with natural morphology
Prediction of bedload transport inside vegetation canopies with natural morphology
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
万方数据
Due to vegetation drag and vegetation-generated turbulence,bedload transport in vegetated channels is more complicated than that in nonvegetated channels.It is challenging to obtain accurate predictions of bedload transport in vegetated channels.Previous studies generally used rigid circular cylinders to simulate vegetation,and the impact of plant morphology on bedload transport was typically ignored,these methods deviate from natural scenarios,resulting in prediction errors in transport rates of more than an order of magnitude.This study measured bedload transport rates inside P.australis,A.calamus and T.latifolia canopies and in arrays of rigid cylinders for comparison.The impact of plant morphology on bedload transport in vegetated channels was examined.Inside the canopies of natural morphology,the primary factor driving bedload transport is the near-bed turbulent kinetic energy(TKE),which consists of both bed-generated and vegetation-generated turbulence.A method was proposed to predict the near-bed TKE inside canopies with natural morphology.For the same solid volume fraction of plants,the transport rate inside canopies with a natural morphology is greater than or equal to that within an array of rigid cylinders,depending on the plant shape.This finding indicates that plant morphology has a significant impact on transport rates in vegetated regions and cannot be ignored,which is typical in practice.Four classic bedload transport equations(the Meyer-Peter-Müller,Einstein,Engelund and Dou equations),which are suitable for bare channels(no vegetation),were modified in terms of the near-bed TKE.The predicted near-bed TKE was inserted into these four equations to predict the transport rate in canopies with natural morphology.A comparison of the predictions indicated that the Meyer-Peter-Müller equation had the highest accuracy in predicting the transport rate in vegetated landscapes.
Li He、Yu-qi Shan、Chao Liu、Hui Cao、Xing-nian Liu、Yakun Guo
展开 >
State Key Laboratory of Hydraulics and Mountain River Engineering,Sichuan University,Chengdu 610065,China
Institute for Disaster Management and Reconstruction,Sichuan University,Chengdu 610065,China
China Yangtze Power Co.,Ltd.,Yichang 443002,China
Hubei Key Laboratory of Intelligent Yangtze and Hydroelectric Science,Yichang 443002,China
Faculty of Engineering and Informatics,University of Bradford,Bradford,UK
展开 >
National Key Research and Development Program of ChinaNational Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaFundamental Research Project of China Yangtze Power Co.,Ltd
NETL
NSTL
万方数据
