Analytical solution of velocity distribution for flow through submerged large deflection flexible vegetation

Wang, Wei-Jie; Huai, Wenxin; Chen, Qiuwen; Zhou, Jifu

doi:10.1007/s10483-015-1897-9

Cited by 29 publications

(21 citation statements)

References 30 publications

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“…For example, individual plant species with differing spatial distributions of leaf area and stems have been shown to not significantly alter f [ Nikora et al ., ]. Indeed, there is an extensive literature in “ecohydraulics” that addresses the effect of vegetation on the bulk flow, mean velocity, and the vertical distribution of turbulent stresses in uniform open channel flow subjected to various driving gradients [ Järvelä , ; Poggi et al ., ; Carollo et al ., ; Huthoff et al ., ; Nepf and Ghisalberti , ; Poggi et al ., ; Huai et al ., ; Luhar et al ., ; Katul et al ., ; Nepf , ; Konings et al ., ; Siniscalchi et al ., ; Huai et al ., ; Okamoto and Nezu , ; Huai et al ., ; Wang et al ., ; Banerjee et al ., ]. The specific case here, however, with emergent vegetation interacting with a nonuniform and complex flow in the absence of a strong driving land surface gradient, has yet to be explored.…”

Section: Introductionmentioning

confidence: 99%

Steady nonuniform shallow flow within emergent vegetation

Wang

Huai

Thompson

et al. 2015

Water Resources Research

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Surface flow redistribution on flat ground from crusted bare soil to vegetated patches following intense rainfall events elevates plant available water above that provided by rainfall. The significance of this surface water redistribution to sustaining vegetation in arid and semiarid regions is undisputed. What is disputed is the quantity and spatial distribution of the redistributed water. In ecohydrological models, such nonuniform flows are described using the Saint-Venant equation (SVE) subject to a Manning roughness coefficient closure. To explore these assumptions in the most idealized setting, flume experiments were conducted using rigid cylinders representing rigid vegetation with varying density. Flow was induced along the streamwise x direction by adjusting the free water surface height H(x) between the upstream and downstream boundaries mimicking the nonuniformity encountered in nature. In natural settings, such H(x) variations arise due to contrasts in infiltration capacity and ponded depths during storms. The measured H(x) values in the flume were interpreted using the SVE augmented with progressively elaborate approximations to the roughness representation. The simplest approximation employs a friction factor derived from a drag coefficient (C d ) for isolated cylinders in a locally (but not globally) uniform flow and upscaled using the rod density that was varied across experiments. Comparison between measured and modeled H(x) suggested that such a ''naive'' approach overpredicts H(x). Blockage was then incorporated into the SVE model calculations but resulted in underestimation of H(x). Biases in modeled H(x) suggest that C d must be varying in x beyond what a local or bulk Reynolds number predicts. Inferred C d (x) from the flume experiments exhibited a near-parabolic shape most peaked in the densest canopy cases. The outcome of such C d (x) variations is then summarized in a bulk resistance formulation that may be beneficial to modeling runon-runoff processes on shallow slopes using SVE.

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Section: Introductionmentioning

confidence: 99%

Steady nonuniform shallow flow within emergent vegetation

Wang

Huai

Thompson

et al. 2015

Water Resources Research

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“…Then, the cutoff would suppress the small eddies and, presumably, lead to drag reduction. Several researchers conducted a comprehensive study on drag reduction for water flow and proposed relationships for the Fanning friction factor, which helps other researchers to analyze their results [16][17][18][19]. Virk [20] investigated the performance of different polymer solutions and found a trend to a maximum drag reduction (MDR) asymptote in all cases.…”

Section: Introductionmentioning

confidence: 99%

Soft computing method for predicting pressure drop reduction in crude oil pipelines based on machine learning methods

Moayedi

Foong

Nguyen

2020

J Braz. Soc. Mech. Sci. Eng.

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In the oil industry, the drag-reducing agent has been used to reduce turbulent friction of fluids. The main effort of this study is to examine the feasibility of four novel machine learning models, namely multilayer perceptron, M5Rules, decision table (DT), and trees M5P to estimate the percentage of drag reduction. Then, the mentioned methods are utilized to identify a relationship between the input and output parameters of the crude oil pipeline system. The parameter percentage of drag reduction was taken as the essential output. In contrast, the input parameters selected the flow rate of oil, polymer concentration, kind of polymer, temperature, as well as pipe diameter and roughness. The predicted results obtained by the tools mentioned above were evaluated according to several known statistical indices, namely coefficient of determination (R 2), mean absolute error (MAE), root mean squared error (RMSE), relative absolute error (RAE), and root relative squared error (RRSE) as well as novel ranking systems of color intensity rating and total ranking method. The training and testing results of the DT learning method for the

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“…China has been recognized as a landslide-prone country, particularly those triggered by significant earthquakes. Generally, strong ground motions trigger heavy landslides which cause more intense damages than earthquake its self (Zhong-sheng 2003;Wang et al 2015b). An M6.5 earthquake occurred on 3 August 2014 in Ludian district of Yunnan province, China.…”

Section: Introductionmentioning

confidence: 99%

A particle-based optimization of artificial neural network for earthquake-induced landslide assessment in Ludian county, China

Li²,

Moayedi

et al. 2019

Geomatics, Natural Hazards and Risk

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The focal point of this study is to assess the efficacy of a state-ofthe-art optimization technique namely, particle swarm optimization (PSO) for enhancing the performance of the artificial neural network (ANN) in modeling the seismic landslides at Ludian districts, China. Twelve geological and hydrological landslide-conditioning factors namely, elevation, lithology, slope degree, slope aspect, stream power index, peak ground acceleration, topographic wetness index, distance to river, distance to road, distance to fault, normalized difference vegetation index and plan curvature were considered within a geographic information system (GIS). After achieving the optimal structure of the multilayer perceptron neural network, the PSO algorithm was applied to improve its efficiency. The landslide susceptibility maps were generated in a GIS environment and area under the curve (AUC) criterion was used to assess the integrity of employed predictive models. The results showed that after applying the PSO algorithm, AUC experiences a significant increase from 0.765 to 0.825 in the validation phase. Moreover, respective AUCs of 0.812 and 0.828 obtained for the training phase of ANN and PSO-ANN reveal the efficiency of the proposed algorithm in improving the ANN accuracy. ARTICLE HISTORY

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Analytical solution of velocity distribution for flow through submerged large deflection flexible vegetation

Cited by 29 publications

References 30 publications

Steady nonuniform shallow flow within emergent vegetation

Steady nonuniform shallow flow within emergent vegetation

Soft computing method for predicting pressure drop reduction in crude oil pipelines based on machine learning methods

A particle-based optimization of artificial neural network for earthquake-induced landslide assessment in Ludian county, China

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