Assessment of Roughness Coefficient for Meandering Compound Channels

Pradhan, Arpan; Khatua, Kishanjit Kumar

doi:10.1007/s12205-017-1818-9

Cited by 14 publications

(9 citation statements)

References 20 publications

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“…Manning's roughness is dependent upon various factors including channel geometry and sinuosity and not just the bed material [20][21][22]. erefore, calculation of Manning's n for the bed material is not appropriate on meandering channels.…”

Section: Methodsmentioning

confidence: 99%

Variation of Velocity Distribution in Rough Meandering Channels

Pradhan

Khatua

Sankalp

2018

Advances in Civil Engineering

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Distribution of flow and velocity in a meandering river is important in river hydraulics to be investigated from a practical point of view in relation to the bank protection, navigation, water intakes, and sediment transport-depositional patterns. When flow enters a bend, the centrifugal force arising from the channel curvature leads to a transversal slope in the water surface. The interaction between the centrifugal force and transversal pressure gradient causes secondary flows in cross-sections, and the secondary flows spread further by moving along the bend. Hence, at the bends, these processes lead to longitudinal velocity increase in the inner wall and decrease at the outer wall. In this paper, experimentation is carried out for two different bed roughness on a 4.11 sinuosity meandering channel with 110° crossover. Longitudinal velocity distribution is analysed with the graphical illustrations for the detailed experimental study. Study of flow profile across the crossover is also particularly important as the inner bank of the bend changes to the outer bank and vice versa which has a significant effect on the water surface profile and hence on the velocity distribution along the full meander path. The objective of the analysis is to determine the effect of curvature and roughness on the velocity profiles, throughout the meander path. It is determined that the resistance of flow, on the smoother bed channel, is higher than that of the channel with higher Manning’s n above a certain depth at the apex and transition sections. A reciprocal study of the experimental investigation is attempted with a numerical hydrodynamic tool, namely, CCHE (Centre for Computational Hydroscience and Engineering) developed by NCCHE, University of Mississippi, US. The model is applied to simulate the inbank flow velocity distribution and validate the experimental observation for the meandering channel with rough bed.

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Section: Methodsmentioning

confidence: 99%

Variation of Velocity Distribution in Rough Meandering Channels

Pradhan

Khatua

Sankalp

2018

Advances in Civil Engineering

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“…However, very few methods are able to explain, in detail, the physics between fluid flow and solid structure interactions that result in high-flow resistance leading to very low-flow velocity as well as energy dissipation in the subsurface modules. For example, experimental studies were conducted to determine the impact of the module on flow pattern by Kee et al [14], Manning's roughness coefficient by Zakaria [13], Muhammad et al [15] and Pradhan et al [16], quality and quantity control and most recently effects of backwater on the hydraulic performance of the module [2]. In terms of numerical methods, Sánchez et al [17] applied computational fluid dynamics (CFD) to characterize the hydraulic performance of a drainage network and develop a clear validation with experimental data.…”

Section: River Sandmentioning

confidence: 99%

Modelling of Flow Parameters through Subsurface Drainage Modules for Application in BIOECODS

Abdurrasheed

Yusof

Al‐Qadami

et al. 2019

Water

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The flow resistance of the existing modules in the bio-ecological drainage system (BIOECODS) is high and may lead to flood instead of its mitigation. As part of efforts to enhance the performance of the system, the river engineering and urban drainage research center (REDAC) module was developed. This study modelled the hydrodynamics of flow through this module using FLOW-3D and laboratory experiments for two cases of free flow without module (FFWM) and flow with a module (FWM) to understand and visualize the effects of the module. With less than 5% error between the numerical and experimental results, REDAC module altered the flow pattern and created resistance by increasing the Manning's roughness coefficient at the upstream, depth-averaged flow velocity (43.50 cm/s to about 46.50 cm/s) at the downstream and decreasing water depth (7.75-6.50 cm). These variations can be attributed to the complex nature of the module pattern with further increase across the porous openings. Therefore, the technique used herein can be applied to characterize the behavior of fluids in larger arrangments of modules and under different flow conditions without the need for expensive laboratory experiments.

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“…The data are acquired from the Birmingham university website (http://www.birmingham.ac.uk/) and also collected from other articles [10,38,39]. Other than FCF-B, the authors have also used data from Toebes-Sooky [40], Kar [41], Das [42], Kiely [43], Patra-Kar [44], Khatua [45], Mohanty [46], and Pradhan [22] to predict the roughness coefficient, as given in Table 2. All of the experimental channel data used in the present work consists of similar hydraulic nature having smooth meandering compound channels.…”

Section: Sl Nomentioning

confidence: 99%

“…Studies [8,11,15,16,21,22,47,48] have indicated that Manning's n not only represent the resistance to the flow in the channel, but it represents the lumped response of all the hydraulic and geometric parameters influencing the flow in the channel. Therefore, in the current study, the author considers the influencing non-dimensional factors, such as the width ratio of the channel (α = B/b), depth ratio or relative depth (β = (H − h)/H), sinuosity (s), longitudinal channel slope (S o ), and meander belt width ratio (ω = B MW /B) as the inputs to model for predicting the Manning's n value.…”

Section: Factors Affecting Roughness Coefficientmentioning

confidence: 99%

Anticipate Manning’s Coefficient in Meandering Compound Channels

2018

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Estimating Manning's roughness coefficient (n) is one of the essential factors in predicting the discharge in a stream. Present research work is focused on prediction of Manning's n in meandering compound channels by using the Group Method of Data Handling Neural Network (GMDH-NN) approach. The width ratio (α), relative depth (β), sinuosity (s), Channel bed slope (S o ), and meander belt width ratio (ω) are specified as input parameters for the development of the model. The performance of GMDH-NN is evaluated with two different machine learning techniques, namely the support vector regression (SVR) and multivariate adaptive regression spline (MARS) with various statistical measures. Results indicate that the proposed GMDH-NN model predicts the Manning's n satisfactorily as compared to the MARS and SVR model. This GMDH-NN approach can be useful for practical implementation as the prediction of Manning's coefficient and subsequently discharge through Manning's equation in the compound meandering channels are found to be quite adequate.Hydrology 2018, 5, 47 2 of 21 hydraulic parameters. Formulation for Manning's roughness showing the gradient effect in channels having longitudinal slopes greater than 0.002 was derived by Jarrett [7]. Due to variation in flow depth, geometry and slope, the distinction of roughness coefficient in a straight channel as compared to a meandering channel were discussed by Arcement and Schneider [8]. Yen [9] proposed Manning's n for simple uniform flows taking account for a geometric measure, such as unevenness of the edge. Further, SCS method was linearized by James [10] for a meandering channel, named the Linearized SCS (LSCS) and recommended the Manning's roughness value for the different sinuosity channels. Shiono et al. [11] developed a model to predict discharge considering the Manning's n in the case of longitudinal slope and the meander effect of the channel. Jena [12] proposed an equation to calculate the n by considering the effect of channel width, longitudinal channel slope, and the flow depth of the channel. The variations of wetted area, wetted perimeter, and the velocity data on Manning's roughness coefficient by considering the flow simulation and sediment transport in irrigated channels was investigated by Mailapalli et al. [13]. Khatua et al. [14-16] formulated a mathematical equation for roughness coefficients by varying the sinuosity and geometry of the meandering compound channel. Xia et al. [17] and Barati [18-20] carried out the experiments for predicting discharge by taking care of the effect of bed roughness. Dash et al. [21] modeled the Manning's roughness coefficient by considering the aspect ratio, viscosity, slope of the bed, and sinuosity. Pradhan et al. [22] proposed an empirical formulation of predicting Manning's n by dimensional analysis for the compound meandering channel, which affects relative depth, width ratio, longitudinal channel slope, and sinuosity.In the recent years, ML techniques have been successfully implemented for predicting complex phenom...

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Assessment of Roughness Coefficient for Meandering Compound Channels

Cited by 14 publications

References 20 publications

Variation of Velocity Distribution in Rough Meandering Channels

Variation of Velocity Distribution in Rough Meandering Channels

Modelling of Flow Parameters through Subsurface Drainage Modules for Application in BIOECODS

Anticipate Manning’s Coefficient in Meandering Compound Channels

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