scholarly journals Calibration method for Manning's roughness coefficient for a river flume model

2020 ◽  
Vol 20 (8) ◽  
pp. 3597-3603
Author(s):  
Yujian Li ◽  
Yixin Geng ◽  
Liang Mao
Keyword(s):  
Numerical Simulation ◽  
Error Analysis ◽  
Water Surface ◽  
Calibration Method ◽  
Empirical Method ◽  
Tarim River ◽  
Roughness Coefficient ◽  
Flume Experiments ◽  
N Value ◽  
Manning’S Roughness Coefficient

Abstract This paper takes the Tarim River as an example to study the selection of Manning's roughness coefficient (n) in numerical simulation and presents a new method for calibrating Manning's roughness coefficient of a flume model. The measured topographic data and hydraulic data obtained from the flume experiments are taken as the initial boundary conditions in flow simulation, and n value for a flume model of the Qiman reach of Tarim River is calibrated by using a CCHE2D model. The consistency between the simulated water surface and the measured water surface with different n value is compared by using the error analysis method. Manning's n value for a flume model which meets the minimum error requirements is determined. The relative error between n value obtained by the empirical method and n value obtained by the numerical simulation method is analyzed. The result shows that the calibration method of n value for a flume model by using the CCHE2D model and error analysis presented in this paper is feasible and reliable.

Manning’s N Value of Bridge-in-Backpack

2014 ◽  
Vol 638-640 ◽  
pp. 965-968
Author(s):  
Jing Ma ◽  
Ling Qiang Yang
Keyword(s):  
Water Flow ◽  
Roughness Coefficient ◽  
N Value ◽  
New Type ◽  
Manning’S Roughness Coefficient ◽  
Manning’S N ◽  
Manning's N

Bridge-in-a-Backpack is a new type bridge. this study will investigate the interaction of flow under the bridge with the tubes and decking, and recommend Manning’s roughness coefficient for water flow under the composite backbridge system.

Estimation of Manning’s Roughness Coefficient for Tigris River by Using HEC-RAS model

2018 ◽  
Vol 6 (3) ◽  
pp. 90-97 ◽  
Author(s):  
Mohammed Siwan Shamkhi ◽  
Zainab Shakir Attab
Keyword(s):  
Water Surface ◽  
Mean Value ◽  
Surface Elevation ◽  
Roughness Coefficient ◽  
Data Sets ◽  
Flow Discharge ◽  
Analysis Study ◽  
N Value ◽  
Tigris River ◽  
Discharge Measurements

Tigris River (downstream of the kut barrage reach) there is no study conducted on it to estimate its Manning n value. HEC-RAS was used to analysis study reach and calibrate n value of the study reach .filed data were collected during 2016-2017 (duration of study), eights data sets were observed included stage and discharge measurements. The discharge is controlled by Kut Barrage Operation. The range of water surface elevation is (+10.300 to +12.511) and flow discharge range is (202.7 - 355.280) m3/sec. The range of n value for study reach is (0.021-0.034). The calibration results provided suitable Manning n of 0.026 for downstream of Kut Barrage reach which represent mean value of results

scholarly journals Automated calibration of a two-dimensional overland flow model by estimating Manning's roughness coefficient using genetic algorithm

2017 ◽  
Vol 20 (2) ◽  
pp. 440-456
Author(s):  
J. Drisya ◽  
D. Sathish Kumar
Keyword(s):  
Genetic Algorithm ◽  
Flow Model ◽  
Overland Flow ◽  
Fitness Function ◽  
Roughness Coefficient ◽  
Model Parameters ◽  
Automatic Data ◽  
Automated Calibration ◽  
Manning’S Roughness Coefficient ◽  
Overland Flow Model

Abstract Calibration is an important phase in the hydrological modelling process. In this study, an automated calibration framework is developed for estimating Manning's roughness coefficient. The calibration process is formulated as an optimization problem and solved using a genetic algorithm (GA). A heuristic search procedure using GA is developed by including runoff simulation process and evaluating the fitness function by comparing the experimental results. The model is calibrated and validated using datasets of Watershed Experimentation System. A loosely coupled architecture is followed with an interface program to enable automatic data transfer between overland flow model and GA. Single objective GA optimization with minimizing percentage bias, root mean square error and maximizing Nash–Sutcliffe efficiency is integrated with the model scheme. Trade-offs are observed between the different objectives and no single set of the parameter is able to optimize all objectives simultaneously. Hence, multi-objective GA using pooled and balanced aggregated function statistic are used along with the model. The results indicate that the solutions on the Pareto-front are equally good with respect to one objective, but may not be suitable regarding other objectives. The present technique can be applied to calibrate the hydrological model parameters.

scholarly journals Manning’s roughness coefficient for the Doce River

RBRH ◽  
2018 ◽  
Vol 23 (0) ◽  
Author(s):  
Emmanuel Kennedy da Costa Teixeira ◽  
Márcia Maria Lara Pinto Coelho ◽  
Eber José de Andrade Pinto ◽  
Jéssica Guimarães Diniz ◽  
Aloysio Portugal Maia Saliba
Keyword(s):  
River Flow ◽  
Hydraulic Modeling ◽  
Roughness Coefficient ◽  
Local Approach ◽  
Water Courses ◽  
Time Variations ◽  
The Subject ◽  
Manning’S Roughness Coefficient ◽  
Doce River ◽  
Water Depths

ABSTRACT The Manning’s roughness coefficient is used for various hydraulic modeling. However, the decision on what value to adopt is a complex task, especially when dealing with natural water courses due to the various factors that affect this coefficient. For this reason, most of the studies carried out on the subject adopt a local approach, such as this proposal for the Doce River. Due to the regional importance of this river in Brazil, the objective of this article was to estimate the roughness coefficient of Manning along the river, in order to aid in hydraulic simulations, as well as to discuss the uncertainties and variations associated with this value. For this purpose, information on flow rates and water depths were collected at river flow stations along the river. With this information, the coefficients were calculated using the Manning equation, using the software Canal, and their space-time variations were observed. In addition, it was observed that the uncertainties in flow and depth measurements affect the value of the Manning coefficient in the case studied.

Manning's roughness coefficient for ecological subsurface channel with modules

2019 ◽  
Vol 18 (3) ◽  
pp. 349-361 ◽  
Author(s):  
Reza Mohammadpour ◽  
Muhammad Kashfy Zainalfikry ◽  
Nor Azazi Zakaria ◽  
Aminuddin Ab. Ghani ◽  
Ngai Weng Chan
Keyword(s):  
Roughness Coefficient ◽  
Manning’S Roughness Coefficient

scholarly journals Buried Depth of a Submarine Pipeline Based on Anchor Penetration

2019 ◽  
Vol 7 (8) ◽  
pp. 257
Author(s):  
Xueyuan Zhu ◽  
Qinglong Hao ◽  
Jie Zhang
Keyword(s):  
Numerical Simulation ◽  
Penetration Depth ◽  
Water Depth ◽  
Calculation Method ◽  
Water Surface ◽  
Submarine Pipeline ◽  
Projected Area ◽  
Actual Measurement ◽  
Calculation Results ◽  
The Relationship

Anchor penetration is an important issue involved in the study of submarine pipeline damage accidents. To explore the penetration of a ship’s anchor under certain conditions, this study investigated the motion and force of an anchor and formulated a calculation method for the bottoming speed of an anchor. Meanwhile, the depth of anchor penetration was calculated under different conditions according to bottoming speed through programming. Finally, the reliability of the calculation method for the penetration depth was verified by comparing the actual measurement and the numerical simulation. On the basis of the findings, the calculation results were further analyzed, and conclusions were derived regarding the relationship between anchor mass, the horizontal projected area of the anchor, the anchor height on the water surface, and water depth. The conclusions provide suggestions for the application of anchor penetration in terms of seabed depth with certain reference values.

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