Investigation of Shear Stress Distribution in a 90 Degree Channel Bend

Abstract Shear stress is a key parameter that plays an important role in sediment transport mechanisms; therefore, understanding shear stress distribution in rivers, and especially in river bends, is necessary to predict erosion, deposition mechanisms and lateral channel migration. The aim of this study is to analyze the shear stress distribution near a river bed at 90-degree channel bend using a depth-average method based on experimental measurement data. Bed shear stress distribution is calculated using the depth-averaged method based on velocity components data has been collected from a 3D-ADV device (three-dimensional acoustic doppler velocity) at different locations of a meandering channel. Laboratory experiments have been made at the hydraulic laboratory of the RCRFIDF (Research Center for River Flow Impingement and Debris Flow), Gangneung-Wonju National University, South Korea to provide data for simulating the incipient motion of the riverbed materials and then predicting the river morphological changes in the curved rivers. The calculated results show that the maximum value of shear stress distribution near the riverbed in the different cross sections of the surveyed channel occurs in a 70-degree cross section and occurs near the outer bank. From the beginning of a 40-degree curved channel section, the maximum value of the shear stress occurs near the outer bank at the end of the channel.

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A comparative evaluation of shear stress modeling based on machine learning methods in small streams

Journal of Hydroinformatics ◽

10.2166/hydro.2015.142 ◽

2015 ◽

Vol 17 (5) ◽

pp. 805-816 ◽

Cited By ~ 5

Author(s):

Onur Genç ◽

Bilal Gonen ◽

Mehmet Ardıçlıoğlu

Keyword(s):

Machine Learning ◽

Shear Stress ◽

Stress Distribution ◽

Measurement Data ◽

Machine Learning Algorithms ◽

Shear Stress Distribution ◽

Classification And Regression Tree ◽

Data Set ◽

Small Streams ◽

Artificial Neural

Predicting shear stress distribution has proved to be a critical problem to solve. Hence, the basic objective of this paper is to develop a prediction of shear stress distribution by machine learning algorithms including artificial neural networks, classification and regression tree, generalized linear models. The data set, which is large and feature-rich, is utilized to improve machine learning-based predictive models and extract the most important predictive factors. The 10-fold cross-validation approach was used to determine the performances of prediction methods. The predictive performances of the proposed models were found to be very close to each other. However, the results indicated that the artificial neural network, which has the R value of 0.92 ± 0.03, achieved the best classification performance overall accuracy on the 10-fold holdout sample. The predictions of all machine learning models were well correlated with measurement data.

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Evaluation of Wall Shear Stress Distribution in Upward Slug Flow

10.26678/abcm.encit2016.cit2016-0032 ◽

2016 ◽

Author(s):

jonathan nobrega ◽

Eugênio Spanó Rosa

Keyword(s):

Shear Stress ◽

Stress Distribution ◽

Wall Shear Stress ◽

Slug Flow ◽

Shear Stress Distribution ◽

Wall Shear Stress Distribution ◽

Wall Shear

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Assessing Machine Learning versus a Mathematical Model to Estimate the Transverse Shear Stress Distribution in a Rectangular Channel

Mathematics ◽

10.3390/math9060596 ◽

2021 ◽

Vol 9 (6) ◽

pp. 596

Author(s):

Babak Lashkar-Ara ◽

Niloofar Kalantari ◽

Zohreh Sheikh Khozani ◽

Amir Mosavi

Keyword(s):

Shear Stress ◽

Stress Distribution ◽

Fuzzy Inference ◽

Rectangular Channel ◽

Tsallis Entropy ◽

Shear Stress Distribution ◽

Data Series ◽

Shear Stresses ◽

Inference System ◽

Aspect Ratios

One of the most important subjects of hydraulic engineering is the reliable estimation of the transverse distribution in the rectangular channel of bed and wall shear stresses. This study makes use of the Tsallis entropy, genetic programming (GP) and adaptive neuro-fuzzy inference system (ANFIS) methods to assess the shear stress distribution (SSD) in the rectangular channel. To evaluate the results of the Tsallis entropy, GP and ANFIS models, laboratory observations were used in which shear stress was measured using an optimized Preston tube. This is then used to measure the SSD in various aspect ratios in the rectangular channel. To investigate the shear stress percentage, 10 data series with a total of 112 different data for were used. The results of the sensitivity analysis show that the most influential parameter for the SSD in smooth rectangular channel is the dimensionless parameter B/H, Where the transverse coordinate is B, and the flow depth is H. With the parameters (b/B), (B/H) for the bed and (z/H), (B/H) for the wall as inputs, the modeling of the GP was better than the other one. Based on the analysis, it can be concluded that the use of GP and ANFIS algorithms is more effective in estimating shear stress in smooth rectangular channels than the Tsallis entropy-based equations.

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