scholarly journals Numerical study of boundary shear stress distribution in rectangular open channel flow

Author(s):  
Hossein BONAKDARI ◽  
Mohtaram TOOSHMALANI ◽  
Daniel LEVACHER ◽  
Gislain LIPEME KOUYI
2008 ◽  
Vol 46 (5) ◽  
pp. 598-609 ◽  
Author(s):  
Saeed Reza Khodashenas ◽  
Kamal El Kadi Abderrezzak ◽  
André Paquier

2022 ◽  
Author(s):  
Bandita Naik ◽  
Vijay Kaushik ◽  
Munendra Kumar

Abstract The computation of the boundary shear stress distribution in an open channel flow is required for a variety of applications, including the flow resistance relationship and the construction of stable channels. The river breaches the main channel and spills across the floodplain during overbank flow conditions on both sides. Due to the momentum shift between the primary channel and adjacent floodplains, the flow structure in such compound channels becomes complicated. This has a profound impact on the shear stress distribution in the floodplain and main channel subsections. In addition, agriculture and development activities have occurred in floodplain parts of a river system. As a consequence, the geometry of the floodplain changes over the length of the flow, resulting in a converging compound channel. Traditional formulas, which rely heavily on empirical approaches, are ineffective in predicting shear force distribution with high precision. As a result, innovative and precise approaches are still in great demand. The boundary shear force carried by floodplains is estimated by gene expression programming (GEP) in this paper. In terms of non-dimensional geometric and flow variables, a novel equation is constructed to forecast boundary shear force distribution. The proposed GEP-based method is found to be best when compared to conventional methods. The findings indicate that the predicted percentage shear force carried by floodplains determined using GEP is in good agreement with the experimental data compared to the conventional formulas (R2 = 0.96 and RMSE = 3.395 for the training data and R2 = 0.95 and RMSE = 4.022 for the testing data).


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