Naik, B, Khatua, KK, Wright, N et al. (2 more authors) (2018) Numerical modeling of converging compound channel flow. ISH Journal of Hydraulic Engineering, 24 (3). pp. 285-297. ISSN 0971-5010
Abstract
This paper presents numerical analysis for prediction of depth-averaged velocity distribution of compound channels with converging flood plains. Firstly, a 3D Computational Fluid Dynamics model is used to establish the basic database under various working conditions. Numerical simulation in two phases is performed using the ANSYS-Fluent software. k-ω turbulence model is executed to solve the basic governing equations. The results have been compared with high-quality flume measurements obtained from different converging compound channels in order to investigate the numerical accuracy. Then Artificial Neural Network are trained based on the Back Propagation Neural Network technique for depth-averaged velocity prediction in different converging sections and these test results are compared with each other and with actual data. The study has focused on the ability of the software to correctly predict the complex flow phenomena that occur in channel flows.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | (c) 2017, Indian Society for Hydraulics. This is an Accepted Manuscript of an article published by Taylor & Francis in the ISH Journal of Hydraulic Engineering on 20 September 2017, available online: https://doi.org/10.1080/09715010.2017.1369180 |
Keywords: | Compound channel, stage discharge, prismatic, non-prismatic, ANN, ANSYS |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Civil Engineering (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 25 Jan 2018 12:08 |
Last Modified: | 20 Sep 2018 00:39 |
Status: | Published |
Publisher: | Taylor & Francis |
Identification Number: | 10.1080/09715010.2017.1369180 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:126633 |