relation: http://eprints.science-line.com/id/eprint/978/
title: Effects of Divergence Shape on the Characteristics of Hydraulic Jump in Stilling Basins Using Numerical Simulation and Neural Networks
creator: Aghamagidi, Roozbeh
creator: Firooznia, Dariush
creator: Vaezi, Mostafa
subject: TA Engineering (General). Civil engineering (General)
description: Measures such as sudden cross-sectional divergence is among the factors affecting the characteristics of hydraulic jump. If for any reason it is not possible or cost-effective to provide the depth required for the hydraulic jump, then gradual or sudden flow cross-sectional divergence can be a good way to reduce the depth required for the jump. In this research, using the neural network and FLOW-3D numerical model, a three-dimensional (3D) model for fluid simulation, the effect of sudden divergence stilling basin on characteristics was simulated. The results of the neural network are very close to the physical model. The study revealed that 3D simulation using Flow-3D software could simulate a hydraulic jump with an average error of 2.41%. The efficiency of stilling basins divergent was calculated to be 71%, which is higher than the classic stilling basins with an efficiency of 53.3%. Depth after jumping in divergent stilling basins modeled at 27.8 and 41.4 l/s was found to be 12 and 25% less than classic basins, respectively. Compared to the classical mode in the divergent stilling basins parabolic, gradual, and sudden, the decrease in jump length was found to be 25.9%, 27.5%, and 31.8%, respectively. The results showed that the sudden divergent stilling basin has the best performance in terms of hydraulic parameters.
publisher: Scienceline Publication, Ltd
date: 2021-11-25
type: Article
type: PeerReviewed
format: text
language: en
identifier: http://eprints.science-line.com/id/eprint/978/1/JCEU%2011%286%29%2065-73%2C%202021.pdf
identifier:    (2021)     Effects of Divergence Shape on the Characteristics of Hydraulic Jump in Stilling Basins Using Numerical Simulation and Neural Networks.   Journal of Civil Engineering and Urbanism.  pp. 65-73.  ISSN 22520430   
relation: https://doi.org/10.54203/jceu.2021.9
relation: doi:10.54203/jceu.2021.9