A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Turbulent Flow Modeling at Tunnel Spillway Concave Bends and Prediction of Pressure using Artificial Neural Network
A tunnel spillway is one of the spillway types in which a high free surface flow velocity is established. The pressure increases in concave vertical bends due to the rotational acceleration and the nature of irregularities in the turbulent flow. Physical models are the best tools to analyze this phenomenon. The number of the required physical models to cover all practical prototype condition analysis is so large that makes it impractical in terms of placement and costs. Therefore, the FLOW-3D software has been chosen to analyze and produce a database of turbulent flow in tunnels concave bends covering all possible practical alternatives. Various tunnels with different discharges and geometries have been simulated by this software. The numerical results were verified with the experimental ones of the constructed physical model of Alborz Dam tunnel spillway, and a satisfactory agreement was obtained. Dimensional analysis is used to group the involved variables of the problem into dimensionless parameters. These parameters are utilized in the artificial neural network simulation. The results showed a correlation coefficient R2=0.95 between the dimensionless parameters obtained by the Flow-3D software and those predicted by the neaural network which leads to the conclusion that the artificial neural network based on the database obtained by the turbulent flow modeling in this regard is a powerful tool for pressure prediction.
Turbulent Flow Modeling at Tunnel Spillway Concave Bends and Prediction of Pressure using Artificial Neural Network
A tunnel spillway is one of the spillway types in which a high free surface flow velocity is established. The pressure increases in concave vertical bends due to the rotational acceleration and the nature of irregularities in the turbulent flow. Physical models are the best tools to analyze this phenomenon. The number of the required physical models to cover all practical prototype condition analysis is so large that makes it impractical in terms of placement and costs. Therefore, the FLOW-3D software has been chosen to analyze and produce a database of turbulent flow in tunnels concave bends covering all possible practical alternatives. Various tunnels with different discharges and geometries have been simulated by this software. The numerical results were verified with the experimental ones of the constructed physical model of Alborz Dam tunnel spillway, and a satisfactory agreement was obtained. Dimensional analysis is used to group the involved variables of the problem into dimensionless parameters. These parameters are utilized in the artificial neural network simulation. The results showed a correlation coefficient R2=0.95 between the dimensionless parameters obtained by the Flow-3D software and those predicted by the neaural network which leads to the conclusion that the artificial neural network based on the database obtained by the turbulent flow modeling in this regard is a powerful tool for pressure prediction.
Turbulent Flow Modeling at Tunnel Spillway Concave Bends and Prediction of Pressure using Artificial Neural Network
Zeinab bashari moghaddam (author) / Hossein Mohammadvali Samani (author) / Seyed Habib Mousavi Jahromi (author)
2021
Article (Journal)
Electronic Resource
Unknown
Metadata by DOAJ is licensed under CC BY-SA 1.0
Pressure Losses for Turbulent Flow through Bends in Series
| British Library Online Contents | 2018
Bumbuna Falls spillway tunnel modelling
Online Contents | 1994
Improved Tunnel-Spillway Flip Buckets
| ASCE | 2021
HYDRAULIC RESEARCH - Studying flow at tunnel bends
| Online Contents | 1999
Improved Tunnel Spillway Flip Buckets
| ASCE | 2021