Flow Discharge Prediction Study Using a CFD-Based Numerical Model and Gene Expression Programming

Mozaffari, S; Amini, E; Mehdipour, H; Neshat, M

Flow Discharge Prediction Study Using a CFD-Based Numerical Model and Gene Expression Programming

Mozaffari, S Amini, E Mehdipour, H Neshat, M

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Publisher:: MDPI
Publication Type:: Journal Article
Citation:: WATER, 2022, 14, (4)
Issue Date:: 2022-02-01

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Field	Value	Language
dc.contributor.author	Mozaffari, S
dc.contributor.author	Amini, E
dc.contributor.author	Mehdipour, H
dc.contributor.author	Neshat, M https://orcid.org/0000-0002-9537-9513
dc.date.accessioned	2023-05-23T09:50:56Z
dc.date.available	2023-05-23T09:50:56Z
dc.date.issued	2022-02-01
dc.identifier.citation	WATER, 2022, 14, (4)
dc.identifier.issn	2073-4441
dc.identifier.issn	2073-4441
dc.identifier.uri	http://hdl.handle.net/10453/170413
dc.description.abstract	<jats:p>The significance of spillways is to allow the flood to be safely discharged from downstream. There is a strong correlation between the poor design of spillways and the failures of dams. In order to address this concern, the present study investigates the flow over the Nazloo-ogee spillway using the CFD 3D numerical model and an artificial intelligence method called Gene Expression Programming (GEP). In a physical model, discharge and flow depths were calculated for 21 different total heads. Among different turbulence models, the RNG turbulence model achieved the maximum compatibility in computational fluid dynamic simulation. In addition, GEP was used to estimate Q, in which 70% of collected data was dedicated to training and 30% to testing. R2, RMSE, and MAE were obtained as performance criteria, and the new mathematical equation for the prediction of discharge was obtained using this model. Finally, the numerical model and GEP outputs were compared with the experimental data. According to the results, the numerical model and GEP exhibited a high level of correspondence in simulating flow over an ogee-crested spillway.</jats:p>
dc.language	English
dc.publisher	MDPI
dc.relation.ispartof	WATER
dc.relation.isbasedon	10.3390/w14040650
dc.rights	info:eu-repo/semantics/openAccess
dc.title	Flow Discharge Prediction Study Using a CFD-Based Numerical Model and Gene Expression Programming
dc.type	Journal Article
utslib.citation.volume	14
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	open_access	*
dc.date.updated	2023-05-23T09:50:54Z
pubs.issue	4
pubs.publication-status	Published
pubs.volume	14
utslib.citation.issue	4

Abstract:

The significance of spillways is to allow the flood to be safely discharged from downstream. There is a strong correlation between the poor design of spillways and the failures of dams. In order to address this concern, the present study investigates the flow over the Nazloo-ogee spillway using the CFD 3D numerical model and an artificial intelligence method called Gene Expression Programming (GEP). In a physical model, discharge and flow depths were calculated for 21 different total heads. Among different turbulence models, the RNG turbulence model achieved the maximum compatibility in computational fluid dynamic simulation. In addition, GEP was used to estimate Q, in which 70% of collected data was dedicated to training and 30% to testing. R2, RMSE, and MAE were obtained as performance criteria, and the new mathematical equation for the prediction of discharge was obtained using this model. Finally, the numerical model and GEP outputs were compared with the experimental data. According to the results, the numerical model and GEP exhibited a high level of correspondence in simulating flow over an ogee-crested spillway.

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http://hdl.handle.net/10453/170413