Numerical and statistical analysis of auxiliary geometrical parameter effects on piano key weir discharge capacity

Binit Kumar , Rahil Ahmad , Manish Pandey , Anil Kumar Gupta

AIMS Environmental Science ›› 2024, Vol. 11 ›› Issue (5) : 723 -740.

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AIMS Environmental Science ›› 2024, Vol. 11 ›› Issue (5) :723 -740. DOI: 10.3934/environsci.2024036
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Numerical and statistical analysis of auxiliary geometrical parameter effects on piano key weir discharge capacity
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Abstract

Nowadays, piano key (PK) weir with an expanded crest length are often used to deal with surplus discharge in dams due to unexpected climate change effects, increasing safety. The present study deals with the numerical modelling of a group of PK weirs with auxiliary geometrical parameters to predict the flow over a PK weir using different FLOW-3D turbulence models. The numerical outcomes were compared with the experimental results to check the accuracy of the underlying FLOW-3D models. It was found that the k-𝜀𝜀 turbulence model of FLOW-3D estimated the flow over a piano key weir more closely to the experimental results than the RNG (renormalized group) and LES (large eddy simulation) models. Statistical parameters were used to evaluate the simulated results. It was observed that the coefficient of correlation (CC) was close to one and the root mean square error (RMSE) close to zero when numerical outcomes were compared with experimental datasets. The results show that the FLOW-3D software is quite effective in estimating the flow. Therefore, the present study will help to understand the best combination of mesh, models, adaption and convergence processes in simulation and provide an insight into the numerical analysis of flow configuration over PKW by considering one of the best numerical models.

Keywords

crest length / discharge coefficient / FLOW-3D / labyrinth weir / piano key weir

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Binit Kumar, Rahil Ahmad, Manish Pandey, Anil Kumar Gupta. Numerical and statistical analysis of auxiliary geometrical parameter effects on piano key weir discharge capacity. AIMS Environmental Science, 2024, 11 (5) : 723-740 DOI:10.3934/environsci.2024036

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