Using a hybrid artificial intelligence approach to estimate length of the hydraulic jump caused by novel kind of Sharp-Crested V-notch weirs

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Küçük Resim

Tarih

2022

Dergi Başlığı

Dergi ISSN

Cilt Başlığı

Yayıncı

Taylor & Francis Ltd

Erişim Hakkı

info:eu-repo/semantics/closedAccess

Özet

In this study, the SCVW (a novel type of Sharp-Crested V-notch weirs) is employed for more experimentally and theoretically investigations. The length of the hydraulic jump at downstream of the SCVW (L-jSCVW) is measured via new supplementary experimental datasets via most popular vertex angles theta (128 degrees and 60 degrees). The experiments are conducted under aerated, steady, and free overflow conditions in an open channel for large physical models. To assess the variations of L-jSCVW versus the theta, extensive laboratory work is performed at different discharges (Q), diverse triangular segments number (N-st) in the SCVW, and various tailgate angles (Phi). Via dimensional analysis and pre-processing method, most effectual parameters on the L-jSCVW were obtained as Fr-1 (approaching Froude number), Re-1 (incoming Reynolds number), and y(2)/y(1) (relative sequent depths). Based on the experimental results, by increasing the values of Fr-1 and Re-1, the values of L-jSCVW noticeably increased. Three types of data-driven models (DDMs), namely, support vector regression (SVR), gene expression programming (GEP), and a robust hybrid model entitled hybrid (SVR-ACO) are developed to estimate the L-jSCVW. The suggested hybrid model is a coupled form of SVR with ant colony optimization (ACO) algorithm, which is used to enhance the estimation precision of the L-jSCVW. According to the attained statistical indices (determination coefficient (R-2) = 0.98, root mean square error (RMSE) = 0.191, and value of total grade (TG) = 19.78) and scatter plots, the hybrid SVR-ACO model was determined as the most superior method to estimate the L-jSCVW with high accuracy.

Açıklama

Anahtar Kelimeler

Experimental Model, Length of Hydraulic Jump, The SCVW, Open Channel Flow, Data-Driven Methods

Kaynak

European Journal of Environmental and Civil Engineering

WoS Q Değeri

Q2

Scopus Q Değeri

Q2

Cilt

26

Sayı

13

Künye