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

Abstract

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 θ (128° and 60°). 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 θ, extensive laboratory work is performed at different discharges (Q), diverse triangular segments number (N_st) in the SCVW, and various tailgate angles (ɸ). Via dimensional analysis and pre-processing method, most effectual parameters on the L_jSCVW were obtained as Fr₁ (approaching Froude number), Re₁ (incoming Reynolds number), and $\frac{{\mathrm{y}}_2}{{\mathrm{y}}_1}$ (relative sequent depths). Based on the experimental results, by increasing the values of Fr₁ and Re₁, 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 (R²) = 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.

Keywords:

• Experimental model
• length of hydraulic jump
• the SCVW
• open channel flow
• data-driven methods

Acknowledgment

The author appreciates extremely from Mr. Hamid Saadatnejadgharahassanlou for providing experimental datasets.

Disclosure statement

No potential conflict of interest was reported by the author.