Experimental study and performance comparison on various types of rectangular piano key side weirs at a 120° section of a 180° curved channel

Abstract

Application of side weirs with high effective length is necessary to discharge excessive flows, to control the flow in water conveyance systems, and irrigation and drainage systems. Most of the studies on the side weirs have been conducted on the straight channels and linear weirs. The flow pattern on the outer arc of the curved channels and its suitability for side weir can be used and combined with the piano key weirs. So far, no comparison has been made on rectangular piano key side weirs (RPKSW) at a 120° Section of a 180° Curved Channel. In this study, an experimental study was performed on A-, B-, C-, and D-type RPKSW at a bend angle of 120 degrees. The results showed that the specific energy at two ends of the RPKSWs was the same, with a slight difference of 3.4% for A-Type, 1.3% for B-Type, 1.1% for C-Type, and 1.8% for D-Type weirs. The discharge coefficients of the studied weirs were also investigated, and it was concluded that B-Type weir has better performance than other weirs. On average, the discharge coefficient of B-Type weir was 9.9%, 21.2%, and 24.1% higher than that of A-Type, C-Type, and D-Type weir, respectively. It was shown that the ratio of P/h1 is the main parameter affecting the weir discharge coefficient. Finally, an empirical equation was proposed for each weir. The proposed equation has MAE = 0.028 for A-Type weir, MAE = 0.041 for B-Type weir, MAE = 0.049 for C-Type weir, and MAE = 0.053 for D-Type weir.

Hydraulic Behavior of Weir-Gate Structure with Rectangular Side Obstacle under Free and Submerged Flow Conditions

“…The literature extensively records the broad efficacy of machine learning techniques in modeling hydraulic issues, particularly in forecasting the discharge coefficient of different types of weirs. This success has been evidenced in several researches [35][36][37][38][39][40]. Machine learning methods provide a combination of high precision and effectiveness in predicting discharge coefficients.…”

“…This enables more dependable and precise forecasts in the planning and functioning of hydraulic infrastructure. Scientists have examined the utilization of artificial intelligence methods, such as artificial neural networks, adaptive neuro fuzzy inference systems, support vector machines, and group technique of data processing [26,37,42]. Safari et al [43] evaluated various models -Artificial Neural Networks (ANN), Gene Expression Programming (GEP), and regression models -in estimating the discharge coefficient for rectangular broadcrested weirs.…”