Multiple nonlinear regression-based functional relationships of energy loss for sluice gates under free and submerged flow conditions

Sluice gates are commonly used to measure water discharge and to adjust the water level in open canals. Estimation of head loss (ΔE) and discharge coefficients (Cd) for a sluice gate is essential for the design of open canals. Depending on the downstream water level, free or submerged flow conditions may occur. Although there have been some investigations on Cd for sluice gates, a comprehensive literature review shows that there are no studies of ΔE (to the best knowledge of the authors). Knowledge of ΔE is necessary for the design of intakes and irrigation canal inverts. This study investigates ΔE and Cd using scaled experimentation. Results show that ΔE for free flow is greater than that for submerged flow conditions. Meanwhile, discharge coefficients in a free flow are greater than those under submerged flow conditions. Relative energy losses (ΔE) have a minimum value of 0.271 and a maximum value of 0.604. These high energy losses cannot be ignored in intake structures and canal-designing processes, and their impact on minor canal inverts receiving water from main canals should be considered. Application of multiple nonlinear regression (MNR) models is presented for predicting ΔE and Cd. The MNR method yields accurate predictions.