Simulation of relative energy loss downstream of multi-gate regulators using ANN. Issue 1 (31st December 2022) - Sauida, Mohamed F.
Abstract: Regulators are commonly used to control and measure the flow in streams and irrigation canals. The number of opened gates and their arrangements significantly affected the flow characteristics downstream (DS) of multi-gate regulators. For the first time, an Artificial Neural Network (ANN) is utilized to forecast the relative energy loss of the submerged hydraulic jump (H.J) generated DS of multi-gate regulators under various arrangements of opened gates. The data used for training the network was collected from experimental work conducted at the Hydraulic Research Institute (HRI) on a model of a regulator with five vents. Different flow conditions and different expansions are used through the experimental program. Seventy percent of the data is used to train the network, while the rest of the data is used to validate and test the developed ANN model. A tanh activation function is used in the hidden layer of the ANN network, which consists of 8–11-1. The determination coefficients (R 2 ) and MRAE of the ANN model were 0.9278 and 0.016, respectively. Also, an empirical prediction equation is developed using statistical multiple line regression (MLR). The results show that ANN is more accurate than MLR and the preceding theoretical model. The ANN model can be utilized to determine the optimal multi-gate operation scenario for multi-vent regulators. Short summary: This study aims to incorporate the artificial intelligence methodology as a modern modeling method for predicting downstream flow characteristics under multi-gate management and operating types. In the current analysis, the Artificial Neural Network (ANN), in particular, is used in combination with experimental findings to predict the relative energy loss of the submerged hydraulic jump (H.J) occurring downstream (DS) of multi-gate hydraulic structures within the various gate operation cases. Results indicate that ANN is much more efficive in modeling the relative energy los than the multiple linear regression (MLR) and the previous theoretical model.