A Method for Constructing Open-Channel Velocity Field Prediction Model Based on Machine Learning and CFD

Abstract

Rapid and accurate prediction of the sectional velocity field of the channel is of great significance to the design and maintenance of open channels and the improvement of irrigation efficiency. During the water delivery process of Renmin Canal of Dujiangyan irrigation system, the water level of the main canal changes rapidly and in a large range, which is the biggest difficulty in real-time prediction of its velocity field. Therefore, based on machine learning, this paper proposes a new method to construct a real-time velocity field prediction model, which can directly predict the velocity field of the channel according to the water level. According to this method, the computational fluid dynamics (CFD) technology is used to simulate the target open channel, and a machine learning model that can adaptively optimize the characteristics of the velocity field data is designed as the velocity field prediction model, which is experimented in the main canal of Renmin Canal of Dujiangyan irrigation system. The results suggest that the predictions are in line with the general features of flow velocity distribution in open channels and have high precision. Therefore, this method is of high value for engineering application and theoretical research.