Harnessing AI for Hydraulic Efficiency: A Comparative Study of Numerical Modeling and Machine Learning in Energy Dissipation Over a Vertical Drop Structure

Abstract

In hydraulic engineering, accurately predicting energy dissipation at vertical drop structures is crucial for ensuring structural integrity and optimizing design efficiency. This study presents a comparative analysis of energy dissipation calculations using two methodologies: numerical modeling through Computational Fluid Dynamics (CFD) and advanced Machine Learning (ML) techniques. The study is performed at vertical drop structure at Lower Gogera Branch Canal (LGBC) in Pakistan, experimental data has been utilized alongside CFD simulations to establish a reliable benchmark for energy dissipation at a vertical drop of 3.69 meters, with a discharge rate of 63.73 m³/s. The CFD method employs the Volume of Fluid (VOF) technique to simulate flow dynamics, providing precise insights into energy dissipation. In parallel, we implement various ML algorithms, including decision trees, random forests, and neural networks, to model energy dissipation based on key input parameters such as flow rate, height of fall, and downstream conditions. The performance of the ML models is evaluated against the numerical results, allowing for a comprehensive comparison of their predictive capabilities. Initial findings indicate that while the CFD model shows a high level of accuracy with discrepancies within 5% of empirical measurements, the ML techniques also demonstrate significant potential in capturing complex, nonlinear relationships inherent in energy dissipation phenomena. The use of ML not only provides a faster alternative for predictions but also offers insights into optimizing design parameters in hydraulic structures. This research highlights the transformative impact of AI methodologies on hydraulic engineering, illustrating how integrating ML can enhance traditional modeling techniques. The results contribute to the ongoing discourse on the application of artificial intelligence in water resources management, paving the way for more efficient and sustainable engineering practices.