MLP-Optimized Duct Design for Enhanced Hydrodynamic Performance in Tidal Turbines

The duct, a crucial component of tidal energy power generation devices, is designed to enhance the environmental benefits of tidal energy by optimizing water flow paths and improving energy conversion efficiency. Traditional duct design methods are often considered overly complex, lacking precision, and exhibiting poor optimization efficiency and accuracy. In this study, computational fluid dynamics (CFD) and multi-layer perceptron (MLP) models are employed to investigate the impact of various duct designs on turbine power output and thrust. The MLP model is trained using numerical simulation results, which are then validated by comparing them with experimental data from the literature. Under optimized conditions—specifically, an attack angle of 20°, a blade tip distance of 8 mm, and a cubic curve Xm = 0.796—the power coefficient is found to increase by approximately 11.14% compared to the conventional duct 1, while thrust is reduced by about 52.11% compared to the conventional duct 2. Furthermore, energy loss in the wake vortex is minimized. Flow field analysis is conducted to further confirm the effectiveness of the optimized design, with the high-speed zone area being expanded and pressure extremes reduced by approximately 31.71%. These results demonstrate that machine learning methods can effectively be used to extract nonlinear relationships between complex parameters, offering more design options for duct development and facilitating the engineering application of tidal energy generation technology.