A novel generative approach to the parametric design and multi-objective optimization of horizontal axis tidal turbines

As the demand for ocean energy continues to grow, the development of efficient design and optimization methods for tidal current turbines is crucial. Traditional approaches, often based on parameterized models, face challenges in fully capturing the intricate geometric features of turbine blades, limiting the optimization space and affecting convergence efficiency. In response, this study introduces a novel design methodology for horizontal axis tidal turbines (HATTs) using a variational autoencoder generative adversarial network (VAEGAN) model. This approach uses unsupervised learning from a custom dataset to generate new HATT designs, with the VAEGAN model encoding distinct geometric features of turbine blades within a compact latent space, enabling more efficient design space exploration and facilitating the discovery of innovative shapes. Furthermore, in the multi-objective optimization process targeting both hydrodynamic performance and structural strength, the reduced dimensionality of the design variables accelerates convergence while maintaining a broad and meaningful design space. The proposed methodology demonstrates the VAEGAN model's ability to generate diverse and effective turbine blade designs, highlighting its potential as a powerful tool in advancing HATT technology.