A Data-Driven Design Framework for Wind Turbines via Design-by-Morphing and Bayesian Optimization

Abstract

This study presents a data-driven design optimization framework the integrates Design-by-Morphing (DbM) with Bayesian optimization (BO) to facilitate the discovery of novel, high-performance aerodynamic geometries. Applied to vertical-axis wind turbines (VAWTs), the DbM approach enables the generation of novel shapes that go beyond the conventional constraints of lift-based (Darrieus) and drag-based (Savonius) geometries. Candidate designs were evaluated via expensive high-fidelity Unsteady Reynolds-Averaged Navier-Stokes (URANS) simulations under representative urban wind conditions, with the design space systematically explored to maximize the power coefficient, C_p. From a design space of 6.28 million configurations, the BO-driven framework identified a high-performance design within a remarkably low number of iterations. The optimal configuration achieved a 21.26% increase in power extraction efficiency over existing benchmark designs, while simultaneously maintaining a stable torque profile and structural robustness across a range of Reynolds numbers. Ultimately, this research establishes the DbM-BO architecture as a scalable and computationally efficient pipeline for addressing complex aerodynamic challenges where the derivation of non-intuitive, high-performing surfaces is critical.