The Savonius wind turbine offers promising possibilities for wind energy harvesting in urban environments. However, the disadvantage of low power coefficient constitutes a major impediment to the widespread application of Savonius wind turbines. Aiming at further improvement of the power coefficient, the objective of this research is to optimize the aerodynamic shape of the Savonius wind turbine based on numerical simulations incorporated with an evolutionary-based optimization algorithm.

This thesis presents a computational fluid dynamics (CFD) technique for the aerodynamic characteristics investigation and power coefficient evaluation of the Savonius wind turbine. Two-dimensional (2D) flow around the wind turbine is modeled by the shear-stress transport (SST) k-ω turbulence m...[ Read more ]

The Savonius wind turbine offers promising possibilities for wind energy harvesting in urban environments. However, the disadvantage of low power coefficient constitutes a major impediment to the widespread application of Savonius wind turbines. Aiming at further improvement of the power coefficient, the objective of this research is to optimize the aerodynamic shape of the Savonius wind turbine based on numerical simulations incorporated with an evolutionary-based optimization algorithm.

This thesis presents a computational fluid dynamics (CFD) technique for the aerodynamic characteristics investigation and power coefficient evaluation of the Savonius wind turbine. Two-dimensional (2D) flow around the wind turbine is modeled by the shear-stress transport (SST) k-ω turbulence model and solved through the finite-volume method in ANSYS Fluent. The SST k-ω turbulence model is a two-equation eddy-viscosity model combining the advantages of both the k-ε model for free-stream flows and the k-ω model for boundary-layer flows, thus it ensures a highly accurate prediction of flow separation with adverse pressure gradients. The numerical simulations are conducted at a Reynolds number Re= 1.0 × 10⁵, based on incoming wind velocity (U₀) and the rotor diameter (D).

Inspired by the concepts of Charles Darwin’s theory of evolution and natural selection, a heuristic search approach, namely genetic algorithm (GA), is incorporated into the CFD simulations for aerodynamic shape design optimization of Savonius wind turbines. An automated process has developed to facilitate the optimization, which couples model geometry definition with mesh generation and fitness function evaluation in an iterative manner. Though this approach, the blade shape optimization of Savonius wind turbine and the deflector (an augmentation device) optimization have been conducted. Compared with conventional Savonius wind turbines, the wind turbine with optimal blade shape and the wind turbine with optimal deflector achieve significant power coefficient improvements of 33.4% and 91.6%, respectively. Furthermore, this work also aims to analyze the flow physics underlying the behavior of the optimal designs, thus comparisons of the aerodynamic forces and flow structures on the wind turbines with the optimal and conventional configurations are presented and discussed in detail for one period of rotation.