Abstract
In this work, a 3D computational model based on computational fluid dynamics (CFD) is built to simulate the aerodynamic behavior of a Savonius-type vertical axis wind turbine with a semi-elliptical profile. This computational model is used to evaluate the performance of the wind turbine in terms of its power coefficient (Cp). Subsequently, a full factorial design of experiments (DOE) is defined to obtain a representative sample of the search space on the geometry of the wind turbine. A dataset is built on the performance of each geometry proposed in the DOE. This process is carried out in an automated way through a scheme of integrated computational platforms. Later, a surrogate model of the wind turbine is fitted to estimate its performance using machine learning algorithms. Finally, a process of optimization of the geometry of the wind turbine is carried out employing metaheuristic optimization algorithms to maximize its Cp; the final optimized designs are evaluated using the computational model for validating their performance.
Original language | English |
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Article number | 233 |
Journal | Energies |
Volume | 15 |
Issue number | 1 |
DOIs | |
State | Published - 1 Jan 2022 |
Keywords
- CAE model
- Computational fluid dynamics
- Evolutionary algorithms
- Machine learning
- Optimization
- Surrogate model
- Vertical axis wind turbine