Integrated surrogate optimization of a vertical axis wind turbine

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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 languageEnglish
Article number233
JournalEnergies
Volume15
Issue number1
DOIs
StatePublished - 1 Jan 2022

Keywords

  • CAE model
  • Computational fluid dynamics
  • Evolutionary algorithms
  • Machine learning
  • Optimization
  • Surrogate model
  • Vertical axis wind turbine

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