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Optimal Design of Archimedes Wind Turbine Using Intelligent Methods

Salah Samiani, Omid | 2024

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 57340 (46)
  4. University: Sharif University of Technology
  5. Department: Energy Engineering
  6. Advisor(s): Boroushaki, Mehrdad
  7. Abstract:
  8. This study focuses on optimizing the design of an Archimedes wind turbine using genetic algorithms. Unlike traditional lift-type wind turbines analyzed through methods like Blade Element Momentum (BEM) theory or Double Stream Tube Method (DSTM), the unique design of the Archimedes turbine requires a different approach. Therefore, Computational Fluid Dynamics (CFD) was employed to evaluate the performance of the design, and the SST k-ω model was used to solve the Navier-Stokes equations, which lead to finding the distribution of pressure and velocity on the rotor. Subsequently, for validation of the simulation results, the obtained data was compared with the results available in one of the previous studies, and it was determined that they only differ by 5.9% in terms of Mean Absolute Error (MAE). Following the validation process, a Genetic Algorithm (GA) was chosen to tackle the optimization challenges presented by this turbine’s design. This study explored various scenario involving changes to parameters such as angle, pitches, and rotational speed, either individually or in combination. In the most comprehensive scenario, where the search space includes all parameters, an optimized Archimedean wind turbine with an angle of 63.49 degrees, a rotational speed of 59/03 radians per second (tip speed ratio of 1.12), Pitch 1 with a value of 115.03 millimeters and Pitch 2 with a value of 389.54 millimeters was obtained, yielding a power coefficient equal to 0.2644. Furthermore, in this scenario, the optimized model’s power coefficient improved by 72.27% compared to the base model’s power coefficient. The algorithm converged to its convergence criterion after 24 iterations, within a time span of 27 hours and 45 minutes
  9. Keywords:
  10. Wind Energy ; Computational Fluid Dynamics (CFD) ; Genetic Algorithm ; Optimization ; Archimedes Wind Turbine ; Blade Element Momentum Theory

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