Aerodynamic Optimization of 3D Micro HAWT Blade via RSM

Document Type : Regular Article

Authors

Laboratory of Environment, Faculty of Sciences and Technology, Echahid Cheikh Larbi Tebessi University, Tebessa, 12002, Algeria

10.47176/jafm.18.2.2769

Abstract

The goal of this research is to redesign the three-dimensional geometry of a micro horizontal-axis wind turbine blade using response surface methodology. The variation of the two influential design parameters, chord length and twist angle, along the blade is geometrically modelled using a fourth- and second- degree polynomial, respectively. Therefore, the optimization process is performed basing on eight input parameters that describe the initial blade design. The performance of the initial and the new optimized wind turbine are compared using CFD and BEM approaches. To well study fluid flow through the wind turbine and assess its performance, the CFD analysis step is carried out using the RANS equations with the k-ω SST turbulence model. Concerning the optimization step, The MOGA (Multi-Objective-Genetic Algorithm) method is employed in an automated manner based on a metamodel with non-parametric regression NPR to identify the best candidate with high efficiency. The performance of turbine rotor types is analyzed using the open source Qblade software and compared with CFD methodology for different TSR (Tip Speed Ratio) values. An increase of 14.65% and 17.17% in power coefficient is marked for CFD and Qblade, respectively, at the design TSR of 3. Compared to the initial blade, the optimal one produces more lift, has a lower separation area, and performs significantly better performance at all TSR values. The detailed representation of 3D flow via pressure distribution and limiting streamlines on both blade surfaces confirm the optimization target which leads to reduce separation zones and improve rotor torque. Additionally, a 37% improvement in starting operability at the lowest wind speed is achieved compared to the initial rotor.

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Main Subjects


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Volume 18, Issue 2 - Serial Number 94
February 2025
Pages 504-517
  • Received: 08 April 2024
  • Revised: 05 September 2024
  • Accepted: 17 September 2024
  • Available online: 04 December 2024