Optimization of Savonius wind turbine with additional blades by surrogate model using artificial neural networks. (1st May 2023)

• Record Type: Journal Article
• Authors: Haddad, Hassan Z.; Mohamed, Mohamed H.; Shabana, Yasser M.; Elsayed, Khairy
• Is Part Of: Energy. Volume 270(2023)
• Publication Date: 2023-05-01
• Subjects: Wind turbine -- Savonius rotor -- Bach-type -- Additional blades -- Outer blades -- CFD -- Shape optimization -- Artificial neural networks -- Surrogate models; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Energy consumption -- Periodicals; 333.7905
• DOI: 10.1016/j.energy.2023.126952
• Languages: English
• ISSNs: 0360-5442

Abstract

Abstract: The aim of the current investigation is to obtain the optimum configuration of Savonius wind turbine which results in maximum power coefficient ( C p ). In order to achieve that Surrogate-based optimization ( SBO ) was used for obtaining the optimum values of investigated parameters. Design of experiment ( DoE ) was applied on four variables which are: the arc angle of original blade ( ψ ), the shape factor of original blade ( p/q ), the arc angle of additional blades ( β ), and the additional blade radii ratio ( R r ). Computational fluid dynamics ( CFD ) simulations using ANSYS FLUENT were conducted to feed the artificial neural networks ( ANN ) with the sufficient data for training for the traditional and optimized rotors. The considered original blade arc angle ( ψ ) and the additional blades angle ( β ) varied from 70° to 180°, the original blade shape factor ( p/q ) ranges from 0.00 to 0.70, and the additional blade radii ratio ( R r ) changes from 0.20 to 1.25 of that of the original blade. The optimized rotor showed a maximum C p of 0.2836 results in 44.5% increase in C p over 0.1962 of conventional one for the tip speed ratio ( TSR ) of 0.75. The maximum increase is 66.12% at TSR = 1.3. Highlights: Optimization of the shape of Savonius rotor with additional blades with different parameters. Surrogate-based optimization was employed to determine the optimal parameters. Using the adjoint method and the surrogate model proved a huge improvement in C p . The optimum design introduces maximum C p = 0.2836 with 44.5% enhancement.