Intelligent parameter optimization of Savonius rotor using Artificial Neural Network and Genetic Algorithm. (15th January 2018)

• Record Type: Journal Article
• Authors: Mohammadi, M.; Lakestani, M.; Mohamed, M.H.
• Is Part Of: Energy. Volume 143(2018)
• Page Start: 56
• Page End: 68
• Publication Date: 2018-01-15
• Subjects: Computational intelligence -- Optimization -- Savonius turbine -- Wind energy -- CFD; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Energy consumption -- Periodicals; 333.7905
• DOI: 10.1016/j.energy.2017.10.121
• Languages: English
• ISSNs: 0360-5442

Abstract

Abstract: Power coefficient, the most significant criterion for evaluating the performance of Savonius rotor is a multi-dimensional function of numerous parameters like overlap ratio, number of stages, blade rotation, etc. All these parameters have been examined separately and an approximate span in which optimum performance can be attained is proposed for each one. Furthermore, neither any attempt on scrutinizing this range accurately nor any investigations on probing the probability of existence of any interacting relation among these parameters have been reported so far. Using computational intelligence, an accurate study toward this span and a probable relation among these parameters has been conducted. Power coefficient is considered as a function of six independent input parameters, according to experimental data extracted from a related paper. An Artificial Neural Network has been assigned to investigate a logical interaction among dependent and independent variables and define a cost function based on same empirical data. This function is then optimized by Genetic Algorithm and best amount for each parameter has been determined. Suggested geometry and flow field conditions have then been simulated by Computational Fluid Dynamics and acceptable agreement is detected. Highlights: Present study considers multidimensional function to improve a drag wind turbine. Artificial Neural Network was assigned to define a cost function. Cost function is optimized by Genetic Algorithm to obtain the optimum design. Optimum geometry has been simulated by CFD to get the off design performance. A more tranquil flow-field around the optimal rotor is observed.