A novel heuristic method for optimization of straight blade vertical axis wind turbine. (1st November 2016)

• Record Type: Journal Article
• Authors: Tahani, Mojtaba; Babayan, Narek; Mehrnia, Seyedmajid; Shadmehri, Mehran
• Is Part Of: Energy conversion and management. Volume 127(2016)
• Page Start: 461
• Page End: 476
• Publication Date: 2016-11-01
• Subjects: VAWT -- Heuristic optimization -- Continuous/discrete problem -- DMST -- CFD; Direct energy conversion -- Periodicals; Energy storage -- Periodicals; Energy transfer -- Periodicals; Énergie -- Conversion directe -- Périodiques; Direct energy conversion; Periodicals; 621.3105
• DOI: 10.1016/j.enconman.2016.08.094
• Languages: English
• ISSNs: 0196-8904

Abstract

Highlights: A novel heuristic method has been proposed for optimization of VAWTs. The proposed method is the combination of DMST model with heuristic algorithms. A continuous/discrete optimization problem has been solved. A novel continuous optimization algorithm has been developed. The CFD simulation of the optimized geometry has been carried out. Abstract: In this research study it is aimed to propose a novel heuristic method for optimizing the VAWT design. The method is the combination of continuous/discrete optimization algorithms with double multiple stream tube (DMST) theory. For this purpose a DMST code has been developed and is validated using available experimental data in literature. A novel continuous optimization algorithm is proposed which can be considered as the combination of three heuristic optimization algorithms namely elephant herding optimization, flower pollination algorithm and grey wolf optimizer. The continuous algorithm is combined with popular discrete ant colony optimization algorithm (ACO). The proposed method can be utilized for several engineering problems which are dealing with continuous and discrete variables. In this research study, chord and diameter of the turbine are selected as continuous decision variables and airfoil types and number of blades are selected as discrete decision variables. The average power coefficient between tip speed rations from 1.5 to 9.5 is considered as the objective function. The optimization results indicated that the optimized geometry can produce a maximum power coefficient, 44% higher than the maximum power coefficient of the original turbine. Also a CFD simulation of the optimized geometry is carried out. The CFD results indicated that the average vorticity magnitude around the optimized blade is larger than the original blade and this results greater momentum and power coefficient.