A Novel Parametric Modeling Method and Optimal Design for Savonius Wind Turbines

Abstract

Abstract:Under the inspiration of polar coordinates, a novel parametric modeling and optimization method for Savonius wind turbines was proposed to obtain the highest power output, in which a quadratic polynomial curve was bent to describe a blade. Only two design parameters are needed for the shape-complicated blade. Therefore, this novel method reduces sampling scale. A series of transient simulations was run to get the optimal performance coefficient (power coefficient C p ) for different modified turbines based on computational fluid dynamics (CFD) method. Then, a global response surface model and a more precise local response surface model were created according to Kriging Method. These models defined the relationship between optimization objective C p and design parameters. Particle swarm optimization (PSO) algorithm was applied to find the optimal design based on these response surface models. Finally, the optimal Savonius blade shaped like a "hook" was obtained. C m (torque coefficient), C p and flow structure were compared for the optimal design and the classical design. The results demonstrate that the optimal Savonius turbine has excellent comprehensive performance. The power coefficient C p is significantly increased from 0.247 to 0.262 (6% higher). The weight of the optimal blade is reduced by 17.9%.

Computational fluid dynamics on a newly developed Savonius rotor by adding sub-buckets for increase of the tip speed ratio to generate higher output power coefficient

“…In this study, two-dimensional simulation was carried out to reduce computational cost. Though two-dimensional simulations may fail to consider the three-dimensional effect, previous studies have shown that two-dimensional give acceptable results for Savonius rotor (Tian et al, 2015;Zhang et al, 2017). The regularized lattice Boltzmann method (RLBM) for the D2Q9 lattice speed model was used as a governing equation (Izham et al, 2011;Morinishi et al, 2016).…”

“…Many researchers have investigated the effect of the modified main bucket shape. For example, Zhang et al (2017) modified the traditional Savonius rotor and found that the output power coefficient of this rotor improved by about 6% when compared with the traditional one. AI-Faruk et al (2016) investigated the effect of the arc angle for the cylinder bucket and found that the bucket with the arc angle of 195° improved the output power coefficient by about 29% compared with one with an arc angle of 180°.…”

Optimal Design of PMSM Based on Automated Finite Element Analysis and Metamodeling

“…This is because the predictive performance of the metamodel affects the reliability of the optimal design. Most of the existing studies have been metamodeled by a single method such as Kriging and RSM, and the accuracy evaluation has not been performed [10][11][12][13][14][15][16][17][18]. In this study, however, metamodels for the objective function and constraints are generated in 11 ways provided by PIAnO, and the best metamodels are selected, respectively, by comparing the RMSE test results to evaluate the metamodel accuracy.…”

“…To obtain a reliable optimal design result, a large number of DOE have to be carried out. However, the conventional method of manually performing the process was complicated and time consuming, and thus the number of DOE was limited [10][11][12][13][14][15][16][17][18]. However, using the automated DOE process proposed in this study, not only can the DOE be easier but also the number of DOE can be dramatically increased, resulting in high reliability of the optimal design result.…”

“…Most of the previous studies have been applied to optimal design using metamodel generated in one way. There have been a lot of optimizations recently using a single metamodeling technique such as Kriging and response surface method [10][11][12][13][14][15][16][17][18]. However, since a suitable metamodel is different according to each design problem and condition, it is necessary to select the best metamodel through accuracy evaluation after generating several metamodels.…”