Research on Aerodynamic Performance of J-type Blade Vertical Axis Wind Turbine

Pan, Lin; Xiao, Haodong; Zhang, Yanwei; Shi, Zhibin

doi:10.1109/ccdc49329.2020.9164408

Cited by 4 publications

(4 citation statements)

References 11 publications

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“…At this point, the results are said to be grid independent. accuracy and computational cost could be gained using the 𝑘-𝜀 standard model (Pan et al 2020;Jang et al 2021;Ahmad et al 2022), which was adopted for the subsequent CFD simulations.…”

Section: Computational Grid and Grid Independencementioning

confidence: 99%

Surrogate-Based Design Optimization of a H-Darrieus Wind Turbine Comparing Classical Response Surface, Artificial Neural Networks, and Kriging

2023

JAFM

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Clean energy sources like wind energy have been receiving much attention, and great emphasis has been given to the design and optimization of horizontal axis wind turbines, but just as important are the vertical axis wind turbines that can be used for generating energy for small businesses, houses, and buildings. This article sought to study the optimal geometrical parameters of a H-Darrieus vertical axis wind turbine using surrogate-based optimization with three different types of surrogate models and compared them. Airfoil chord and thickness were chosen as the design variables and respective ranges set at 0.32-0.6 m and 0.04-0.16 m. All evaluations are carried out for a tip-speed ratio of 1.5. Three different surrogate models were used and compared, namely a quadratic polynomial response surface, an artificial neural network based on radial basis functions called Extreme Learning Machine and a Kriging interpolator. Surrogates were constructed based on an initial sample data distributed according to a full factorial design. A test set was designed to evaluate the accuracy of the surrogates. Both training and testing data sets were generated using 2D CFD modeling to reduce computational cost. From the test set, Extreme Learning Machine surrogate showed the smallest RMSE of 11.24%, followed by Kriging, at 17.64%, and Response Surface of 22.17%. For the optimal designs the same pattern ensued, with optimal power coefficient overestimated by 8.7% for the response surface surrogate, followed by 3.12% and 2.17% for the Kriging interpolator and the Extreme Learning Machine, respectively. Power coefficient curves comparing the three optimal geometries from each surrogate were calculated and plotted. Optimal turbine obtained from Kriging surrogate optimization process resulted in a 7.92% increase in the Cp, whilst Extreme Learning Machine and Response Surface resulted in a 7.86% and 4.29% increase, respectively, all when compared to baseline CFD model. Concluding guidelines are that the quadratic polynomial response surface may not be the best alternative when dealing with complex non-linear relationships as typically present in VAWT simulations. Superior techniques such as Extreme Learning Machine and Kriging could be more suitable for this application.

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Section: Computational Grid and Grid Independencementioning

confidence: 99%

Surrogate-Based Design Optimization of a H-Darrieus Wind Turbine Comparing Classical Response Surface, Artificial Neural Networks, and Kriging

2023

JAFM

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“…Pada pressure outlet nilainya sebesar 101,325 Pa [14] . Diskretisasi momentum yang digunakan adalah second orde, sedangkan calculation algorithm dipilih jenis simple [15][16] . Report definition digunakan untuk menampilkan torsi.…”

Section: Pre-processingunclassified

Optimasi Desain Sudu Turbin Dengan Paduan Savonius Dan Darrieus Untuk Meningkatkan Daya

Al-Janan,

Saputra

2023

JRM

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In this study, the effectiveness of the combination of the Savonius and Darrieus wind turbines was investigated, which is a vertical axis wind turbine. The aim is to determine the optimal blade design and analysis of the combined blade design compared to the stand-alone design. The method used is CFD simulation to find the torque value used for power calculation. From all simulations, the highest torque and power occurred at a wind speed of 5.58 m/s. The simulation shows the Savonius, Darrieus, and Savonius-Darrieus combination turbine variations, - so then as result from the Savonius-Darrieus combination obtained the best results, namely the torque of 82.59 Nm and the power of 2194.41 W. In conclusion, the turbine model with a combined blade design produces better torque and power compared to stand-alone turbine models.

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“…It was also shown that wake velocity recovered sooner. Pan [74] disputes these results, with overall reduced performance compared to conventional blades. J-blades also require investigation from a structural design viewpoint.…”

Section: Performance Enhancing Modificationsmentioning

confidence: 99%

Towards a standard approach for future Vertical Axis Wind Turbine aerodynamics research and development

Barnes

Marshall-Cross

Hughes

2021

Renewable and Sustainable Energy Reviews

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Research on Aerodynamic Performance of J-type Blade Vertical Axis Wind Turbine

Cited by 4 publications

References 11 publications

Surrogate-Based Design Optimization of a H-Darrieus Wind Turbine Comparing Classical Response Surface, Artificial Neural Networks, and Kriging

Surrogate-Based Design Optimization of a H-Darrieus Wind Turbine Comparing Classical Response Surface, Artificial Neural Networks, and Kriging

Optimasi Desain Sudu Turbin Dengan Paduan Savonius Dan Darrieus Untuk Meningkatkan Daya

Towards a standard approach for future Vertical Axis Wind Turbine aerodynamics research and development

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