Improvement of Aerodynamic Performance of Savonius Wind Rotor Using Straight-Arc Curtain

Zhang, Hongfu; Li, Zhiqiang; Xin, Dabo; Jin, Zuanming

doi:10.3390/app10207216

Cited by 11 publications

(10 citation statements)

References 20 publications

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“…In particular, the conservation of mass and momentum were implemented. As a turbulence model, the SST k-ω was employed since several studies revealed its estimation capabilities in VAWTs [39][40][41][42][43]. In a previous work [44], several turbulence models were compared, and it was concluded that the SST k-ω is the most appropriate for these simulations although the computational cost was higher than other turbulence models, such as the commonly employed k-ε.…”

Section: Numerical Modelmentioning

confidence: 99%

Analysis of a Nature-Inspired Shape for a Vertical Axis Wind Turbine

et al. 2022

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Wind energy is gaining special interest worldwide due to the necessity of reducing pollutant emissions and employ renewable resources. Traditionally, horizontal axis wind turbines have been employed but certain situations require vertical axis wind turbines. With a view to improve the efficiency of a vertical axis wind turbine Savonius type, the present work proposes a bioinspired design blade profile relying on the Fibonacci spiral. This shape is repeatedly presented in nature and thus it leads to a bio-inspired blade profile. A numerical model was carried out and it was found that the Fibonacci shape improves the performance of the original Savonius shape, based on semicircular blade profiles. Particularly, the Fibonacci blade profile increases around 14% the power in comparison with the Savonius blade profile. Besides this comparison between Savonius and Fibonacci, a research study was carried out to improve the efficiency of the Fibonacci turbine. To this end, the effect of several parameters was analyzed: number of blades, aspect ratio, overlap, separation gap, and twist angle. Improvements on the average power greater than 30% were obtained.

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Section: Numerical Modelmentioning

confidence: 99%

Analysis of a Nature-Inspired Shape for a Vertical Axis Wind Turbine

et al. 2022

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“…Results showed that at the same angle of attack, static torque is higher with the curtain compared to without any curtain. Compared to the conventional rotor, a 50% gain in power coefficient with the curtain was reported (Zhang et al, 2020). Rengma investigated the interaction of twin Savonius Hydrokinetic turbines experimentally.…”

Section: Introductionmentioning

confidence: 99%

Performance Improvement of Savonius Hydrokinetic Turbine Using Upstream Crescent Shape Deflector

Rehman,

Ali

2024

Preprint

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The use of non-renewable sources such as coal, natural gas, and oil for energy generation has caused negative environmental impacts. In response, clean and sustainable energy technologies are being rapidly developed. Hydrokinetic turbines, which can harness kinetic energy from free-flowing water in various settings, have been studied for their potential to generate electricity. The Savonius turbine is a hydrokinetic turbine that utilizes drag force and has a straightforward design that is independent of flow direction but suffers from lower efficiency. In this work, the effect of a crescent-shaped deflector installed upstream of the returning blade on the performance of the Savonius hydrokinetic turbine has been investigated. The numerical results indicate that the overall performance of the Savonius hydrokinetic turbine was positively influenced by the installation of the crescent deflector upstream, with a maximum power coefficient (Cp) of 0.264 achieved at a Tip Speed Ratio (TSR)=0.8. A further increase of up to 19.51% in the coefficient of power (Cp) of the Savonius hydrokinetic turbine was obtained at TSR=0.8 when a flat plate deflector upstream of the driving blade was installed along with a crescent-shaped deflector.

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“…The efficiency can also be increased through some paramete lated to the rotor design, such as the aspect ratio (AR), overlap (O), separation gap twist angle (TA), number of blades, etc. [17][18][19]. Figure 1a illustrates a 3D view of a S nius turbine.…”

Section: Introductionmentioning

confidence: 99%

Optimization of a Nature-Inspired Shape for a Vertical Axis Wind Turbine through a Numerical Model and an Artificial Neural Network

et al. 2022

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The present work proposes an artificial neural network (ANN) to analyze vertical axis wind turbines of the Savonius type. These turbines are appropriate for low wind velocities due to their low starting torque. Nevertheless, their efficiency is too low. In order to improve the efficiency, several modifications are analyzed. First of all, an innovative blade profile biologically inspired is proposed. After that, the influence of several parameters such as the aspect ratio, overlap, and twist angle was analyzed through a CFD (computational fluid dynamics) model. In order to characterize the most appropriate combination of aspect ratio, overlap, and twist angle, an artificial neural network is proposed. A data set containing 125 data points was obtained through CFD. This data set was used to develop the artificial neural network. Once established, the artificial neural network was employed to analyze 793,881 combinations of different aspect ratios, overlaps, and twist angles. It was found that the maximum power coefficient, 0.3263, corresponds to aspect ratio 7.5, overlap/chord length ratio 0.1125, and twist angle 112°. This corresponds to a 32.4% increment in comparison to the original case analyzed with aspect ratio 1, overlap 0, and twist angle 0.

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Improvement of Aerodynamic Performance of Savonius Wind Rotor Using Straight-Arc Curtain

Cited by 11 publications

References 20 publications

Analysis of a Nature-Inspired Shape for a Vertical Axis Wind Turbine

Analysis of a Nature-Inspired Shape for a Vertical Axis Wind Turbine

Performance Improvement of Savonius Hydrokinetic Turbine Using Upstream Crescent Shape Deflector

Optimization of a Nature-Inspired Shape for a Vertical Axis Wind Turbine through a Numerical Model and an Artificial Neural Network

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