Airfoil optimisation for vertical‐axis wind turbines with variable pitch

Tavernier, Delphine De; Ferreira, Carlos; Bussel, Gerard van

doi:10.1002/we.2306

Cited by 30 publications

(19 citation statements)

References 26 publications

(48 reference statements)

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“…This leads to many inherent difficulties for designers to properly characterize the loads and performance of these turbines, especially in understanding fatigue. Research interest has increased in the areas of active variable pitch for improving power extraction, controlling the direction of the wake, or minimizing loads 1–5 . In order to provide better insight into these phenomena, a set of flow field measurements through Particle Image Velocimetry were conducted using the PitchVAWT active pitch turbine at Delft University of Technology.…”

Section: Introductionmentioning

confidence: 99%

Estimation of blade loads for a variable pitch vertical axis wind turbine from particle image velocimetry

LeBlanc

Ferreira

2021

Wind Energy

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This paper presents the flow fields and aerodynamic loading of a two bladed H‐type vertical axis wind turbine with active variable pitch for load and circulation control. Particle Image Velocimetry is used to capture flow fields at six azimuthal positions of the blades during operation, three upwind and three downwind. Flow phenomena such as dynamic stall and tower shadow are captured in the flow fields. The phase‐averaged velocity fields and their time and spatial derivatives are used to calculate the normal and tangential loading at each position for each pitching configuration using the Noca formulation of the flux equations. The results show the effect of load shifting from the upwind to downwind region of the actuator using pitch and the effects of dynamic stall on the blades. The results also provide an unique database for model validation.

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Section: Introductionmentioning

confidence: 99%

Estimation of blade loads for a variable pitch vertical axis wind turbine from particle image velocimetry

LeBlanc

Ferreira

2021

Wind Energy

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“…For certain cases, when the improved self-starting design was implemented, a decline in the overall energy generation was observed. A genetic algorithm is used in [23] to optimize the airfoil shape considering a balance between the aerodynamic and structural performance of airfoils.…”

Section: Performance Of Lift-driven Vawtsmentioning

confidence: 99%

Improvement of Self-Starting Capabilities of Vertical Axis Wind Turbines with New Design of Turbine Blades

2021

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A lift-driven vertical axis wind turbine (VAWT) generates peak power when it is rotating at high tip-speed ratios (TSR), at which time the blades encounter angles of attack (AOA) over a small range from zero to 30 degrees. However, its ability to self-start is dependent upon its performance at low TSRs, at which time the blades encounter a range of AOAs from zero to 180 degrees. A novel vented aerofoil is presented with the intention of improving the performance of a lift-driven VAWT at low TSRs without hampering the performance of the wind turbine at high TSRs. Computational fluid dynamics (CFD) simulation is used to predict the aerodynamic characteristics of a new vented aerofoil based on the well documented NACA0012 profile. Simulations are performed using the SST turbulence model. The results obtained show a reduction in the coefficient of tangential force (the force that generates torque on the wind turbine) at low AOAs (less than 90 degrees) of no more than 30%, while at high AOAs (more than 90 degrees) an improvement in the tangential force of over 100% is observed. Using a simple momentum based performance prediction model, these results suggest that this would lead to an increase in torque generation by a theoretical three-bladed VAWT of up to 20% at low TSRs and a minor reduction in coefficient of performance of up to 9% at TSR of 2 and closer to 1% at higher TSRs.

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“…Alizadeh et al 10 use a simple barrier to deviate the flow. For the lift-type VAWTs, adjustments range from the modifications to the wind turbine structure itself in the form of blade adaptation as demonstrated by Preen and Bull 11 and Baghdadi et al 8 who investigated supershapes and morphing blades, respectively, or airfoil optimization as considered by De Tavernier et al 12 to the addition of the separate elements that would serve as flow directors or concentrators and whose main role is to locally amplify flow velocity. For example, De Santoli et al 13 investigated a VAWT with a surrounding linear, convergent duct.…”

Section: Introductionmentioning

confidence: 99%

Design of optimal flow concentrator for vertical-axis wind turbines using computational fluid dynamics, artificial neural networks and genetic algorithm

Svorcan

Peković

Simonović

et al. 2021

Advances in Mechanical Engineering

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Wind energy extraction is one of the fastest developing engineering branches today. Number of installed wind turbines is constantly increasing. Appropriate solutions for urban environments are quiet, structurally simple and affordable small-scale vertical-axis wind turbines (VAWTs). Due to small efficiency, particularly in low and variable winds, main topic here is development of optimal flow concentrator that locally augments wind velocity, facilitates turbine start and increases generated power. Conceptual design was performed by combining finite volume method and artificial intelligence (AI). Smaller set of computational results (velocity profiles induced by existence of different concentrators in flow field) was used for creation, training and validation of several artificial neural networks. Multi-objective optimization of concentrator geometric parameters was realized through coupling of generated neural networks with genetic algorithm. Final solution from the acquired Pareto set is studied in more detail. Resulting computed velocity field is illustrated. Aerodynamic performances of small-scale VAWT with and without optimal flow concentrator are estimated and compared. The performed research demonstrates that, with use of flow concentrator, average increase in wind speed of 20%–25% can be expected. It also proves that contemporary AI techniques can significantly facilitate and accelerate design processes in the field of wind engineering.

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Airfoil optimisation for vertical‐axis wind turbines with variable pitch

Cited by 30 publications

References 26 publications

Estimation of blade loads for a variable pitch vertical axis wind turbine from particle image velocimetry

Estimation of blade loads for a variable pitch vertical axis wind turbine from particle image velocimetry

Improvement of Self-Starting Capabilities of Vertical Axis Wind Turbines with New Design of Turbine Blades

Design of optimal flow concentrator for vertical-axis wind turbines using computational fluid dynamics, artificial neural networks and genetic algorithm

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