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Investigation of flow augmentation and dynamic stall control devices as performance enhancement of lift-driven vertical axis wind turbine using high-fidelity CFD methods

Syawitri, Taurista Perdana

Investigation of flow augmentation and dynamic stall control devices as performance enhancement of lift-driven vertical axis wind turbine using high-fidelity CFD methods Thumbnail


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Abstract

This study performs an investigation of dynamic stall control (Gurney flap (GF)) and flow augmentation (straight upstream deflector (SUD)) devices to improve the performance of lift-driven Vertical Axis Wind Turbine (VAWT) as they can improve the power generation of VAWT at all ranges of tip speed ratios (TSRs). High-fidelity Computational Fluid Dynamics (CFD) method is applied to evaluate the performance and geometry optimisation. The accuracy and computational cost of Unsteady Reynolds Averaged Navier-Stokes (URANS) and hybrid RANS-Large Eddy Simulation (LES) turbulence models to predict the overall aerodynamic performance and flow-field characteristics of VAWT at all ranges of TSRs are compared, to identify the most suitable turbulence model with a reasonable computational cost for VAWT simulation at all ranges of TSRs.

Instead of using a single parameter variation optimisation at a time, multiple parameters geometry optimisation of GF using the Taguchi method is performed to understand the correlation between evaluated geometry parameters and optimal performance. Moreover, this study evaluates geometry optimisation of GF and SUD in VAWT configuration (i.e. considering rotating effect and blade-to-blade interaction) at all ranges of TSRs, rather than evaluate a single stationary aerofoil at a single range of TSRs. Evaluation of combining GF and SUD is also delivered in this study. Additionally, 3D modifications of the Gurney flap to reduce the drag generation of VAWT with GF are evaluated to further improve the performance of VAWT.

The results show that URANS turbulence models are sufficient to predict the overall performance of lift-driven VAWT and a single range of TSRs evaluation. However, Hybrid RANS-LES turbulence models are necessary to investigate the aerodynamics and flow-field characteristics of lift-driven VAWT and all ranges of TSRs evaluation. Moreover, the GF and SUD indeed can increase the performance of lift-driven VAWT at all ranges of TSRs (up to 233.19% and 139.11% at low ranges of TSRs). As the range of TSRs increases, both GF and SUD experience a decrement in the rate of Cp-ave improvement. Nevertheless, GF and SUD have different methods to improve the power generation of VAWT at each range of TSRs. Hence, it is proven that design optimisation and flow analysis of GF and SUD need to be performed at each range of TSRs. In addition, by adding the effect of rotating flow and blade-to-blade interaction, the optimum geometry design of GF is changed compared to the optimum design of a single stationary aerofoil. It confirms that design optimisation needs to be performed in a VAWT configuration (i.e., including rotating effect and blade-to-blade interaction). Noting that, combining optimised GF and optimised SUD does not increase the power generation improvement of lift-driven VAWT further. Additionally, introducing 3D modifications of GF (i.e. slits or holes) in the blades of VAWT with GF further improves the power coefficient of lift-driven VAWT. The existence of slits and holes can improve the power coefficient of VAWT with GF by 6.5%, and 0.28%, respectively.

Citation

Syawitri, T. P. Investigation of flow augmentation and dynamic stall control devices as performance enhancement of lift-driven vertical axis wind turbine using high-fidelity CFD methods. (Thesis). University of the West of England. Retrieved from https://uwe-repository.worktribe.com/output/8045334

Thesis Type Thesis
Deposit Date Nov 3, 2021
Publicly Available Date Mar 28, 2024
Keywords Vertical Axis Wind Turbine; Computational Fluid Dynamics; Dynamic Stall Control; Flow Augmentation Device
Public URL https://uwe-repository.worktribe.com/output/8045334
Award Date Aug 10, 2022

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