This study investigates the impact of struts and a central tower on the aerodynamics and aeroacoustics of Darrieus Vertical Axis Wind Turbines (VAWTs) at chord-based Reynolds numbers of 8.12 × 104. A 2-bladed H-Darrieus VAWT is used, featuring a 1.5m diameter, a solidity of 0.1 and a blade cross-section of symmetrical NACA 0021. The turbine design is kept simple and straight-bladed which is essential for isolating and analyzing the specific effects of struts and a tower. The high-fidelity Lattice Boltzmann Method (LBM) in PowerFLOW 6-2020 and the mid-fidelity Lifting Line Free Vortex Wake (LLFVW) method in QBlade 2.0 are employed, with the mid-fidelity method providing a faster analytical tool for insights into the turbine performance. Firstly, both the LLFVW (mid-fidelity) and LBM (high-fidelity) methods effectively capture the general trends observed in VAWT power performance. However, the former predicts mean thrust values that are approximately 10% higher, and mean torque values that are approximately 19% higher, in comparison to the latter. Subsequently, the former predicts lower streamwise wake velocities relative to those predicted by the latter. These differences increase in configurations that include struts and a tower (to 30% - 31%). Secondly, the presence of struts and a tower leads to a reduction in both mean power (by 15% to 55%) and thrust (by 3% to 3.6%), with a further small decrease observed when doubling the tower diameter (power and thrust both by 0.5% to 3%). The struts predominantly affect the spanwise distribution of blade loading, while the tower impacts the azimuthal variation of blade loading. Additionally, the addition of struts and a tower reduces low-frequency noise (50-200 Hz) while increasing high-frequency noise (> 300 Hz). The observed decrease in mean blade loading results in reduced low-frequency noise, while the increase in high-frequency noise is ascribed to the increased intensity of BWI/BVI leading to higher unsteady loading fluctuations on blades.

On-shore horizontal-axis wind turbines (HAWTs) provide a cost-effective solution for low carbon electricity generation. However, public acceptance is still a problem. A possible alternative to a HAWT is a vertical-axis wind turbine (VAWT), which is more visually appealing and less noisy. Furthermore, the inherent omni-directionality of VAWTs makes them suitable for installation in urban environments where the turbulence levels are high, and the wind direction variations are significant. However, the variation with the azimuth angle of the blade-effective wind speed and the angle of attack makes VAWT performance difficult to predict. This study proposes a wind speed estimator for a VAWT to address this challenge and to exploit knowledge of the blade-effective wind speed for load reduction control strategies. An Unscented Kalman Filter is used to solve the blade-effective wind speed estimation problem and is applied to a realistic 1.5 m two-bladed H-Darrieus VAWT model, for which the aerodynamic characteristics are determined using an actuator cylinder model. The system performance is evaluated using different wind speed variation scenarios. Overall, good agreement between the reference and estimated blade-effective wind speed is found both in terms of trend and absolute values.

The combined wind speed estimator and tip speed ratio (WSE-TSR) tracking control scheme is widely used to regulate power production for large-scale modern wind turbines. Although very effective, such an advanced control scheme, based on the prior model information, is highly dependent on external measurements. For partial-load region control, the only external information involved is commonly the measured rotor or generator speed. Inaccuracy in such sole measurement results in an unintended turbine operation and might lead to sub-optimal power production and instability. This paper presents a fault-tolerant control (FTC) method, which aims to eliminate the sensor fault effects for modern wind turbine systems. To fulfil this goal, an iterative learning scheme is proposed to detect and estimate the multiplicative sensor fault, on which an adaptive FTC law is formulated such that the effects of the sensor fault are eliminated. Case studies show that the proposed iterative learning FTC method performs well in detecting, estimating, and accommodating the sensor fault under realistic turbulent wind conditions. The advanced wind turbine controller can maintain its control performance even under faulty conditions, preventing further damage to other turbine components and allowing for continuous power production.

In recent years, industrial controllers for modern wind turbines have been designed as a combined wind speed estimator and tip-speed ratio (WSE-TSR) tracking control scheme. In contrast to the conventional and widely used Kω 2 torque control strategy, the WSE-TSR scheme provides flexibility in terms of controller responsiveness and potentially improves power extraction performance. However, both control schemes heavily rely on prior information about the aerodynamic properties of the turbine rotor. Using a control-oriented linear analysis framework, this paper shows that the WSE-TSR scheme is inherently ill-conditioned. The ill-conditioning is defined as the inability of the scheme to uniquely determine the wind speed from the product with other model parameters in the power balance equation. Uncertainty of the power coefficient contribution in the latter mentioned product inevitably leads to a biased effective wind speed estimate. As a consequence, in the presence of uncertainty, the real-world wind turbine deviates from the intended optimal operating point, while the controller believes that the turbine operates at the desired set-point. Simulation results confirm that inaccurate model parameters lead to biased estimates of the actual turbine operating point, causing sub-optimal power extraction efficiency.

Vertical-axis wind turbines have the potential to be installed nearby urban areas, where noise regulations are a constraint. Accurate modelling of the far-field noise with low-order fidelity methods is essential to account for noise early in the design phase. The challenge for the vertical-axis wind turbine is the unsteady azimuthal variation of the flow over the blades, which makes the prediction of the far-field noise complex with low-fidelity methods. In this paper, the state-of-the-art of low-fidelity methods are assessed against scale-resolving high-fidelity numerical simulations of a realistic vertical-axis wind turbine carried out with the lattice-Boltzmann very large eddy simulations method. High-fidelity numerical data are validated against experimental aerodynamics data of the same vertical-axis wind turbine. The low-fidelity method is based on the actuator cylinder model coupled with semi-empirical models for airfoil-self noise and turbulence-interaction noise. Results show a good agreement between the high-fidelity simulations and the low-fidelity model at low frequencies (i.e. between 2 × 10 ¹ Hz and 1 × 10 ² Hz), where turbulence-interaction noise is the dominant noise source. At higher frequencies, the airfoil-self noise dominates and existing methods, based on steady airfoils, do not correctly predict noise. This paper shows that the presented low-fidelity model predicts the aerodynamics and the aeroacoustics of the turbine with an acceptable accuracy for a design stage. However, improvements are needed to better predict the far-field noise for blades in an unsteady field.

Simulation methods ensuring a level of fidelity higher than that of the ubiquitous Blade Element Momentum theory are increasingly applied to VAWTs, ranging from Lifting-Line methods, to Actuator Line or Computational Fluid Dynamics (CFD). The inherent complexity of these machines, characterised by a continuous variation of the angle of attack during the cycloidal motion of the airfoils and the onset of many related unsteady phenomena, makes nonetheless a correct estimation of the actual aerodynamics extremely difficult. In particular, a better understanding of the actual angle of attack during the motion of a VAWT is pivotal to select the correct airfoil and functioning design conditions. Moving from this background, a high-fidelity unsteady CFD model of a 2-blade H-Darrieus rotor was developed and validated against unique experimental data collected using Particle Image Velocimetry (PIV). In order to reconstruct the AoA variation during one rotor revolution, three different methods-detailed in the study-were then applied to the computed CFD flow fields. The resulting AoA trends were combined with available blade forces data to assess the corresponding lift and drag coefficients over one rotor revolution and correlate them with the most evident flow macro-structures and with the onset of dynamic stall.

The paper presents an experimental study of applying variable loads on a vertical-axis wind turbine (VAWT). The experiment is conducted in an open-jet wind tunnel on a two-bladed Darrieus VAWT equipped with active individual blade pitch control. Variable loads are achieved by dynamically changing the pitch angle of the individual blades and by keeping the wind speed of the tunnel constant. The blade loads are measured using strain gages and the flow velocity is measured upwind and downwind of the rotor using a hotwire. Dynamic inflow phenomena are clearly visible both in the turbine loads and in the velocity field. A time delay based upon the flow convection in the wake is identified. It results that the induction of the turbine can be controlled by changing the pitch of the blades. The experimental database allows to validate a new dynamic inflow model for VAWT and will be made publicly available for research purposes.