Simulation of performance enhancement of horizontal axis wind turbine with guide ring

Abstract

This study investigates the aerodynamic performance of a horizontal axis wind turbine (HAWT) rotor retrofitted with guide rings, specifically focusing on the optimal guide ring diameter for maximizing efficiency. Computational fluid dynamic simulations were conducted on an ideal, 280 mm diameter rotor fitted with guide rings ranging from 15% to 20% of the rotor diameter with 1% incremental placement, and on the same rotor without a guide ring. The simulations revealed that the rotor with an 18% guide ring provided the best performance, achieving a peak power increase of 2.3% at 180 r/min and maintaining superior performance across a range of rotational speeds. Flow visualization indicated that the guide ring improved axial velocity retention, reduced both axial and tangential induction factors, and enhanced the aerodynamic efficiency of the rotor, particularly at higher (beyond design) rotation speeds. This research provides data and flow visualization that quantifies and provides insight into the potential for enhancing energy extraction from the wind through inclusion of a guide ring in HAWT rotor design—either during manufacture or as a retrofit.

Keywords

operational range power increase post installation retrofit aerodynamic improvement

Introduction

A recent experimental study (Barnard et al., 2024) revealed that instead of having a shroud surrounding the rotor diameter, a smaller, concentric guide ring can be placed near the hub region in front of the plane of rotation to improve rotor operational and peak performance. The results suggest that a guide ring helps to prevent transition to the turbulent wake state at higher axial induction. The experimental study used a 280 mm diameter rotor designed using the blade element momentum method and retrofitted with various guide ring sizes near the hub region, ranging from 15% to 20% of the rotor diameter (with size increments of 1%). The study utilized a vertical-travel test apparatus and water as the working fluid.

No other published work has described the impact of a guide ring on rotor performance; however, many studies have investigated shrouded rotors which similarly attempt to control flow through the rotor. Katooli and Noorollahi (2022) found, in their critical review, that the research in shrouded rotors focused on minimizing diffuser size, and maximizing power extraction and economy. The review also found that shrouding a rotor increased power by a factor ranging from 2 to 5. Ohya and Karasudani (2010) found that improvements of between 2 and 5 times were achieved by including a broad-ring brim at the exit periphery of the diffuser shroud. Matsushima et al., (2006) found that the wind velocity increased by a factor of 1.7 with a frustum-shaped diffuser, increasing the power output by a factor of 2.4.

Fletcher et al., (2007) showed that a rotor fitted with a contractor-diffuser increased the airflow through the rotor resulting in a 43% increase in power output. Aranake et al. (2015), using a Reynolds-Averaged-Naviere-Stokes (RANS)-based CFD simulation, found that a fully shrouded rotor could extract power up to 90% beyond the Betz limit.

The experimental study by Barnard et al. (2024) was limited to power output comparison only. This article presents the computational fluid dynamic (CFD) simulation results of the same rotor and guide rings used in the experimental study. The CFD simulation made it possible to extract data such as axial, tangential, and radial velocities and allowed visualization of the impact of the guide ring on flow through the rotor. In this article, the rotor without any guide ring is referred to as the “standard” rotor. Figure 1 shows the difference between a diffuser/shrouded rotor and rotor fitted with a guide ring.

Figure 1.

Diffuser/shrouded rotor (left) (Halo Energy kicks off production of shrouded micro wind turbines, n.d) and guide ring rotor (right).

Rotor aerodynamics

The key aerodynamic variables in this study, the axial and tangential induction factors, measure how effectively the rotor extracts energy from the wind.

Axial induction factor a, defined by (1) and Figure 2, represents the fractional decrease in axial wind velocity from the free stream velocity V₁ to the velocity at the rotor plane V_t.

a = \frac{V_{1} - V_{t}}{V_{1}}

(1)

Figure 2.

Velocity that define axial induction (Manwell et al., 2010).

The wake generated by the rotor rotates in the opposite direction to the rotor, as a reaction to the torque delivered to the rotor blades. Figure 3 shows an air streamline upstream and downstream of the rotor.

Figure 3.

Stream tube with rotating wake (Manwell et al., 2010).

For the purpose of defining the tangential induction factor a′, it is assumed that the rotational speed of the wake is significantly lower than the rotational speed of the rotor itself. By considering a thin control volume of thickness dr within the stream tube, a rotor angular velocity Ω and the wake angular velocity ω, tangential induction is defined as (Manwell et al., 2010)

a^{'} = ω / 2 Ω

(2)

Implications of induction factor values

The axial induction factor is an essential measure of rotor performance, providing insights into the operating state of the rotor. Control of the stream tube to preserve the windmill state and prevent transition into the turbulent state (and higher axial induction), is beneficial for rotor performance, and is the basis for employing a shroud or guide ring. Figure 4 shows the relationship between the thrust coefficient and the axial induction factor for rotors functioning in the turbulent wake state. Depending on the level of interaction between the rotor blades and the wake, a rotor can operate in one of four distinct states:

• Propeller State—where the rotor acts as a propeller, actively driving the air.

• Windmill State—where the rotor extracts energy from the wind, typical of wind turbines.

• Turbulent Wake State—characterized by increased turbulence and complex interactions between the blades and the wake.

• Vortex Ring State—where the rotor experiences a combination of downward and recirculating airflow, leading to inefficient operation.

Figure 4.

Turbine rotor states for various axial induction (adapted from Rajan and Ponta, 2019).

The turbine rotor working states correspond to specific values of the axial induction factor α. Among these, the windmill and turbulent wake states are the most relevant for wind turbines. (Dong et al., 2022).

The rotation of the wake reduces the rotor capacity to extract kinetic energy from the wind. Consequently, a lower tangential induction factor indicates better energy extraction than a higher value.

Figure 5 shows the axial and tangential induction factors along the blade of an ideal rotor design at a non-dimensional blade radius r/R. In the hub region, the tangential induction factor is elevated, indicating reduced kinetic energy extraction by the blade. In contrast, as one moves toward the tip of the blade, a lower tangential induction factor indicates more effective kinetic energy extraction by applying (2). The increase in the axial induction factor toward the tip of the blade suggests that there is less axial velocity available for the blade airfoil to operate efficiently when applying (1).

Figure 5.

Axial and tangential induction factor across a rotor blade (Manwell et al., 2010).

Rotor performance: Peak power versus range of operation

A typical power curve for a HAWT rotor exhibits a parabolic shape, when power or power coefficient (C_p) is plotted against either rotor rotation speed or tip speed ratio (λ). Figure 6 shows the power curves for two different HAWT rotor designs. The selection of the rotor design is influenced by the wind speed characteristics – particularly the stability of the optimal wind speed.

Figure 6.

Typical power coefficient versus tip speed ratio (λ) curves (adapted from Hansen, 2008).

Design 1 achieves high peak performance but experiences rapid increases and decreases in power output before and after this peak. In contrast, Design 2 has a lower peak performance but maintains a higher level of efficiency across a broader range of tip speed ratios. In conditions with very stable wind speeds, Design 1 would be preferred, to maximize performance. Conversely, in unstable wind speed conditions, where off-design rotation speed is expected, Design 2, with lower sensitivity to rotation speed, is more advantageous (Hansen, 2008).

Design methodology

To investigate the impact of integrating a stationary guide ring upstream of the rotor plane, an “ideal” 280 mm diameter rotor was designed using blade element momentum theory, as outlined by Fawkes (2023), utilizing the SG6043 airfoil along the entire blade length. The design utilized the Maalawi chord (El-okda, 2015), Schmitz relative wind angle, Prandtl tip loss factor and Buhl axial induction correction, as presented by Manwell et al. (2010). The label “ideal” indicates that the full blade was designed with these theoretical tools and there was no adjustment for manufacturing practicalities. The 280 mm diameter rotor and guide rings used in this study were the same design used in the physical test study by Barnard et al. (2024). A NACA0025 symmetrical airfoil profile with a camber/chord line parallel to the rotor axial direction was used for the concentric guide rings.

To determine the most effective guide ring diameter for performance enhancement, guide rings were successively installed at diameters, ranging from 15% to 20% of the rotor diameter in 1% increments. By analyzing the performance of each guide ring placement through CFD simulations, this investigation aimed to quantify and find explanation for the aerodynamic benefits and operational efficiency improvements associated with different guide ring sizes. Figure 7 shows the rotor fitted with a 15% guide ring.

Figure 7.

Rotor with 15% guide vane.

Parameters of both the rotor and guide rings, including dimensions and aerodynamic characteristics, are presented in Table 1.

Table 1.

Rotor and guide ring parameters.

Description	Value
Rotor diameter (m)	0.28
Design tip speed ratio	4.8
Number of blades	3
Hub ratio	0.5
Hub diameter (m)	0.0140
15% guide ring diameter (m)	0.0420
16% guide ring diameter (m)	0.0448
17% guide ring diameter (m)	0.0476
18% guide ring diameter (m)	0.0504
19% guide ring diameter (m)	0.0532
20% guide ring diameter (m)	0.0560

The overall methodology comprised the following steps:

• Design of guide rings, using SolidWorks, to integrate seamlessly with the existing rotor design.

• CFD simulation of the rotor without guide ring, to establish a baseline for comparison with retrofitted rotors.

• CFD simulations of all retrofitted rotors.

• Analysis and comparison of results obtained from CFD simulation.

The simulation approach in this study was comparative, focusing on the performance differences between the rotor with and without guide rings. Simulations were conducted using water as the fluid medium to match the experimental study previously done on the same rotors. Simulation allowed for evaluation of the aerodynamic effects of the guide rings by analyzing the changes in rotor performance metrics, such as power output, efficiency, induction factors, and flow visualization. The simulation aimed to isolate the impact of the guide ring configurations on the rotor performance, and provide insight into design modifications that could enhance energy conversion efficiency in practical applications.

Simulation methodology

ANSYS Fluent 2022 version R2 was used for CFD simulation on a Lenovo system (11th generation i5 core processor, 2.4 GHz, 8 GB RAM), and a solid-state drive. A total of seven virtual models were developed for this study, which included a standard rotor and six additional rotors, each fitted with varying sizes of guide rings, as detailed in Table 1.

Each model was set up using a consistent process to ensure that all solver settings and input parameters remained identical across the simulations. This methodology was designed to produce comparable results, allowing for direct analysis of performance changes resulting from different guide ring sizes.

Solid models created in SolidWorks were imported into ANSYS, where all parts and domains were named using the built-in DesignModeler platform. The virtual models were structured to include a fluid domain surrounding a rotating domain that contained the rotor, as detailed in Figure 8.

Figure 8.

Virtual CFD model with rotor within rotating domain enclosed by fluid domain.

The virtual models were then imported into ANSYS Fluent for the meshing process with rotational periodicity. The watertight geometry workflow in Fluent meshing incorporated the following key settings as shown in Table 2.

Table 2.

Watertight geometry workflow key settings.

Surface mesh
Min. size	0.0016 (m)
Max. size	0.0425 (m)
Growth rate	1.2
Curvature normal angle	18
Cells per gap	1
Scope proximity to	Edges
Rotational periodic boundaries	Periodicity angle 120°
Boundary layer settings
Offset method type	Aspect ratio
Number of layers	4
First aspect ratio	5
Growth rate	1.2
Volume settings
Fill with	Poly-hexcore
Buffer layers	1
Peel layer	1

Solver model setup for simulation

ANSYS Fluent utilizes the Reynolds-averaged Navier-Stokes (RANS) equations to solve fluid flow problems, incorporating transport equations to implement the selected turbulent viscosity model. The k-ω turbulence model is well-regarded for its ability to manage flows with low Reynolds numbers, as it effectively resolves the viscous sublayer. This model employs turbulent kinetic energy and a specific turbulent dissipation rate, making it particularly suitable for near-wall treatments. In contrast, the k-ε turbulence model relies on empirical damping functions within the viscous sublayer, which can result in decreased accuracy in boundary layer flows with pressure gradients (SimScale, no date). For this study, the two-equation Generalized k-ω (GEKO) turbulence model was chosen.

Default coefficient settings of the k-ω GEKO model were employed in this research, to provide a balanced approach to turbulence modeling—aiming to accurately capture the flow dynamics around the rotors without introducing bias that could lead to over-predictions of lift or other aerodynamic characteristics at varying angles of attack.

Identical solver settings were applied to simulate all models. A steady-state, pressure-based, multiple reference frame solver was utilized along with the GEKO turbulence model, with water as the working fluid. The fluid inlet velocity was set at 0.5 m/s, while the rotational speed of the rotating domain containing each rotor varied between 140 r/min and 220 r/min to determine an approximate performance peak using initial mesh settings. Power output of the rotors was calculated using

(P = T \cdot ω)

(3)

where T represented the torque acting on the blades and hub, and ω was the rotation speed of the rotating domain during solution iterations. The solution method employed a coupled pressure and velocity approach using a pseudo-transient solver that allowed for user-specified time steps. Convergence criteria were considered met when the scaled residuals for the x, y, and z fluid velocities reached approximately 10⁻⁶, and continuity also approached 10⁻⁶, ensuring that the output torque converged to an oscillating fifth significant digit.

After establishing the approximate performance peak, a mesh independence study was conducted, which involved refining the mesh settings around the identified peak rotational speed for each virtual rotor model to enhance the accuracy and reliability of the results.

Mesh independence study

The aim of the mesh independence study was to ensure that the solution was not overly dependent on the mesh size and that the near-wall boundary layer was accurately captured. The torque generated on the blade and hub was used as the key indicator for assessing mesh independence.

Figure 9 shows a sample of the results of the mesh independence study conducted on the standard rotor, showing how torque varied as the cell count of the virtual model increased with decreased mesh size. Table 3 displays the sample of percentage change in torque as the mesh refinement progressed for the standard rotor and confirmed the accuracy and reliability of the simulation results across different rotor configurations.

Figure 9.

Mesh independence study of standard rotor with torque versus cell count.

Table 3.

Percentage change of torque for mesh independence study of standard rotor.

Standard rotor at 180 r/min
Cell count	Torque hub (N.m)	Torque blade (N.m)	Torque total (N.m)	Percentage change in torque
106,858	4.40E-06	0.028340	0.028344	-
254,994	4.40E-06	0.028014	0.028018	−1.15%
403,129	4.40E-06	0.028370	0.028374	1.27%
1,062,314	4.30E-06	0.028650	0.028654	0.99%
1,419,545	4.47E-06	0.028744	0.028748	0.33%

The final mesh settings used for all rotor types are shown in Table 4.

Table 4.

Final mesh settings.

Surface mesh
Min. size	0.00001 (m)
Max. size	0.000256 (m)
Growth rate	1.2
Curvature normal angle	18
Cells per gap	1
Scope proximity to	Edges
Rotational periodic boundaries	Periodicity angle 120°
Boundary layer settings
Offset method type	Aspect ratio
Number of layers	4
First aspect ratio	10
Growth rate	1.2
Volume settings
Fill with	Poly-hexcore
Buffer layers	1
Peel layer	1

Boundary layer

To accurately capture the boundary layer dynamics in the simulations of the 280 mm diameter rotor models, additional inflation mesh layers were integrated into the virtual models. To accurately capture the boundary layer with the k-ω GEKO turbulence model, the boundary layer at the mid-span of the blade was analyzed at y⁺ = 1 and y⁺ = 5. This analysis guided the determination of the necessary inflation layers for the mesh. The y⁺ value is a dimensionless parameter that represents the distance from the first grid cell to the surface wall and is expressed as:

Y^{+} = \frac{U_{t} y}{μ}

(4)

In this context, U_t is the friction velocity, y denotes the height of the first cell, and μ is the kinematic viscosity. The boundary layer at the mid-span of the blade was assessed for y⁺ = 1 and y⁺ = 5, ensuring that the height of the first inflation layer, y, fell within this range to adequately capture the viscous laminar region during the simulation process. The final inflation layer settings for all virtual models included an aspect ratio of 10 and a growth rate of 1.2, with a total of four inflation layers situated between the model walls and the volume mesh. Figure 10 shows the placement of the inflation layers in relation to the y⁺ = 1 and y⁺ = 5 boundaries for the 280 mm diameter rotors.

Figure 10.

Rotor boundary layer and sub-layers with mesh height calculation.

Figure 11 shows a sample of the final mesh with a cross section of the full domain, cross section of rotating domain and close-up section of the guide ring.

Figure 11.

Sample of final mesh of model.

Results

Power output for each rotor type was calculated across a range of rotational speeds. Power was derived from the product of the rotor net torque output and rotation speed in radians per second. Each data point in the results corresponds to an individual simulation conducted for a specific combination of rotor type and rotational speed.

CFD flow visualization was performed, using ANSYS CFD post-processing, to determine the axial, radial, and tangential velocity at a plane located 2 mm upstream of the rotor plane. Velocity visualizations were created for the standard rotor and the best-performing rotor with guide ring at peak performance (at a rotational speed of 180 r/min).

Additionally, ANSYS CFD post-processing was utilized to determine the axial and tangential induction factors along the blade in annular segments. A total of 15 segments were analyzed from the blade root to the tip. The axial induction factor was obtained at a plane 2 mm in front of the rotor plane, while the tangential induction factor was calculated from a plane 20 mm behind the rotor plane, within the near wake of the rotor. Axial and tangential induction factors were assessed at the peak rotation speed of 180 r/min.

Figure 12 shows the power curves for all rotors at a free stream velocity of 0.5 m/s. The power curves indicate that the rotor with an 18% guide ring achieved the best performance, reaching the highest peak power while also maintaining superior performance across nearly the entire range of rotation speeds.

Figure 12.

Simulation results for all rotors at 0.5 m/s free stream velocity.

Figure 13 shows the comparison between the standard rotor and the retrofitted rotor with an 18% guide ring. It is evident that the standard rotor only outperforms the retrofitted rotor at very low rotation speeds. The peak performance improvement for the 18% guide ring rotor is 2.3% at 180 r/min, with notable enhancements in operational performance starting from 145 r/min. The 18% guide ring rotor is less sensitive to change in rational speed. As an indicator, at off-design rotational speeds of 160 r/min and 200 r/min, the 18% ring vane rotor surpassed the performance of the standard rotor by 4.04% and 3.2%, respectively. Physical testing using the exact same rotor configurations by Barnard et al. (2024) also showed a small peak performance increase and broadening of the operational range and provides validation of the simulation results.

Figure 13.

Simulation results for standard and 18% ring vane rotors at 0.5 m/s free stream velocity.

Flow visualization

Figures 14 –17 present a comparison of flow contours for axial, tangential, and radial velocities, at a plane located 2 mm in front of the rotor (and immediately downstream of the stationary guide ring) during peak performance, for both the standard rotor and the rotor retrofitted with an 18% guide ring.

Figure 14.

Axial velocity contours on standard rotor (left) and 18% ring vane rotor (right) at 180 r/min for 0.5 m/s free stream velocity.

Figure 15.

Close-up view of axial velocity contours on standard rotor (left) and 18% ring vane rotor (right) at 180 r/min for 0.5 m/s free stream velocity.

Figure 16.

Close-up view of tangential velocity contours on standard rotor (left) and 18% ring vane rotor (right) at 180 r/min for 0.5 m/s free stream velocity.

Figure 17.

Close-up view of radial velocity contours on standard rotor (left) and 18% ring vane rotor (right) at 180 r/min for 0.5 m/s free stream velocity.

The full axial velocity range contours depicted in Figure 14, show very little difference between the standard rotor and the 18% guide ring rotor, along the blade beyond the guide ring region. This indicates that the flow dynamics in this part of the blade remain relatively unaltered, despite the enhancements introduced by the guide rings.

A closer examination in Figure 15 of the hub region of the rotor reveals how the presence of the guide ring influences the flow dynamics near the rotor hub. The contours show higher axial velocity within the guide ring area and at the nearby blade leading edge (shown as a darker blue zone). Higher axial velocity implies a reduced axial induction factor which is discussed later in this section. The circles in Figures 15 –17 indicate areas with higher velocities.

Close-up views of the tangential and radial velocity contours for both the standard rotor and the rotor retrofitted with an 18% guide ring are presented in Figures 16 and 17, respectively. Figure 16 shows a significant increase of tangential velocity within the guide ring region. In Figure 16 (right), the guide ring strut can be seen extending vertically from the hub surface to the guide ring. Natural pre-whirl creates high pressure and low pressure on the left and right sides, respectively, of the strut. Flow off the back of the strut follows the pressure gradient and produces the higher tangential velocities seen to the right of the strut. As with the increased axial velocities in Figure 15, the increased tangential velocities in Figure 16 also indicate an intensification of flow within the guide ring.

Figure 17 shows how radial flow is drawn into the low-pressure zone to the right of the strut and is contained by the guide ring. The halo of higher radial velocity along the guide ring indicates a pressure difference across the outer and inner surfaces of the guide ring as it contains the flow in a more axial direction.

The close-up views in Figures 15 –17 suggest that the guide ring partially prevented the conversion of initial axial velocity into radial and tangential components. This allowed for greater retention of axial velocity for interaction with the rotor blades.

The axial and tangential induction factors along the blade are illustrated in Figures 18 and 19 for both the standard rotor and the rotor retrofitted with an 18% guide ring. The axial induction factor was calculated at a plane 2 mm in front of the rotor plane. Notably, the axial induction factor for the 18% guide ring rotor was consistently lower than that of the standard rotor along the blade. A lower axial induction factor suggests that more axial velocity is available to the blades.

Figure 18.

Axial induction factor for standard and 18% ring vane rotor at 180 r/min for 0.5 m/s free stream velocity.

Figure 19.

Tangential induction factor for standard and 18% ring vane rotor at 180 r/min for 0.5 m/s free stream velocity.

A distinct spike in the axial induction factor was observed, starting at the inner radius of the guide ring and peaking at the guide ring outer radius. This spike is attributed to the wake generated by the guide ring shown in the cross-sectional contour view in Figure 20, indicating suboptimal aerodynamic performance for that section of the blade. The reduced axial induction factor within and outside the guide ring, extending up to the mid-span of the blade, again suggests that the guide ring effectively enhanced the aerodynamics in those regions by reducing the conversion of axial velocity into tangential and radial components.

Figure 20.

Cross-sectional axial velocity contour view for standard and 18% guide ring rotor.

Tangential induction across the rotor is shown in Figure 20. The rotor retrofitted with the 18% guide ring exhibited a lower tangential induction factor from the blade root to the outer radius of the guide ring. The tangential induction factor was calculated at a plane 20 mm behind the rotor plane, within the near wake region. The reduced tangential induction in the area of the guide ring indicates that the rotor rotational speed increased relative to the near wake rotation speed. This lower tangential induction could be attributed to a reduction in natural pre-whirl due to the flow straightening effect of the guide ring struts and the greater retention of axial velocity within the guide ring area. There was no significant change in tangential induction further along the blade. This indicates that the effects of the guide ring on tangential induction were localized primarily to the region that it occupied.

Conclusion

The CFD simulations, of both the rotor without a guide ring, and the rotor fitted with guide rings provided the means to identify the optimal guide ring diameter that maximized performance at both peak and off-peak rotor rotation speeds. The results indicate that placing the guide ring at 18% of the rotor diameter yields the most significant performance improvements and this result is in agreement with previously reported experimental results. The peak performance improvement for the 18% guide ring rotor was 2.3% at 180 r/min, with notable enhancements in off-peak performance starting from 145 r/min. At off-peak rotation speeds of 160 r/min and 200 r/min, the 18% guide ring rotor surpassed the standard rotor performance by 4.04% and 3.2%, respectively. Validation of simulation results was achieved by physical testing of the exact same rotor configurations as presented by Barnard et al. (2024).

CFD flow visualization provided evidence that axial velocity within and outside of the guide ring was increased and that the guide ring partially prevented the conversion of initial axial velocity into radial and tangential components, allowing greater retention of axial velocity for interaction with the rotor blades. The interaction of the flow with the guide ring strut was significant, possibly having beneficial effect (reduction of natural pre-whirl) and detrimental effect (flow disturbance ahead of the blades).

The axial induction factor was lowered over most of the blade length, by the presence of the guide ring, while only the blade area immediately downstream of the guide ring experienced an axial induction “spike.” The control of axial flow by the guide ring and the prevention of the turbulent wake state is expected to be the reason for the improved power delivery of the rotor with guide ring at high (off-design) rotation speeds. At lower off-design rotation speeds, the concentration of axial flow closer to the hub is also expected to be the main reason for the improved power delivery.

The simulations benefited from precise control over input variables. All parameters could be easily defined and consistently applied across all simulations. Additionally, the absence of rotor flex during these simulations allowed for a clearer assessment of aerodynamic performance, eliminating potential variations that could arise from physical testing.

Recommendations

To enhance the guide ring performance, the following strategies could be considered:

• Modification of the guide ring profile and/or angle of attack could improve airflow and reduce losses.

• Adjustment of the airfoil profile and/or angle of attack of the supporting struts could reduce flow separation and drag.

• Exploration of self-adjusting and/or free-rotating guide rings could offer adaptive aerodynamic benefits, aligning optimally with changing flow conditions.

Footnotes

Acknowledgments

The authors acknowledge the support of the Cape Peninsula University of Technology.

Author contributions

Daniel Barnard: Conceptualization, investigation, methodology, data curation, visualization, and writing—original draft. Howard Fawkes: Writing—review and editing, supervision, and validation.

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

ORCID iDs

Daniel R Barnard

Howard T Fawkes

Data Availability Statement

Research data is available on request from the authors and via

Appendix

References

Aranake

Lakshminarayan

Duraisamy

(2015) Computational analysis of shrouded wind turbine configurations using a 3-dimensional RANS solver. Renewable Energy 75: 818–832. DOI: 10.1016/J.RENENE.2014.10.049.

Barnard

Fawkes

Oliver

(2024) Performance enhancement of horizontal axis wind turbine with guide ring. Wind Engineering 49(3): 781. DOI: 10.1177/0309524X241292362.

Dong

Viré

(2022) Analysis the vortex ring state and propeller state of floating offshore wind turbines and verification of their prediction criteria by comparing with a CFD model. Renewable Energy 184: 15–25. DOI: 10.1016/j.renene.2021.11.053.

El-okda

(2015) “Design methods of horizontal axis wind turbine rotor blades”,. Int. J. Industrial Electronics and Drive 2(3): 144. doi: 10.1504/IJIED.2015.072789., In this issue.

Fawkes

(2023) Hub ratio of horizontal axis wind turbine rotors for optimal performance. Cape Peninsula. Bellville: University of Technology. Available at: https://etd.cput.ac.za/handle/20.500.11838/3960.

Fletcher

Wang

Bai

, et al. (2007) Wind Tunnel Tests on a Wind Turbine with Contractor and Diffuser Arrangement. ieeexplore.ieee.org. J. Fletcher, F. Wang, L. Bai, J. Whiteford, D. Cullen2007 International Conference on Power Engineering, Energy and, 2007. ieeexplore.ieee.org [Preprint]. Available at: https://ieeexplore.ieee.org/abstract/document/4380208/ (Accessed 26 March 2025).

Halo Energy kicks off production of shrouded micro wind turbines (no date). Available at: https://renewablesnow.com/news/halo-energy-kicks-off-production-of-shrouded-micro-wind-turbines-646126/ (Accessed: 28 September 2023).

Hansen

MOL

(2008) Aerodynamics of Wind Turbines. 2nd edition. London: Earthscan.

K-Omega Turbulence Models, Global Settings, SimScale (no date). Available at: https://www.simscale.com/docs/simulation-setup/global-settings/k-omega-sst/ (Accessed: May 10, 2024).

10.

Katooli

Noorollahi

(2022) Shrouded wind turbines: a critical review on research and development. International Journal of Ambient Energy 43(1): 8775–8791. DOI: 10.1080/01430750.2022.2102064.

11.

Manwell

McGowan

Rogers

(2010) Wind Energy Explained: Theory, Design and Application, Wind Energy Explained: Theory, Design and Application. John Wiley & Sons Ltd. DOI: 10.1002/9781119994367.

12.

Matsushima

Takagi

Muroyama

(2006) Characteristics of a Highly Efficient Propeller Type Small Wind Turbine with a Diffuser. Elsevier. T Matsushima, S Takagi, S MuroyamaRenewable energy, 2006, Elsevier [Preprint]. Available at: https://www.sciencedirect.com/science/article/pii/S0960148105002144 (Accessed 25 March 2025).

13.

Ohya

Karasudani

(2010) A shrouded wind turbine generating high output power with wind-lens technology. Energies 3(4): 634–649. DOI: 10.3390/en3040634.

14.

Rajan

Ponta

(2019) A novel correlation model for horizontal axis wind turbines operating at high-interference flow regimes. Energies 12(6): 1148. DOI: 10.3390/en12061148.

15.

Wilson

(1980) Wind energy systems. NJ: Prentice Hall. doi: 10.1201/b18363-6.