Wind turbine wake position detection and rotor speed-based wake steering validation in a wind tunnel wake simulator

Abstract

Wind farm control has demonstrated power production improvements using yaw-based wake steering compared to individual turbine optimization. However, slower yaw actuation rates in response to rapid inflow changes lend to impracticality of yaw-based steering, as it causes time-varying downstream rotor–wake overlap, power production fluctuation, and consequent reduction. Therefore, closed-loop wake control is required to mitigate wake deflection uncertainty. To respond to rapid inflow variations, rotor speed actuated is investigated here. Furthermore, wake position information is required as feedback for closed-loop control function. For field-installed turbines, nacelle-based Light detection and ranging (LIDAR) is expected to provide this information. So far, LIDAR-derived wake position has been determined through model-based field reconstruction of scattered LIDAR data. However, this requires sophisticated, economically prohibitive LIDARs. To incorporate inexpensive, two-beam LIDAR for wake detection, a tip vortex-based approach was developed and is also presented here. These contributions can be considered as intermediate steps toward realization of a novel closed-loop wake control.

Keywords

Wind turbine wake wind farm control wake deflection wake detection wind turbine in yaw

Introduction

To make wind power competitive with other conventional energy sources, it is important to minimize the cost of wind energy, thus maximize the power production. Grouping wind turbines in large multirow facilities referred to as wind farms has contributed in achieving this goal given the benefits of economies of scale. However, wind turbines within a wind farm are often operating in the wake of another turbine, leading to a reduction in power production and an increase in structural loads. To minimize wake effects, wind turbines are typically spaced between 7 and 10 rotor diameters D. However, full-scale wake measurements have revealed that the wake influence can extend in excess of 20–30D (Hirth and Schroeder, 2013; Hirth et al., 2012). On the contrary, the current wind farm control industry practice relies on the greedy control approach to optimize the power production of individual wind turbines (e.g. Abdullah et al., 2012; Koutroulis and Kalaitzakis, 2006; Kumar and Chatterjee, 2016) without considering the impact of wake effects on neighbor turbines. This approach is suboptimal for the wind farm performance as wind farm wakes account for up to 20% power loss (compared to the case where all the turbines are exposed to freestream inflow $U$ _∞), which have been quantified by Barthelmie et al. (2009, 2010). Therefore, to minimize wake-induced effects, recent research in wind farm control has become wake control oriented, where the aim is to modify the wake flow configuration within a wind farm by adjusting the wind turbines’ control actions (Boersma et al., 2017). Yaw-based wake steering is one of the wake control methods found in the literature, where the wake of an upstream turbine is redirected to avoid or reduce overlapping with downstream turbines. This method has been investigated for specific array configurations, showing increases in overall wind farm power production compared to the greedy control approach in simulation studies (Fleming et al., 2015; Moskalenko et al., 2011) and validated in wind tunnel experiments (Adaramola and Krogstad, 2011; Schottler et al., 2016).

Furthermore, yaw-based wake steering optimization procedures have been proposed with the goal of finding a set of yaw angles among multiple wind turbines that produce a certain wake configuration within the wind farm leading to maximum power production (Gebraad et al., 2016). It is important to note that wake models used to describe the flow for wind farm optimization are static in nature (i.e. assuming steady-state conditions), which overestimate the expected power production enhancement. Although dynamic model considerations are available in recent literature (Munters et al., 2016), simplified models are preferred by the industry, since in their experience, the cost-to-benefit ratio of higher-fidelity models is yet to be realized (Ulker, 2018). In real operating conditions, the wake propagation is dynamically disturbed by the continuously changing wind direction. These wake deflection dynamics might lead to continuous overlapping with downstream rotors, increasing structural loads, causing fluctuation in overall power production, and a consequent reduction in average power production compared to the predicted power output from the static models. In this regard, closed-loop wake steering control can be an effective approach to guide the wake to a steady position in response to changing wind conditions, as illustrated in Figure 1.

Figure 1.

Wake trajectory stabilization. Solid lines: desired wake trajectory; dotted lines: instantaneous disturbed wake trajectory.

One aspect of the closed-loop wake steering control is the control action to actuate the wake. Currently, the wake deflection strategies are driven by active turbine yaw. However, the efficacy of yaw-based wake steering to control the wake position is reduced at small timescales, as the wind variations are too rapid to follow by the low-acting yaw actuator (Vollmer et al., 2016). Studies in the field (Bromm et al., 2018) and wind tunnel (Castillo et al., 2017) have demonstrated that yaw-based wake steering is more prominent at larger downstream distances (>7D). In addition, parameterizations, such as Guntur et al. (2012), show that wake deflection significantly increases with downstream distances. A small change in mean wind direction can produce a significant variation in wake conditions of downstream turbines, which leads to large changes in overall wind farm power output (Porté-Agel et al., 2013). This emphasizes consideration of large-scale mean-flow transients (mesoscale, 10–100 km or 10–100 min, weather changes) to accurately predict wind farm power for typical meteorological conditions (Munters et al., 2016). Furthermore, it is critical that the wake steering control action has a time response that is less than the timescales at which the mesoscale transients are observed to be effective. In addition, the yaw mechanism actuation, although relatively quick (0.5°/s), must rest at the new position for a certain time period. References state a resting period of 10 min (Mikkelsen et al., 2013; Thorson, 2013) and consequent yaw activation only upon meeting predetermined yaw error threshold (Kim and Dalhoff, 2014). This slow movement and frequent pause are required to minimize the effect of destabilizing gyroscopic forces (Kim and Dalhoff, 2014). Therefore, although inter-turbine wake or flow propagation timescale is within the yaw system’s time response, a minor yaw-misalignment local error will likely lead to a high downstream wake deflection. This will reduce the efficacy of the yaw-based wake steering, since the local wind direction is not accurately predicted by upstream measurements. Therefore, to dynamically steer the wake in response to wind variations, the effect of a much faster control action such as rotor speed on the wake deflection is discussed in this article.

In addition, the ability to sense the wake position that could be used to provide the feedback required to adjust the wake trajectory is considered here. Wake position has been characterized in terms of the location of the wake center in several wind tunnel studies using particle image velocimetry (PIV) (Howland et al., 2016). For field-installed turbines, Raach et al. (2016b) proposed local tracking of the wake center trajectory using nacelle-mounted Light detection and ranging (LIDAR) looking downstream. Since the wake center is a function of the geometry of the wake profile (Howland et al., 2016; Vollmer et al., 2016), it cannot be derived directly from single LIDAR line-of-sight measurements. In the wake center tracking approach described in the study by Raach et al. (2016a), the wind velocity field and hence the location of the wake center were estimated through the model-based wind field reconstruction approach. This wake detection approach was tested with high-fidelity simulation SOWFA (Simulator for Off/Onshore Wind Farm Applications), but validation at full scale has not been reported yet. Full-scale wake measurements, where the wake characteristics were derived through the field reconstruction approach, have been reported by Fleming et al. (2016) and Herges et al. (2017) using a ground-based modified LIDAR mounted on the nacelle measuring downstream. However, such LIDAR requires the ability to focus at different ranges and perform complex scanning patterns, which represent a cost issue limiting its commercial application. Therefore, to enable wake detection with simple, low-cost, off-the-shelf nacelle-mounted LIDAR, a wake detection strategy based on the location of the tip vortices is presented in this article. However, in this wind tunnel-based study, explained in section “Methodology,” downwind measuring hot-wire anemometers that emulate nacelle-mounted LIDAR function are utilized for the LIDAR-based wake detection construct validation.

Methodology

Given the inherent unsteady nature of the turbulent inflow, dynamic changes in rotor speed, yaw, and pitch angle should be considered (Westergaard, 2012). High-fidelity simulation tools have been used to study open-loop wake control, requiring substantial computational resources and time (Annoni et al., 2014; Munters et al., 2016). However, it will require significant efforts to accommodate and study unsteady inflow combined with required dynamic controls, substantially increasing computing resources and time. On the contrary, exploring innovative strategies in full-scale turbines is impractical and expensive and may not be available for iterative development of wake control strategies. Therefore, the wind tunnel-based “Hyper Accelerated Wind Farm Kinematic-Control Simulator” (HAWKS), deployed in the study by Castillo et al. (2016, 2017) to characterize wake flow and test wake control strategies, was adapted to test the wake actuation and the wake detection strategies separately.

Wake deflection actuation

Several studies (Guntur et al., 2012; Jiménez et al., 2010; Park et al., 2013) have pointed out that the degree of wake deflection is proportional to the thrust coefficient C_T changes. When a wind turbine operates in yawed conditions, the thrust force T exerted by the wind on the turbine rotor can be divided in two components $T_{x}$ and $T_{y}$ , as depicted in Figure 2. The former is parallel to the inflow wind direction and slows down the flow downstream. The latter is in the cross-stream direction and is responsible for the wake deflection.

Figure 2.

Simplified schematic of the forces exerted on the wake of a yawed turbine.

Since the thrust force component $T_{y}$ is larger with increasing yaw angle offset $ϒ$ , the degree of wake deflection is predominantly proportional to $ϒ$ . Since C_T is a function of $ϒ$ , tip speed ratio (λ), and blade pitch, the degree of wake deflection is not exclusively due to $ϒ$ , but it might also be associated with λ and blade pitch, which are much faster turbine control actions than yaw that may affect the wake deflection while operating in region 2 of the power curve (wind speeds above cutin and below the wind speed where turbine produces rated power). The wake actuation methodology introduced by Castillo et al. (2017), which uses a combination of statically applied yaw offset and dynamic rotor speed variation, showed wake recovery enhancement, but its impact on the wake deflection was inconclusive due to small field of view. Therefore, HAWKS platform tests of rotor speed-based wake deflection will be presented here.

Wake detection

LIDAR technology proposed in the studies by Raach et al. (2016a, 2016b) has been adapted to focus at different ranges and perform complex scanning patterns. However, for practical implementation, wake detection approach development based on a simple, inexpensive fixed focus, two-beam LIDAR is considered here. The continuous-wave, two-beam nacelle-mounted LIDAR model WindEye, developed by Windar Photonics A/S, is used as the primary reference for the development of the wake detection methodology. This LIDAR is feasible due to its lower power laser, smaller optics, and no moving mechanical parts. The LIDAR scanning pattern geometry is shown in Figure 3, where the angle between the two beams is 13° and the focus distance is 80 m.

Figure 3.

Instantaneous vorticity contours under dynamic yaw misalignment illustrating the wake tip vortex movement relative to fixed LIDAR focus point (purple dot).

A characteristic of the near wake is the presence of the tip vortices shed by the blade tips, which rotate and propagate downstream, defining the boundary between the wake and the outer flow, in other words, defining the wake envelope. The wake deflection in the near wake area might be defined by the tip vortices as reported by Haans et al. (2007).

Therefore, as the yaw angle offset changes due to the inherent variability in the inflow, the wake interface defined by the tip vortices moves in and out of the boundary set by the LIDAR focus points as illustrated in Figure 3. The location of the tip vortices was estimated from turbulent statistics based on instantaneous velocity field measurements (e.g. Castillo et al., 2017; Lynum, 2013). These studies are wind tunnel-based using both PIV and hot-wire anemometry, respectively. On the contrary, in a wind tunnel study reported in the study by Van Dooren et al. (2017), hot-wire anemometry showed good agreement with LIDAR measurements, implying its potential to emulate LIDAR wake detection function in wind tunnel using hot-wire anemometry. Therefore, to estimate the position of the tip vortices and hence the wake envelope relative to the LIDAR focus points, hot-wire anemometry will be incorporated in the HAWKS platform to define the instantaneous wake position.

Experimental setup

The HAWKS platform was developed in the National Wind Institute (NWI) closed-loop wind tunnel at Texas Tech University. The test section has a cross section of 1.2 m × 1.8 m. The mean stream-wise incoming velocity (free stream) was kept constant at $U$ ∞ = 10.85 m/s in all the experiments performed here. The horizontal-axis fully controllable turbine used in these experiments is three-bladed, with a diameter D of 0.27 m. The rotor blades are NACA 0012 airfoils with a constant chord of 0.013 m with no twist from root to tip. The rotor drove a small 15 W AMETEK 8693S039-SP DC generator equipped with an encoder to measure the rotor speed $ω$ , which is controlled using a DC–DC buck converter linked to the generator output. The gate switching is controlled by pulse width modulation (PWM) signal of appropriate duty cycle to change the apparent load seen by the generator such that the desired rotor speed is achieved. The pitching mechanism was extracted from a WLtoys model V931 RC helicopter, which comprises a set of three servomotors to perform collective pitch in the range of 0°–24° in steps of 0.3°. Both the pitching mechanism and the generator were enclosed in a nacelle of 0.07 m × 0.038 m cross section and 0.13 m length. In this study, the pitch angle was fixed at 6°. The tower base is coupled to a Futaba S9155 servomotor that allows a variation in the yaw angle. The rotation angles of both pitch and yaw servomotors as well as the rotor speed are controlled through PWM signals generated from Texas Instruments TMS320F28335 digital signal processor (DSP) board in combination with in-house built Simulink code.

The velocity field across a planar area of the flow field downstream of the turbine was measured with the PIV technique, and its setup is shown in Figure 4(a) to (d). Since PIV camera placement was feasible along the wind tunnel sidewall and not the bottom, to obtain wake cross-section measurements in a plane that is horizontal in the field, the turbine tower was mounted to the tunnel sidewall and not its floor. Figure 4(a) shows the 90° rotated wind turbine, where the tower appears to be a cantilever beam, and Figure 4(a) and (b) shows the laser light sheet location that represents the measurement plane at hub height. A 150 mJ/pulse Nd:YAG laser (532 nm) coupled with laser sheet forming optics was used to illuminate the flow seeded by a particle generator using propylene glycol oil droplets. The particle images were recorded with an array of four 14-bit LaVision charge-coupled device (CCD) cameras (Figure 4(c)) with a resolution of 1600 × 1200 pixels each using 50 mm focal length lens that were synchronized with the laser pulses. The time interval dt between two laser pulses was set to 300 μs. However, the HAWKS PIV system characteristics described in the study by Castillo et al. (2017) were modified in order to accommodate a larger field of view. The CCD cameras were 2.75 m away from the light sheet plane, so that the image yielded a total inspection area of about 2.24D × 7.5D (Figure 4(b)), with an image resolution of 2.52 pixels/mm. The image processing was carried out with Davis software (v8.2.2) from LaVision. The size of the interrogation window in the correlation calculation was changed to 16 × 16 pixels with an overlap of 50%. A summary of the HAWKS platform characteristics is listed in Table 1.

Figure 4.

HAWKS PIV setup: (a) model wind turbine when observed from upstream, (b) wind tunnel test section, (c) schematic of the PIV system, and (d) four-camera array.

Table 1.

Summary of HAWKS platform characteristics.

Wind tunnel characteristics
Test section dimensions (H × W)	1.2 m × 1.8 m
Mean stream-wise velocity $(U)$	10.85 m/s
Rotor
Number of blades	3
Airfoil section	NACA 0012
Rotor diameter (D)	0.27 m
Chord	0.013 m
Blade pitch angle	6°
PIV measurements
Field of view (W × L)	2.24D × 7.5D

HAWKS: Hyper Accelerated Wind Farm Kinematic-Control Simulator; PIV: particle image velocimetry.

To describe the wake flow turbulent behavior in detail, a Dantec Dynamics 54T42 MiniCTA (constant temperature anemometer) equipped with 55P16 hot-wire probe was utilized to emulate the LIDAR function. The data acquisition is carried out with a 16-bit NI 9215 DAQ. The hot-wire probes were mounted on the traversing system (Figure 5) to move the probe around the wake flow field.

Figure 5.

Hot-wire traverse system located downstream the wind turbine.

Results

This section outlines the results of the tests performed in the HAWKS platform. Before a closed-loop algorithm is tested in HAWKS, its components that form an open loop were tested. The closed-loop control requires an input of wake location and an ability to respond to the input to actuate wake deflection of desired location. Each of these aspects, wake detection, and actuation were tested independently. This section discusses the experiments undertaken to test these for the HAWKS platform.

Wake deflection actuation

To illustrate the deflection of wakes of the yawed turbine with variable rotor speed, measurements were made for static yaw offset $ϒ$ = –20° (following the convention defined by Marathe (2016)) and three different rotational speeds of ω = 3000, 4000, and 5000 r/min corresponding to λ = 3.91, 5.21, and 6.51, respectively. A set of 1000 PIV image pairs were recorded over a period of 100 s for every rotor speed case. The image plane was at the hub-height horizontal plane. The stream-wise, u, and span-wise, v, components of the instantaneous velocity vector fields were calculated using LaVision Davis software. The instantaneous stream-wise velocity was averaged and normalized with the undisturbed inflow velocity $U$ ∞. Figure 6 shows the resulting contours of normalized mean stream-wise velocity $(U / U)$ for each rotor speed case.

Figure 6.

Contours of the normalized mean stream-wise velocity $(U / U)$ in the hub-height horizontal plane for (a) ω = 3000 r/min, (b) ω = 4000 r/min, and (c) ω = 5000 r/min, at yaw angle $ϒ$ =−20°.

Figure 6 shows that the wake is more pronounced as the rotor speed $ω$ increases, which is expected due to the increase in the thrust force component parallel to the inflow ( $T_{x}$ ). However, the degree of wake deflection is not clearly revealed in Figure 6. To quantitatively compare the wake deflection, cross-stream profiles of $(U / U)$ at x/D = 3 are shown in Figure 7.

Figure 7.

Comparison of normalized mean stream-wise velocity $(U / U)$ profile in the hub-height horizontal plane at x/D = 3 for yaw angle $ϒ$ = –20°.

Profiles shown in Figure 7 reveal wake shift as ω increases due to an increasing thrust force. To further illustrate this, the location of the wake center $y_{c} (x)$ , at each downstream distance from the turbine x/D, was determined by taking the center of mass between the two points with $U / U$ = 0.95, following the approach suggested by Howland et al. (2016). The results shown in Figure 8 reveal that the wake deflects more for turbines operating at higher ω. This observation implies a larger thrust component in the cross-stream direction as ω increases, which is in agreement with previous studies (Guntur et al., 2012; Jiménez et al., 2010; Park et al., 2013). Since C_T was not directly measured, C_T was estimated by comparing the experimentally measured wake deflection $y_{c} (x)$ against the linear empirical model proposed by Guntur et al. (2012)

\frac{y_{c}}{D} = 0.24 \frac{x}{D} C_{T} t a n (ϒ) + o f f s e t

(1)

Figure 8.

Comparison of measured wake deflections $y_{c} (x)$ for ω = 3000, 4000, and 5000 r/min at yaw angle of $ϒ$ = –20° with wake deflection given by equation (1).

In the three cases, Figure 8 shows that the empirical model applied to cases C_T = 0.55, 0.66, and 0.85 works reasonably well with the measured wake deflection at $ϒ$ = –20° for ω = 3000, 4000, and 5000 r/min, respectively. Furthermore, these C_T values agree with a simple Blade Element Momentum (BEM) model of the setup. The C_T values were selected since their corresponding curves obtained from equation (1) appear to be the best fit to the measured $y_{c} (x)$ . For the purpose of control, it is noted that based on the measured wake deflection, the estimated ${C_{T} (λ) |}_{ϒ = - 20 °}$ exclusively due to λ exhibited a monotone behavior with $λ$ .

Detection task

As the wake deflection changes, the fixed LIDAR focus point sweeps across the wake interface. The power spectral density (PSD) of the measured stream-wise velocity u at the focus point will vary depending on the relative wake interface location. The spectra were calculated with discrete Fourier transform (DFT). At each measurement point, the total number of samples was divided into 10 successive windows, and the DFT of each successive window was then calculated and finally averaged to find the mean spectrum E_uu(f). The resulting spectrum was normalized by the free-stream velocity variance σ_u². As a starting point, only zero-yawed case is analyzed here.

The tip speed ratio of the model turbine was set to 3.91, corresponding to 3000 r/min. The hot-wire probes acquired data at 20 kHz, which provides 400 samples per revolution. The sampling time of each window was 0.1024 s, giving a total of 2048 samples, corresponding to about 5 revolutions. First, the hot-wire probes were traversed across the wake interface very close to the rotor at x/D = 0.2 on one side of the wake to perform measurements at three locations. Figure 9(a) shows a comparison of the spectral characteristics at each location. The peaks in Figure 9(a) demonstrate that at every position across the wake interface, the dominant frequencies are multiples of the rotor rotational frequency, 3000 r/min or 50 Hz. The once-per revolution occurrence is denoted as 1 P. The second dominant peak is close to 100 Hz, meaning two occurrences per revolution and is denoted as 2 P. The third peak is close to 150 Hz, meaning three occurrences per revolution and is denoted as 3 P. The peak occurrences shown in the spectrum represent tip vortices.

Figure 9.

Comparison of spectral characteristics across the wake interface at x/D = 0.2: (a) PSD and (b) 1, 2, and 3 P spectral energy.

To better interpret the peaks resulting from the PSD shown in Figure 9(a), Figure 9(b) shows the spectral energy at 1, 2, and 3 P frequencies of the different points across the wake interface at x = 0.2D. At y/D = 0.45 (the closest to the rotor center), the peaks are barely visible and hence there is no evidence of the presence of the tip vortices at this location. At y/D = 0.5, the three peaks are clearly present. At y/D = 0.55, the only peak at 3 P is an indication that the three tip vortices are clearly present at this location. When comparing the spectral energy at 3 P between y/D = 0.5 and 0.55, there is almost no change.

However, the peaks at 1 and 2 P increase the most at y/D = 0.5 compared to y/D = 0.55 and y/D = 0.44, indicating the presence of wake interface. Furthermore, the peak at 1 P is an indication that the tip vortices have merged. The peak at 2 P is also prominent, perhaps indicating an intermediate state during the vortex merging at this measurement location, which is close to the rotor plane (x = 0.2D).

The hot-wire probes were traversed across the wake interface at x/D = 3, the plane where the nacelle-mounted LIDAR would be focused, to perform measurements at six locations. Figure 10(a) shows a comparison of the spectral characteristics at each location. Figure 10(b) clearly reveals a slight difference among the spectral energy across the wake interface at both 2 and 3 P. On the contrary, the peak at 1 P, which is the most evident peak in every position, shows a significant variation across the wake interface reaching a maximum at blade tip y/D = 0.5. The fact that 1 P peak dominates at x/D = 3 at a wider range might be an indication that the tip vortices have completely merged and further diffused.

Figure 10.

Comparison of spectral characteristics across the wake interface at x/D = 3: (a) PSD and (b) 1, 2, and 3 P spectral energy.

Figure 11 illustrates the effect of changing the rotor speed on the spectrum characteristics at x/D = 3. Figure 11 shows that the dominant peak in all the spectrums is at 1 P. It also reveals two features when the rotor speed increases: (1) the 1 P peaks occur at higher frequencies (shifted to the right) and (2) the 1 P peak spectral energy increases. The former is expected as the rotational frequency of the rotor is higher. The tip vortex circulation is proportional to the turbine blade lift force described by the Kutta–Joukowski law (Lynum, 2013). Therefore, the latter is caused by an increase in the lift force on account of the angle of attack increase due to rotor speed increase.

Figure 11.

Effect of tip speed ratio on the spectral characteristics at x/D = 3 and y/D = 0.5.

Subsequently, the PSD magnitude for 1 P will be used to calibrate the location of the wake interface relative to the hot-wire location. This information will be used as an input to the actuation system. The rotation speed will be adjusted such that the wake is moved to a desired location. These experiments will be reported in future studies.

Conclusion and future work

In this article, experimental validation rotor speed-based wake steering and position detection in a controlled wind turbine wake simulator was presented. First, the influence of the rotor speed $ω$ on the wake deflection was demonstrated and compared to a wake steering model to estimate C_T. The results showed that C_T variation exclusively due to λ was large enough to deflect the wake. As discussed, yaw angle will vary naturally and thus cause variable wake location, but variable speed is sufficient to counter this motion. Second, a spectrum analysis was carried out to characterize the presence of the tip vortices in the wake interface to assist in the development process of a tip vortex wake detection approach that can be implemented with fixed two-beam LIDAR to track the location of the wake. The initial stage of the study, which involved only a zero-yaw case, showed that the 1 P spectral energy peaks at x/D = 3 dominated and showed a significant variation across the wake interface. Furthermore, as the rotational frequency increases, the spectral energy at 1 P occurs at higher frequency (right shift) and also has greater magnitude. In the future, the spectral characteristics for full-scale turbine will be compared with HAWKS, since, at full-scale, perhaps the vortices may not merge at x = 3D and spectral energy at 3 P may be more prominent. Regardless, the Windar Photonics LIDAR measurement frequency is sufficiently higher than nominal 1, 2, or 3 P rotational frequency of SWiFT turbines and certainly higher for installed full-scale turbines (>80 m). These contributions can be considered as intermediate steps toward realization of a novel closed-loop wake control in future.

Footnotes

Acknowledgements

The experiments were performed at the TTU’s National Wind Institute (NWI) closed-loop wind tunnel at Texas Tech University.

Declaration of conflicting interests

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

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was funded by the Governor’s Office in the State of Texas Emerging Technology Funds (ETF), Global Laboratory of Energy Assets Management and Manufacturing (GLEAMM), and the Binational Industrial Research and Development (BIRD) Foundation.

ORCID iD

Ricardo Castillo

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