# Experimental and numerical comparison of aerodynamics for a wind turbine rotor model under turbulent inflow conditions

Mylène Dumanoir, Annie Leroy, Emmanuel Guilmineau, Stéphane Loyer, Azeddine Kourta

► To cite this version:

Mylène Dumanoir, Annie Leroy, Emmanuel Guilmineau, Stéphane Loyer, Azeddine Kourta. Experimental and numerical comparison of aerodynamics for a wind turbine rotor model under turbulent inflow conditions. Journal of Physics: Conference Series, 2024, 2767 (2), pp.022020. 10.1088/1742-6596/2767/2/022020. hal-05016875

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PAPER • OPEN ACCESS

Experimental and numerical comparison of aerodynamics for a wind turbine rotor model under turbulent inflow conditions

To cite this article: M Dumanoir et al 2024 J. Phys.: Conf. Ser. 2767 022020

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# Experimental and numerical comparison of aerodynamics for a wind turbine rotor model under turbulent inflow conditions

M Dumanoir¹, A Leroy¹,², E Guilmineau³, S Loyer², A Kourta²

¹ Centre de Recherche de l'Ecole de l'Air et de l'Espace (CREA), Ecole de l'Air et de l'Espace, 13661 Salon-de-Provence Air, France.

² PRISME Laboratory, University of Orleans and INSA Centre Val de Loire, 45100 Orléans, France.

³ Laboratoire de recherche en Hydrodynamique, Énergétique et Environnement Atmosphérique (LHEEA), CNRS UMR 6598, Ecole Centrale de Nantes, 44300 Nantes, France.

E-mail: mylene.dumanoir@ecole-air.fr

**Abstract.** Computational prediction of aerodynamics for a two-bladed wind turbine rotor model at the profile, blade, and rotor scale are compared to wind tunnel experimental data. Wind tunnel tests are carried out in the large subsonic wind tunnel at the University of Orleans with an instrumented rotor model to obtain power and thrust coefficients, bending moments and pressure chordwise distributions. A passive uniform turbulence grid is used to obtain a turbulence intensity of 3.8% at the front of the rotor. A $k-\omega$ SST unsteady Reynolds-averaged Navier-Stockes (URANS) simulation with the ISIS-CFD flow solver is conducted with a fully resolved rotor, with and without automatic grid refinement. The focus of this study is to examine the prediction capabilities of the simulation for various physical variables. The simulation demonstrates a relatively good prediction of power and thrust coefficients compared with experimental data with a maximum scatter of 8.5%. The prediction of the pressure coefficient distribution at three different radial positions is satisfactory, but shows discrepancies when it comes to accurately predicting flow separation and Reynolds effects.

## 1. Introduction

In wind farms, wind turbines are subject to turbulent flows resulting from upstream wind conditions and wakes. The concomitant presence of three-dimensional, rotational, turbulent effects, or even separated flows makes the flow fields highly complex. The prediction of rotor aerodynamics in such conditions must be reliable as it is directly related to wind turbine performances and therefore to the estimation of fatigue of the latter. Numerical modeling for the analysis of rotor aerodynamics will need to rely on a new generation of high-fidelity modeling [1] accompanied by increasingly complex experimental configurations and on-site measurements.

Bartl and Sætran [2] worked on the effects of various turbulent inflow conditions on a row of two wind turbines. By comparing experimental and numerical results, their study focused on evaluating the performances of both wind turbines and to assess wake velocity deficit. One of their results showed that the turbulence effect generated by a passive grid on the power and thrust coefficients is well predicted by $k-\omega$ SST turbulence model among RANS models. To extend the previous study, Chen et al. [3] carried out a fully resolved rotor IDDES simulation.

<figure><img src="image_4.png" /></figure>
They found that the mean power and thrust coefficients agreed well with existing experimental data. It is also shown that turbulence had few impacts on these latter coefficients except in shear inflow conditions. Krogstad and Lund [4] compared experimental and numerical results on predicting wind turbine model performances. The numerical predictions are considered satisfactory compared to the experimental results despite overestimating the power coefficient for tip speed ratios greater than 6. This can be explained by difficulties in estimating blockage effects and drag force. Moshfeghi et al. [5] compared experimental and numerical results to study the aerodynamic behavior at different scales for their cases. The SST-$k-\omega$ seems to fail to predict the separation point emergence. The thrust values are mispredicted for highly separated flows. They suggested paying particular attention to the number of nodes in the chordwise and spanwise directions, close to the surface. Lee et al. [6] conducted the same type of comparison using a twisted and non-twisted blade. With the twisted blade a better optimum power coefficient is unsurprisingly obtained because it is designed to alleviate separated flows. This brief literature review shows that further efforts are still required to make progress in the development of high-fidelity modeling.

This paper is part of a project that aims to make further progress in the development of reliable CFD tools for studying and predicting rotor aerodynamics and aeroelasticity. Before running simulations with a more complex turbulence model and higher Reynolds number, the initial step is to compare characteristic physical quantities derived from high-fidelity 3D URANS simulations with and without automatic grid refinement and measured from wind tunnel experiments performed on a wind turbine model under turbulent inflow conditions. Sensors are fitted on this model to get data at the airfoil, blade, and rotor scales.

First, a comprehensive description of the wind turbine experimental model and its testing environment are presented, offering sufficient information to enable the setup of a numerical simulation. Then, the focus shifts to the parameters of this simulation. Secondly, a comparison is presented between the numerical results and the measured data obtained from the wind tunnel investigation, with particular emphasis on the turbulent inflow conditions. Afterwards, the paper is concluded with key findings and future perspectives.

## 2. Methods

### 2.1. *Experimental setup*

The experiments are performed in a closed-loop large subsonic wind tunnel. The wind turbine model (visible in figure 1(a)) is positioned in the return test section at the exit of a convergent which has a section of 3 × 3 m².

<figure><img src="image_5.png" /><figcaption>**Figure 1.** (a) Passive turbulence grid and wind turbine model; (b) NACA 65₄-421 airfoil</figcaption></figure>

The incoming wind speed $U_{ref}$ is controlled by one Pitot probe located at 0.3 m of the wind
tunnel wall and 1 m upstream of the rotor, and by a second one in the main test section. From these two probes, it is possible to observe the blockage effect. Each acquisition being unique, the upstream velocity $U_{ref}$ is specified in table 1. A passive and uniform turbulence grid is used as shown on figure 1(a). According to Wagner *et al.* [7], the turbulent intensity $TI$ varies from 2 to 14% on-shore. This grid made it possible to achieve a case at $TI=3.8\%$. The bar width $b$ and the mesh size $M$ are equal to 25 and 225 mm respectively. The turbulence grids are characterized using hot wire anemometry at the rotor position without the wind turbine model in the test section. More precision are provided in the section 2.1. The distance grid-rotor $d$ is 2.7D. The wind turbine bench has a hub height of 1.56 m. Designed to measure its performance and its local aerodynamic properties by measuring the chordwise pressure distribution at various radial positions [8], the wind turbine bench is equipped with two untwisted and untapered blades with a rotor diameter $D$ of 1.4 m and a swept rotor surface $S_{rotor}$ of $1.54 \ m^2$. The airfoil section is a NACA 654-421 as presented in figure 1(b) with a chord length $c$ of 0.1 m, slightly modified at the trailing edge for Baleriola works [9].

The turbine rotates counter-clockwise when the observer is facing the wind. To have the same order of magnitude in tip speed ratio $\lambda = \frac{\Omega r}{U_{\infty}}$ [10] as full-scale rotors, the wind turbine bench is made up of a reversible motor Phase Ultract 509 that enables the monitoring of the turbine rotational velocity up to 1000 rpm. Thus, a kinematic scaling is chosen. The servomotor provides energy when the working point of the turbine is propulsive and dissipates energy when the working point is extractive (present case).

The blade is equipped with several sensors. Placed at the rear of the turbine, the torque and thrust of the wind turbine are measured with a Scaime M2392 transducer. For the flapwise bending moment $M_{fbm}$, Kyowa strain gages full bridge circuit are placed at the roots of the two blades. 27 pressure taps measure the mean pressure distribution. They are placed on one of the two blades in chordwise at three different possible radial positions $r/R$: 38, 65, and 88%. The taps are connected to a 32-channel differential pressure scanner ESP-32HD (GE, ±0.361 PSI) embedded in a MicroDAQ system (CHELL). A pitch angle $\beta$ of 4° is chosen since it is a nominal point.

The experimental acquisition parameters are given in Table 1.

<table><caption>**Table 1.** Sampling parameters. Abbreviations: $No_{rev}$ for rotor revolution number, HWA for hot wire anemometry. $U_{ref}$ is obtained at the test section center with HWA (*) or at $\lambda = 0$ with the Pitot probe (**).</caption><thead><tr><th></th><th>Measurement means</th><th>Sampling freq. [Hz]</th><th>No. of samples</th><th>Acquisition time</th><th>U<sub>ref</sub> [m.s<sup>-1</sup>]</th></tr></thead><tbody><tr><td>Sweeping (fig. 3)</td><td>HWA</td><td>10 000</td><td>65 536</td><td>6.5 s</td><td>-</td></tr><tr><td>Mapping (fig. 4)</td><td>HWA</td><td>10 000</td><td>65 536</td><td>17.5 s</td><td>9.85*</td></tr><tr><td>Torque, thrust, M<sub>fbm</sub></td><td>See sect. 2.1</td><td>500</td><td>15 000</td><td>No<sub>rev</sub> = 0 - 400</td><td>9.18**</td></tr><tr><td>C<sub>p</sub></td><td>See sect. 2.1</td><td>500</td><td>15 000</td><td>No<sub>rev</sub> = 150, 400</td><td>10.26**</td></tr></tbody></table>

## 2.2. Numerical setup

Numerical simulations are carried out by the flow solver ISIS-CFD, developed by CNRS and Centrale Nantes. An incompressible unsteady Reynolds-Averaged Navier-Stokes (URANS) simulation is performed using $k - \omega$ SST model for turbulence closure.

Based on the finite volume method, the solver builds the spatial discretization of the transport equations. The unstructured discretization is face-based. A second-order backward difference
scheme is used to discretize time. The velocity field is obtained from the momentum conservation equations and the pressure field is extracted from the mass equation constraint, or continuity equation, expressed into a pressure equation. Transport equations for the variables in the turbulence model are added to the discretization [11]. The full rotor geometry with the hub and a part of the nacelle is resolved in its frame of reference. The simulations are carried out with a rotating frame method which means that the equations are written in the rotor frame. The computational domain consists of a cylindrical domain whose diameter is 10 m = 7.14D, and the height is 14 m = 10D, with D the rotor diameter. The inlet is located at 2.8 m = 2D in front of the rotor plane. At the inlet and the external boundary of the computational domain, a Dirichlet condition is used while at the outlet the pressure is imposed. For the blades and the shaft, a no-slip condition is used and for the rotation axis it is a wall function. The upstream velocity is fixed to $U_{\infty, num} = 9.38$ m/s.

The mesh is generated with Hexpress™, an automatic unstructured mesh generator that generates meshes containing only hexahedrons. The mesh consists of $18.6 \times 10^6$ points. The number of faces to represent the geometry is $5.7 \times 10^5$ with $2.5 \times 10^5$ faces for each blades. The average wall normal resolution on the blades is $y^+ = 0.6$ while for the shaft the value is 0.2 and for the rotation axis 3.3. The automatic grid refinement (AGR) is activated for few rotation velocities and the mesh depends on the chosen tip-speed ratio. AGR in ISIS-CFD is performed by local division of unstructured hexahedral meshes [12]. The decision where to refine is based on a metric refinement criterion, a tensor field computed from the flow. The tensor is based on the second derivative of the flow variables. The refinement criterion is the flux-component Hessian. The grid is refined until the dimension of each cell satisfy the threshold $Tr$ and the appropriate mesh density is specified by the minimum cell size. Figure 2 represents the variation of the mesh depending on the radial position $r$ with and without AGR. It should be noted that the mesh without AGR is the same whatever the radial position $r$.

<figure><img src="image_6.png" /><figcaption>Figure 2. Meshes $r$ without (a) and with AGR at 3 different radial positions (b)(c)(d) for $\lambda = 2.4$</figcaption></figure>

The time step for all cases is 0.025 s. The table 2 gives information for 3 tip-speed ratios $\lambda$.

### 3. Results and discussion

#### 3.1. Inflow conditions

The first step is to characterize the upstream turbulent flow obtained by the passive grid. Three-component hot-wire measurements allow the calculation of turbulence intensities defined as follows:

$$TI_u = \frac{\sqrt{u'^2}}{\sqrt{U^2 + V^2 + W^2}}, \quad TI_v = \frac{\sqrt{v'^2}}{\sqrt{U^2 + V^2 + W^2}}, \quad TI_w = \frac{\sqrt{w'^2}}{\sqrt{U^2 + V^2 + W^2}}, \quad (1)$$
<table><caption>**Table 2.** Simulation parameters</caption><thead><tr><th>λ</th><th>No. of cells</th><th>No. of faces on one blade</th><th>No<sub>rev</sub></th></tr></thead><tbody><tr><td>0.0</td><td>37.9 × 10<sup>6</sup></td><td>4.0 × 10<sup>5</sup></td><td>0</td></tr><tr><td>2.4</td><td>42.4 × 10<sup>6</sup></td><td>8.2 × 10<sup>5</sup></td><td>500</td></tr><tr><td>5.5</td><td>32.7 × 10<sup>6</sup></td><td>4.5 × 10<sup>5</sup></td><td>800</td></tr></tbody></table>

where $U, V, W$ and $u', v', w'$ stand for the mean and the fluctuation of the components of velocity respectively, according to a reference coordinate system based on the streamwise x-direction. (·) denotes time averaging. To simplify the notation, the longitudinal turbulence intensity $TI_u$ will be denoted as $TI$.

Figure 3 shows the evolution of the turbulence intensity $TI$ in the nondimensional streamwise direction $x/D$. These measurements are carried out before the installation of the wind turbine model. The black vertical dashed line marks the position of the rotor plane.

<figure><img src="image_7.png" /><figcaption>**Figure 3.** Turbulence intensity $TI$ as function of $x/D$. The origin $x/D = 0$ corresponds to the position of the grid.</figcaption></figure>

The measurements allow to check a decay in the turbulent intensity $TI$. When a passive turbulence grid is used, the turbulence generated is divided into two regions: the production region and the decay region. The first, located directly after the grid, is associated with an increase of the turbulence intensity, unlike the decay region. Furthermore, the measurements are rather well approximated by the following power law [13]:

$$TI \sim C \left(\frac{x}{M}\right)^{-n} \qquad (2)$$

$C$ and $n$ are values determined experimentally. Here, 0.7 is found for $n$ and other authors also found this same value [14][15]. The turbulence intensity of 3.8% is obtained at the rotor position with an integral turbulent length scale of 7.4 cm and a Taylor microscale of 0.7 cm.

Figures 4 show longitudinal velocity $U/U_{ref}$ (a) and turbulent intensity $TI$ (b) maps 2.6D downstream of the grid, i.e. 3.6 m from the grid, in the $(y, z)$ plane. The swept section is $1 \times 1$ m$^2$. The area of the rotor disk is marked with a white circle.
<figure><img src="image_8.png" /><figcaption>(b)</figcaption></figure>

<figure><img src="image_9.png" /><figcaption>**Figure 4.** Velocity field $U/U_{ref}$ (a) and turbulent intensity field $TI$ (b) obtain in wind tunnel</figcaption></figure>

In figure 4(a), a velocity gradient is visible with lower velocity $U/U_{ref}$ towards the bottom. This is also the case in the disc area. This is explained by the fact that the section studied faces a flow accelerated by a convergent built on flat ground. However, the scatter remains less than 10%. Concerning figure 4(b), the turbulent intensity $TI$ is uniform in the rotor disk. Indeed, table 3 lists the extreme deviations observed in the rotor disk for the three components of velocity.

<table><caption>**Table 3.** Turbulent intensities $TI$ and velocities ($U, V, W$) according to the 3 components</caption><thead><tr><th></th><th>TI<sub>min</sub></th><th>TI<sub>max</sub></th><th>Velocities</th></tr></thead><tbody><tr><td>Free-stream flow direction</td><td>3.4%</td><td>4.2%</td><td>U&#773; ± 0.1 m/s</td></tr><tr><td>Transverse direction</td><td>3.5%</td><td>3.9%</td><td>0 ± 0.1 m/s</td></tr><tr><td>Vertical direction</td><td>3.7%</td><td>4.0%</td><td>0 ± 0.1 m/s</td></tr></tbody></table>

An overall turbulent intensity of approximately 3.8% is obtained in the three directions. Velocities $V$ and $W$ are about 0 m/s. In addition, the maximum absolute deviation for turbulent intensities and velocities is 10% whatever the direction of the flow. Thus, the measurements are carried out in the decay region, and the rotor plane position will be subject to a shear turbulent flow. It is assumed that the turbulent flow is nearly homogeneous and isotropic on the rotor area.

In order to obtain the same turbulent intensity $TI$ at the rotor plane in the simulation, according to Mishra *et al.* [16], the turbulent intensity $TI$ of 23.2% with a Taylor scale of 0.0031 m are imposed at the inlet of the computational domain. Figure 5 shows longitudinal velocity maps with the rotor, positioned horizontally with zero rotation speed.

The blocking effect due to the presence of the rotor seems to be well captured by the numerical simulation. Indeed, an oval and horizontally elongated velocity $U$ deficit zone corresponds to the presence of the rotor in a stationary position. As a reminder, the mast is not modeled in the simulation.
<figure><img src="image_10.png" /><figcaption>Figure 5. Velocity field $U/U_\infty$ with the rotor at 2.6D downstream of the grid obtained by CFD</figcaption></figure>

## 3.2. Performance of the wind turbine model

### 3.2.1. Aerodynamics at rotor scale
First, the global variables, i.e. the power and torque, are compared. For this purpose, those values are nondimensionalized using the following formulas:

$$C_{power} = \frac{Q\Omega}{\frac{1}{2}\rho U_{\infty}^{3} S_{rotor}} \quad (3) \qquad C_{thrust} = \frac{T}{\frac{1}{2}\rho U_{\infty}^{2} S_{rotor}} \quad (4)$$

where $Q$ is the torque, $T$ the overall drag, $\rho$ the air density, $U_\infty$ the incoming wind velocity, $S_{rotor}$ the rotor swept area and $\Omega$ the rotational velocity of the turbine.

Figures 6 show a comparison of the power coefficient $C_{power}$ (a) and the thrust coefficient $C_{thrust}$ (b) between experimental and numerical results as a function of the tip-speed ratio $\lambda$. For the legend, the labels 'Wi AGR' and 'Wo AGR' stand for with and without AGR meshing respectively. This latter remark will be applied for the rest of the paper.

<figure><img src="image_11.png" /><figcaption>Figure 6. Power coefficient $C_{power}$ (a) and thrust coefficient $C_{thrust}$ (b)</figcaption></figure>

The prediction is satisfactory for the power coefficient $C_{power}$ from $\lambda = 0$ to $\lambda = 5.0$. For the experimental data, the optimal operating point of the turbine is obtained for a tip-speed ratio $\lambda$
equal to 5.6. From this value of $\lambda$, a slight divergence is observed. It corresponds to a maximum power coefficient $C_{power,max}$ equal to 0.338 compared to simulations with $C_{power,max} = 0.363$ at $\lambda_{opt} = 5.8$. The scatter $\Delta C_{power,max}$ is about 7.4% between these last two values. This discrepancy can be explained by the difference in inlet velocities $U_\infty$. Indeed, it is observed that in a wind tunnel, a 10% reduction in $U_\infty$ leads to a reduction in $\lambda_{opt}$ of about 12% and $C_{power,max}$ is smaller and vice versa. Focusing on the simulations, the numerical results with AGR and without AGR are superimposed, meaning that the AGR meshing does not seem to have any significant impact on these global variables for the tested configuration.

Regarding $C_{thrust}$, the prediction deviates more and more from the experimental measurements from $\lambda = 1.2$ but remains acceptable, even for large tip-speed ratios $\lambda$.

### 3.2.2. Aerodynamics at blade scale
First, the flapwise bending moment $M_{fbm}$ on blade roots are plotted. Figure 7 shows the latter as a function of the tip-speed ratio $\lambda$. Only the $M_{fbm}$ for either blade is shown for ease of reading.

<figure><img src="image_12.png" /><figcaption>**Figure 7.** Flapwise bending moment $M_{fbm}$ for two blades</figcaption></figure>

The experimental results are well predicted by the simulation. The greater the rotation speed, the more the $M_{fbm}$ increases. Indeed, the blades undergo increasing aerodynamic and mechanical efforts. The fluctuation for the experimental results generally remains constant. The distribution of mass and inertia along the blades is not uniform because of the implementation of pressure taps and tubes. Slight vibrations have been observed depending on the rotational velocity. This is not taken into account in the simulation and may explain some of the observed discrepancies. The numerical simulation has a maximum relative gap of about 7.1%. However, two areas are notable from $\lambda = 1.2$:

* for $\lambda = [1.2, 4.8]$, the $M_{fbm}$ is underestimated by the simulation

* for $\lambda > 4.8$, the $M_{fbm}$ is overestimated by the simulation.

The AGR mesh does not seem to have any impact on this physical variable either.

### 3.2.3. Aerodynamics at airfoil scale
The pressure measurements make it possible to obtain a chordwise pressure distribution at three different radial positions $r$ and give access to local information on the aerodynamics of the blade profile. In this section, the pressure coefficient $C_p$
distributions are compared. The pressure coefficient $C_p$ is expressed as follows:

$$C_p = \frac{p - p_\infty}{\frac{1}{2}\rho(U_\infty^2 + (\Omega r)^2)} = \frac{p - p_\infty}{\frac{1}{2}\rho W_r^2} \quad (5)$$

with $p$ the local pressure, $p_\infty$ the freestream pressure, and $r$ the radial position.

Figures 8 show the $C_p$ distribution as a function of the radial position $r$ for a low and a high $\lambda$. dashed, continuous, and mixed lines stand for the experimental, the numerical without AGR, and with AGR results respectively. The tip-speed ratios of experimental $\lambda_{exp}$ and numerical $\lambda_{num}$ are specified, as well as the geometrical angle of attack for each section.

$$ (a) \lambda_{num} = 2.4, \lambda_{exp} = 2.2 $$

$$ (b) \lambda_{num} = 5.5, \lambda_{exp} = 5.7 $$

<figure><img src="image_13.png" /><figcaption>Figure 8. Pressure coefficient $C_p$ distribution as a function of three radial positions $r/R$</figcaption></figure>

Numerical and experimental $C_p$ distributions corroborate rather well for the pressure side and show some discrepancies for the suction side. The cases with notable differences are the case with low $\lambda$ for $r = 0.88R$ (figure 8(a) in green) and the case with high $\lambda$ for $r = 0.38R$ (figure 8(b) in dark blue). For low $\lambda$ (a), the numerical and experimental comparison showed the highest variations for the power and trust coefficients. More specifically at $r = 0.88R$, the numerical $C_p$ distribution shape predicts a high suction peak at the leading edge while the experimental does not. At the other two radial positions, the comparison is more satisfactory. If the numerical simulation predicts higher leading edge suction peaks, it suggests that the induced pressure force could be overestimated, leading to a higher power coefficient and bending moment prediction as observed in figure 6 and in figure 7. However, some differences in the pressure gradient evolution can be observed with a gap that increases towards the trailing edge. For example, the numerical simulation predicts a pressure plateau, signature for full separated flows, whereas the experiment does not. The zoomed area highlights that the AGR mesh tends to improve the pressure gradient prediction along the airfoil compared with the experimental one. Therefore, the prediction of the separation location and detached flow region should be improved with AGR. In addition, for this low $\lambda$, the chord-based Reynolds number can be quite low and under 200 000. Reynolds effects that occur may not be so well predicted by the turbulence model chosen for the URANS simulation. For high $\lambda$ (b), close to the operating point that maximizes the power coefficient, the comparison is more satisfactory as a whole. As expected, flows are less subject to separation and Reynolds effects for the resulting flow velocities experienced by the blade sections.
## 4. Conclusion and perspectives

A comparative study between numerical simulations and experiments to predict the aerodynamic performances of a wind turbine rotor is discussed in this paper. $k-\omega$ SST URANS simulation of a complete rotor, with and without AGR method, is performed. Wind tunnel experiments is conducted on a wind turbine model under turbulent inflow conditions. An encouraging agreement between the experimental and numerical values can be seen for physical variables computed at three scale levels, i.e. airfoil, blade, and rotor scale. Slight discrepancies are particularly noticeable when Reynolds effects and separated flows have to be predicted accurately. Indeed, the right assessment of power and thrust coefficients is highly dependent on this. Furthermore, the AGR meshing tends to improve the prediction of flow conditions at the airfoil scale.

Future work will focus on comparison with DDES simulation data. The comparison will ensure the validity of the code for low turbulent inflow conditions and low chord-based Reynolds number before moving on to increasingly realistic turbulence rates and higher Reynolds number. Another aspect of the research will focus on the need to model the velocity gradient measured in the wind tunnel. Taking into account the real inlet field in the wind tunnel will help to further explain the observed differences between numerical and experimental results.

## Acknowledgments

The authors acknowledge the support of the French Agence Nationale de la Recherche (ANR), under grant ANR-19-CE05-0034 (project MOMENTA). The computations is performed using HPC resources from GENCI (Grand Equipement National de Calcul Intensif) (Grant-A0152A00129) which is gratefully acknowledged.

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