Influences of operating parameters on the

Accepted Manuscript Influences of operating parameters on the aerodynamics and aeroacoustics of a horizontal-axis wind turbine

Sanxia Zhang, Kun Luo, Renyu Yuan, Qiang Wang, Jianwen Wang, Liru Zhang, Jianren Fan PII:

S0360-5442(18)31345-8

DOI:

10.1016/j.energy.2018.07.048

Reference:

EGY 13313

To appear in:

Energy

Received Date:

17 January 2018

Accepted Date:

09 July 2018

Please cite this article as: Sanxia Zhang, Kun Luo, Renyu Yuan, Qiang Wang, Jianwen Wang, Liru Zhang, Jianren Fan, Influences of operating parameters on the aerodynamics and aeroacoustics of a horizontal-axis wind turbine, Energy (2018), doi: 10.1016/j.energy.2018.07.048

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ACCEPTED MANUSCRIPT

Influences of operating parameters on the aerodynamics and aeroacoustics of a horizontal-axis wind turbine Sanxia Zhang1, Kun Luo1, Renyu Yuan1, Qiang Wang1, Jianwen Wang2,3, Liru Zhang2,3, Jianren Fan1

1 State Key Laboratory of Clean Energy Utilization, Zhejiang University, Hangzhou 310027, China 2 School of Energy and power Engineering Inner Mongolia University of Technology, Hohhot 010051, China 3 Key Laboratory of Wind and Solar Power Energy Utilization Technology Ministry of Education and Inner Mongolia Construction, Hohhot 010051, China

Submitted to

Energy

Corresponding author: [email protected]; Tel/Fax: 86-571-87951764



ACCEPTED MANUSCRIPT Abstract A computational framework used to evaluate the aerodynamics and aeroacoustics is developed and validated against the experimental data in the previous work. In the present work, the different operating parameters of inlet flow velocity, tip speed ratio and turbulence intensity have been considered separately. The aerodynamic performance, the vortex dynamics and the aerodynamic acoustics of the full scale horizontal-axis wind turbine under different operating conditions have been investigated. And the analysis of the impact of different operating parameters is discussed. It is observed that the model has an extension to different conditions and it is sensitive and accurate for simulating the results of different condition parameters. According to the results, the wind with lower turbulence intensity will be better for the operating, and the wind turbine operation can be optimized by adjusting the rotating speed (TSR) according to the inflow wind velocity. In the end, a noise and power trade-off graphics has been proposed based on the wind turbine acoustics and performance results. With enough operating conditions available for reference, selecting the optimal operating parameters under specific operating conditions according to the noise and power trade-offs graphics becomes feasible. Keywords: Large-eddy simulation, aerodynamics, aeroacoustics, operating parameters, horizontal-axis wind turbine.

ACCEPTED MANUSCRIPT Highlights • LES and FW-H method has been employed for aerodynamics and aeroacoustics analysis. • The research is conducted with a combination of simulations and experiments method. • Three effective operating parameters have been studied. • Trade-off between power and noise has been investigated.

Nomenclatures Variables CP power coefficient CT thrust coefficient D wind turbine rotor diameter [m] P pressure [Pa] or power [W] T thrust force [N] U inflow velocity [m/s] λ tip speed ratio I turbulent intensity X downstream distance [m] R radial distance [m] CPr pressure coefficient Cf wall fraction coefficient Y y-axis coordinate value Nomenclatures C chord length [m] L blade edge length [m]

Abbreviations CFD computational fluid dynamics LES large-eddy simulation RANS Reynolds-Average Navier Stokes URANS Unsteady Reynolds-averaged Naviere-Stokes HAWT Horizontal Axis Wind Turbine PIV particle image velocity TSR tip speed ratio DES detached eddy simulation FW-H Ffowcs Williams-Hawkings SPL sound pressure level

ACCEPTED MANUSCRIPT 1. Introduction Energy is a vital input for social and economic development in any country including China. With the agricultural and industrial activities in the country increasing, the demand for energy is also increasing. On the other hand, it is observed that there are growing concerns about global energy demand and environmental pollution [1]. In a positive and responsible attitude, China is actively fulfilling its obligations under the international environmental conventions. For many important international environmental conventions, China has formulated a proactive and actionable plan of action. According to the “13th Five-Year Energy Development Plan” in China [2], the proportion of non-fossil energy consumption will be increased to more than 15% during the plan from 2016 to 2020. In that regard, wind energy will play a significant role in the near future [3]. Wind energy has been developing rapidly in China, and the cumulative installed capacity of wind power in China exceeded 168 GW in 2016 according to the statistics [2]. China plans to speed up the development of wind power in the eastern and southern regions, where there is a need to improve low-speed wind turbine technology and micro-site selection level to promote decentralized wind power construction. With the growing interest in decentralized wind power construction, small-scale wind turbines are an interesting option for household and small firms. To make such an investment more efficient, it is crucial for the wind turbine working conditions to be favorable. Therefore there is a need to research the operating characteristics under variable operating conditions and optimize the operating parameters of the wind turbine [4]. And the establishment of a cost-effective model to study the influence of parameters is needed. In addition, the noise is another vital problem that should be considered [5]. Since the sites are often located near rural areas, conflicts with the recreational life of people nearby often occur [6]. Noise is an annoyance factor at wind farms and hence there is a need for reliable methods to solve the problem [7]. It is time consuming and difficult to conduct field measurement that contain a large variety of metrological problems [8]. Therefore it is important to develop good prediction models and reliable methods for efficiency improvement and noise reduction of the wind turbine [9, 10]. The mainly noise source from modern wind turbine is aerodynamic, which has a broadband sound spectrum and increases when blade tip rotation reaches higher speed [11].

ACCEPTED MANUSCRIPT Large Eddy Simulation (LES) method has superiority compared with RANS (Reynolds-Average Navier Stokes) except for computationally expensive for industry applications. LES method resolves the large scales directly, which contain the most energy, and the behavior of small scales eddies is simply modeled. Sedaghatizadeh et al. [12] develop a numerical model of wind turbine wakes using LES method. Their results show that the LES model can produce detailed information about the flow field, which can be used to design a wind farm in terms of power production and maintenance cost. While the semi-empirical engineering models may have restrictions due to the assumptions they based on when applying to real conditions, such as different wind profiles and incoming turbulence. Amin Allah and Shafiei Mayam [13] carried out a LES simulation to study the flow field in a single wind turbine and in two in-line wind turbines. They aim to reveal the effect of turbulent structures on wind turbine performance, and furthermore it is concluded that wind turbine efficiency is increased by 4% in the counter-rotating configuration. In addition to the numerical work, some experimental work also has been completed. Li et al. [14-19] made a series of experimental studies on the Horizontal Axis Wind Turbine (HAWT) flow field visualization. The turbulent wake characteristics [15] and the power characteristics [14] of the HAWT has been presented to provide a better understanding of the wind turbine wake. However, the disadvantage of the study is that the measurements are two-dimensional. The pressure distribution acting on a single blade surface [16] has also been investigated, with a disadvantage of static measurement. Later, the power coefficiens [17] and the wind velocity distribution in the horizontal axis [18] and the vertical axis [19] direction was investigated in the three-dimensional dynamic field experiments. However, the authors pointed out that evaluating these measurements with wake model simulations is very important. It is difficult to assess the entire field and measure certain dynamic parameters only by experiments. Combining numerical and experimental approaches can help to improve accuracy in wind turbine performance prediction and facilitate the design of wind turbine blades [20]. As for noise, Cotté [21] modeled wind turbine noise emission and propagation in an inhomogeneous atmosphere, and the trailing edge noise and turbulent inflow noise generation mechanisms are considered. The validity of the point source approximation (often used in noise propagation models) is finally assessed. Maizi et al. [22] investigated the blade tip shape effect on the noise emission from horizontal axis wind turbine, and three different tip blade configurations are

ACCEPTED MANUSCRIPT tested by using the Unsteady Reynolds-averaged Naviere-Stokes (URANS) and Detached Eddy Simulation (DES). The Ffowcs Williams-Hawkings (FW-H) analogy is used to predict the aeroacoustics characteristics, and the results indicated that the specific tip shape has a significant influence on the noise emission, particularly at the high frequency range. Ghasemian and Nejat [23] carried out numerical prediction for aerodynamic noise radiated from a vertical-axis wind turbine, and LES method is conducted and the noise prediction is performed by the FW-H acoustic analogy formulation. The results indicate that the dominant noise sources are the combination of thickness and loading noise at those frequencies, while the quadrupole noise has negligible influence on the tonal noise. And there is a direct relation between the rotational speed and the radiated noise strength. Similarly, in addition to the simulation work, there are also some experimental works on noise have been done. Lee and Lee [24] predicted and measured the aerodynamic noise from a 10 kW wind turbine, and the measurement is carried out at a reference position with free-field microphones. They found the trailing edge bluntness noise can be a dominant noise source for small wind turbines unless the blades have very sharp trailing edges. SolísGallego et al. [25] carried out a numerical prediction of the wind turbine airfoil trailing edge noise using Curle's surface approach and FW-H volumetric analogy. The aeroacoustic results were validated with experimental measurements, which are carried out in a wind tunnel using a frequency analyzer. The results indicate that the FW-H method shows a better agreement with the experiments, especially in the range of trailing edge frequencies. The subject of improving the performance of wind turbines has become a hot topic in wind energy community. Researchers strive to achieve performance improvements of wind turbines by implementation of various concepts [26]. An advanced topic related to aerodynamic performance of wind turbine rotor has been considered by Sedaghat et al. [27]. The authors indicate that the study can be extended to achieve a more comprehensive and desirable optimal blades when experimental data becomes available. The optimization of aerodynamic performance and acoustic noise of airfoils can be found in research conducted by Göçmen and Özordem [28]. Their results show that the airfoils after geometrical optimization have higher lift to drag ratios and lower noise emission levels. The aerodynamic and aeroacoustic optimization also has been studied by Kaviani and Nejat [29]. The Multi-Objective Particle Swarm Optimization algorithm with relatively

ACCEPTED MANUSCRIPT small computing cost has been proposed. It is indicated that the CFD (computational fluid dynamics) method solves the three-dimensional, nonlinear, and complex turbulence flow field more accurately. In order to predict the full-scale, three-dimensional dynamic rotating near wake under turbulent inflow conditions efficiently, the LES approach has been employed. Then the aeroacoustic result is carried out with the FW-H acoustic analogy formulation. The effective model has been proposed and both the aerodynamics and aeroacoustics results have been validated against the experimental data to verify the accuracy of the model in the previous work [30]. There are variations of the angle of attack, the inlet flow velocity and turbulence intensity in the actual wind turbine operation, therefore the full scale analysis of the influences of the different operating condition parameters on the aeroacoustics and aerodynamics is vital for wind turbine optimization. In the present work, different turbulent inflow conditions have been considered and the characteristics of the variable operating parameters have been analyzed. Then the noise and power trade-offs has been proposed. With enough operating conditions available for reference, selecting the optimal operating parameters under specific operating conditions according to the noise and power trade-offs graphics becomes feasible.

2. Experimental and computational cases 2.1 Experimental and computational configuration The experiment is conducted in the open section of the wind tunnel in the Key Laboratory of Renewable Energy in Inner Mongolia University of Technology. The B1/K2 wind tunnel is used in the experiment, together with the NACA4415 horizontal-axis wind turbine and the CCD camera. The diameter of the open test section of the wind tunnel is 2.04m. The wind turbine blade has a diameter of D=1.4m. The tail is removed in order to achieve stable experimental conditions. The flow configuration and computational domain are established exactly the same as the experimental structure to simulate the wind turbine case as real as possible, as shown in Fig.1. The coordinate origin is in the middle of the blade rotational plane. The wind turbine is placed in the middle of the horizontal line of the cross section and 0.78m

ACCEPTED MANUSCRIPT from the exit of the wind tunnel. The wind turbine hub height is 1.71m. The wall, the roof and the ground of the test section are set to be the wall boundary condition.

Fig. 1 Test section and computational domain

2.2 Acoustic radiation measurement in the rated condition Acoustic analysis requires detailed records of sound information, and MP201 microphone is applied in the experiment. Background noise is measured before the experiment to remove its influence. The experiment was performed under rated conditions. The rated wind speed is 8m/s and the rated tip-speed ratio λ is 5 in this case. The distribution of the experimental data point used in the acoustic results comparison section is shown in Fig.2.

Fig.2 Experimental data point distribution for noise measurement

2.3 Models of different inflow conditions The current simulations take into account the influence of three different parameters: inflow velocity, tip speed ratio, and turbulent intensity. And each

ACCEPTED MANUSCRIPT parameter takes three different values. The details of the conditions are shown in Table 1 as below.

Table 1．Models of different inflow conditions

Model U1

U=5

λ=5

I=4%

Model U2

U=8

λ=5

I=4%

Model U3

U=11

λ=5

I=4%

Model λ1

U=8

λ=4

I=4%

Model λ2

U=8

λ=5

I=4%

Model λ3

U=8

λ=6

I=4%

Model I1

U=8

λ=5

I=0.4%

Model I2

U=8

λ=5

I=4%

Model I3

U=8

λ=5

I=10%

In the table, U represents for the inlet flow velocity (m/s), λ represents for the blade tip speed ratio, and I represents for the turbulence intensity. In each model, there is only one parameter that is different from the rated condition. It can be seen that the U2, λ2, and I2 condition is the same rated condition. 3. The mathematical models and numerical details The LES approach developed in the previous study [30] is applied in the present work, and the Ffowcs Williams and Hawkings Model is utilized for the aeroacoustics calculation. The flow around and downstream the wind turbines, even in the largest inlet velocity and tip speed ratio case, is still essentially incompressible. Therefore an incompressible Navier Stokes solver is applied and the LES Smagorinsky-Lilly model is used for the current simulations. . 3.1 The LES approach formula

Numbering

ACCEPTED MANUSCRIPT   x      x '  G  x, x '  dx '

(1)

ρ   (ρu i )  0 t x i

(2)

σij    p τij (ρu i )  (ρu i u j )  (μ )  t x j x j x j x j x j

(3)

 u u j  2 u l σij  μ( i  )  μ δij  x j x i  3 x l

(4)

τij  ρu i u j  ρ u i u j

(5)

1 τij  τ kk δij  2μ t Sij 3

(6)

1 u u j Sij  ( i  ) 2 x j x i

(7)

μ t  ρL2s S

(8)

d

1

Ls  min(κd, Cs V 3 )

(9)

In LES model, large eddies are resolved directly, while small eddies are modeled. Filtering the time-dependent Navier-Stokes equations in either wave-number space or physical space can get the governing equations employed for LES. A filtered variable (denoted by an overbar) is defined by Eq.1. Where G is the filter function that determines the scale of the resolved eddies and d is the fluid domain. For incompressible flows, one obtains Eq.2 and Eq.3 after filtering the NavierStokes equations. Where σij is the stress tensor due to molecular viscosity defined by Eq.4, and τij is the subgrid-scale stress defined by Eq.5. The subgrid-scale stresses resulting from the filtering operation can be modeled as Eq.6. Where μt is the subgridscale turbulent viscosity. The isotropic part of the subgrid-scale stresses τkk is added to the filtered static pressure term. Sij is the rate-of-strain tensor for the resolved scale defined by Eq.7. In the Smagorinsky-Lilly model, the eddy-viscosity is modeled by Eq.8, where

S  2Sij Sij . Lsis the mixing length for subgrid scales and it is computed using Eq.9.

ACCEPTED MANUSCRIPT Where d is the distance to the closest wall, κ is the von Kármán constant, V is the volume of the computational cell and Csis the Smagorinsky constant. 3.2 The Ffowcs Williams and Hawkings Model formula

Numbering

1  2 p' 2  2 '  Pijn j  ρu i  u n  v n   δ  f    p  TijH  f    2 2 a 0 t x ix j x i 





  ρ0 v n  ρ  u n  v n   δ  f  t 





(10)

Tij   u i u j  Pij  a 02     0  δij

(11)

 u u j 2 u k  Pij  pδij  μ  i   δij   x j x i 3 x k 

(12)

   p( x,t )  pT ( x,t )  pL ( x,t )

(13)

 4 pT ( x,t )  

  0U n rM r  a0 ( M r - M 2 )   0 U n  U n   dS     dS  2  3 f 0 f 0 r 2 1  M r   r 1  M r    

 1 4 pL ( x,t )  a0 1  a0

Ui  vi 



   L L  Lr M dS    2 r    dS 2 2 f 0 f 0  r 1  M r    r 1  M r  

 Lr rM r  a0 ( M r - M 2 )  dS 3  f 0  2 r 1  M    r 

 (u - v ) 0 i i

Li  Pijnˆ j   u i  u n  v n 

 t

r a0

(14)

(15)

(16)

(17)

(18)

The FW-H equation is essentially an inhomogeneous wave equation that can be derived by manipulating the continuity equation and the Navier-Stokes equations. The FW-H equation can be written as Eq.10, where δ(f) = Dirac delta function H(f) = Heaviside function

ACCEPTED MANUSCRIPT a0= the far-field sound speed

p' = the sound pressure at the far field ( p' = p - p0) Tij = the Lighthill stress tensor (defined as Eq.11) Pij = the compressive stress tensor (For a Stokesian fluid, this is given by Eq.12) ni = the unit normal vector pointing toward the exterior region(f > 0) un = fluid velocity component normal to the surface f = 0 ui =fluid velocity component in the xi direction vn = surface velocity component normal to the surface vi = surface velocity components in the xi direction, The solution to Eq.10 is obtained using the free-space Green function (δ(g)/4πr). The complete solution consists of surface integrals and volume integrals. The surface integrals represent the contributions from dipole and monopole acoustic sources and partially from quadrupole sources. The volume integrals represent quadrupole (volume) sources in the region outside the source surface. In the present wind turbine cases, the source surface encloses the source region and the flow is low subsonic. The contribution of the volume integrals becomes small, so the volume integrals are     dropped. Thus, we have Eq.13~17. pL ( x,t ) and pT ( x,t ) are referred to as loading terms and thickness, respectively. The square brackets in Eq.14 and Eq.15 denote that the kernels of the integrals are computed at the corresponding retarded times(τ), defined as Eq.18. where t is the observer time, and r is the distance to the observer. The various subscripted quantities appearing in Equations (14) and (15) are the inner products of a vector and unit vector implied by the subscript. E.g.,     Lr  L  rˆ  Li ri and U n  U  n  U i ni , where n and r denote the wall-normal directions and the unit vectors in the radiation, respectively. 3.3 Numerical details In order to approach the experimental inlet conditions, the flow velocity of the

ACCEPTED MANUSCRIPT inlet plane is described by U in  U c 

U j  Uc 2

(tanh(

yH 2

2 )  tanh(

yH 2

2 ))

(19)

where H is the inlet diameter of 2.04 m. Uc and Uj represent the co-flow and the mean inflow velocity respectively. δ = 0.05H. The speed is set to be zero when y is greater than H/2. Furthermore, the turbulence intensity is imposed on the inflow according to the different cases. The computational domain of about 2.86 million cells remains the same. It contains the two parts: the internal rotating field and the external relatively stationary flow field. And the interfaces are set to transfer data between the rotational and stationary parts. The quality of the mesh in each case has been checked and meets the needs of the LES calculation. It has also been verified that 2.86 million cells are enough for the present LES simulation. 4. Results and analysis The results and analysis part of this paper is organized as follows: In Section 4.1, the wind turbine aerodynamic performance is presented, including the power and thrust coefficients and the wall shear stress and pressure coefficient distribution. The aeroacoustic noise results are shown in Section 4.2. It also contains two parts, the vortex dynamics and the aeroacoustic noise. The vortex dynamics is added because of the relationship between vortex and aeroacoustics. Finally, the trade-offs between the wind turbine performance and acoustic noise has been proposed based on Section 4.1 and Section 4.2. The result is presented in Section 4.3. 4.1 Wind turbine performance 4.1.1 Power and thrust coefficients Fig. 3 presents the wind turbine performance in terms of power and thrust coefficients, which is obtained by LES simulation under different operating conditions. The power and thrust coefficients are defined by Eqs. 20 and 21, respectively.

CP 

2P U 3 R 2

(20)

ACCEPTED MANUSCRIPT

CT 

2T U 2 R 2

(21)

where P represents the rotor power and T is the thrust force of the rotor. Fig. 3(a) shows different wind turbine performances with three different velocity inlet flow conditions. Both the power and thrust coefficients are elevated very slightly with the increase of the inlet flow velocity. It seems that there is not much change overall. According to the Blade Element Momentum theory, as long as the tip speed ratio unchanged, the rotor's power factor remains unchanged in the ideal situation. But in the real three-dimensional model, each section of the blade has a twist and the flow can be complicated. Thus tiny changes show in the results. We can know that the rotor's power has different values, which shows that the more wind power, the more rotor power within a certain range. However the more rotor power will cause the larger load. Fig. 3(b) shows different wind turbine performances with three different TSR conditions. The literature about the experiments of the airfoil [14, 15] can be used as a reference. Their research shows that the power coefficient is affected by the tip speed ratio, and the thrust coefficient increased with the increase of the tip speed ratio. In the present research, it can be observed that the thrust coefficient increases with the improvement of TSR, which means that with the same inlet flow velocity, the thrust force enlarges when the rotor rotation speed increases. Therefore, the higher TSR will cause the rotor bearings to withstand greater aerodynamic loads, and it will affect the durability and operation cost of the wind turbine. On the other hand, with the increases of TSR, the power coefficient increases and reaches the maximum value at the design tip speed ratio (TSR=5) and then it decreases. With different TSRs, the angle of attack of the airfoil is not the same, so it will affect the power factor of the blade rotor. Fig. 3(c) shows different wind turbine performances with three different turbulence intensities. It can be seen that the power factor is almost constant when the turbulence intensity changes. The power coefficient and thrust coefficient are the aerodynamic characteristics of the wind turbine, and they are mainly related to the tip speed ratio and the blade solidity. However the change in inflow turbulence intensity is not directly related to them.

ACCEPTED MANUSCRIPT

(a)

(b)

(C)

Fig. 3 Power and thrust coefficients

4.1.2 Wall shear stress and pressure coefficient distribution The aerodynamics of wind turbine concerns modeling and prediction of the aerodynamic forces on the solid wind turbine structure and in particular on the rotor blades [31]. Fig. 4 is a diagrammatic drawing shows the mean wall shear stress distribution of the wind turbine in the rated condition. The section that marked 1 represents the tip section of the blade. Similarly, section 2 is the suction side of the blade and section 3 is the pressure side of the blade. In the right side there is a partial enlarged view that is presented by looking straightly in the direction of the arrow. It can be seen from the tip section that the maximum stress appears at the very tip of the windward side, and then there is a second peak value appears near the trailing edge of

ACCEPTED MANUSCRIPT the suction side. They are the parts that prone to be damaged first. In section 2 and section 3, it can be seen that the stress is mainly concentrated in the leading edge, and the large value appears near the tip of the blade. The distribution of stress on the surface of the blade depends mainly on the airfoil structure. In this study, the thick airfoil NACA4415 is used, and the stress concentration part is usually located at the leading edge of the airfoil. To quantitatively describe this, the details are shown in the Fig. 5 and Fig. 6 below.

Fig. 4 Mean wall shear stress distribution of the blade in the rated condition

The variation of pressure coefficient along the chord direction on the front and back section edge of the blade is shown in Fig. 5. The section is 1/10 radius away from the blade tip. The curve is drawn from the value of the grid point on the blade, so it’s not smooth enough. The point 0 represents the point on the leading edge and the end point represents the point on the trailing edge. And C represents for the chord length of the section. It can be observed that the negative pressure appears at the suction side and has the larger value. The maximum force generated by the wind turbine blade is near the leading edge of the blade. This is because the wind wheel rotates clockwise, and the leading edge portion of the blade is first blocked by air, and the airflow speed is rapidly slowed down. According to Bernoulli's equation, the pressure generated at this portion is relatively large. Flow separation occurs at the trailing edge of the blade, forming a vortex zone. And the pressure is reduced relatively quickly, causing the pressure difference at the leading edge position to be greater than the pressure difference at the trailing edge. The three curves coincide in Fig. 5(a) and Fig. 5(c), the pressure coefficient does not change with the turbulence intensity within a certain range [32, 33]. However, there is a certain law of change in Fig. 5(b). It has different TSRs, which means that the angle of attack is different. The

ACCEPTED MANUSCRIPT transfer point of the positive and negative pressure moves with the angle of attack changes. It can be seen clearly that the ratio of the rotating blade tip speed and the undisturbed wind velocity at hub height is a very important parameter to be decided when operating the wind turbine. And it is shown that the simulation model developed in the present work can simulate the actual situation in condition of the variation of angle of attack.

(a)

(b)

(c) Fig. 5 Pressure coefficient along the chord line at 1/10 blade length

Fig. 6 shows the distribution of the mean wall shear stress and wall friction coefficient along the blade edge of the windward side. In the figures the point 0 represents the point of the blade tip. It can be observed that the mean wall shear stress increases from blade root to blade tip because that the wall velocity gradient increases from root to top, and it has a maximum value near the tip. Near the root of the blade there is a small bulge. It means that there is a small area of increased wall velocity gradient near the blade root, which is caused by the rotation effect of the blade root

ACCEPTED MANUSCRIPT vortex. In the blade tip area there also have small bulges, which can be effected by the blade tip vortex that resulting in wall velocity gradient fluctuations. It is observed that in Figs. 6(a), (b) and (c) the blade edge mean wall shear stress increases with the inlet flow velocity and the TSR increases, while the variety is not obvious with the change of the turbulence intensity. The distribution of wall friction coefficient has the same pattern of change with the mean wall shear stress except in Fig. 6(d). Comparing Fig. 6(a) and Fig. 6(d) it can be seen that when the inlet flow velocity reaches 11m/s, the wall friction coefficient no longer increases but decreases.

(a)

(b)

(c)

(d)

ACCEPTED MANUSCRIPT

(e)

(f)

Fig. 6 Mean wall shear stress and wall friction coefficient along the blade edge

4.2 Aeroacoustics results 4.2.1 Vortex dynamics The aerodynamic characteristics of the near wake flow field and the vortex structure in it are inseparable. The air flow pattern in blade rotation region is very complex, but the analysis of the vortex characteristic is necessary. Fig. 7 shows the different vortices in the wind turbine flow fields identified by λ2 criteria. And the vortices are presented under the same λ2 value. Turbulent vortex structures of the flow field for three inlet flow velocities U= 5,8,11 m/s are visualized in Fig. 7(a). The λ2-criterion isosurface colored by the values of the velocity magnitude is shown. It is observed that finer vortex structures are identified with the inlet velocity increases. According to the third law of motion, the vortex rotate in the opposite direction of the rotor, which is easily illustrated by showing the wake swirls behind the turbine. Wake vortices are part of the kinetic energy which cannot be recovered downstream to the undisturbed wind stream. Higher inlet flow velocity results to higher power when the TSR is the same. Higher torque, in return, results in intensified wake turbulence [13]. The same situation is shown in Fig. 7(b) and Fig. 7(c). With the increase of tip speed ratio, the rotating speed increases, and the wind turbine interact more strongly with the fluids, then the vortex shedding speed is increased and the vortex structure is more complicated. The increase in turbulence intensity also exacerbates the flow fluctuation around the wind turbine, and small-scale vortex structures can be observed. The details of the vortex are shown in Fig. 8 and Fig. 9 below.

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Fig. 7 Vortex structures under the same λ2 value

ACCEPTED MANUSCRIPT Fig. 8 shows the integral vortices in the near wake colored by the value of the velocity magnitude, which contains the tip vortex, the attached vortex, the center vortex and the vortex behind the tower. The vortex structure is a result for visual observation under the rated condition. The aerodynamic characteristic of the wind turbine is characterized by the near-wake structure and the near-wake structure is characterized by the tip vortex. Fig. 8 is a diagrammatic sketch clearly shows several circles of tip vortex. And the vortex core position in the flow direction is presented in Fig. 9.

Fig. 8 Vortex structure in the rated condition

In Fig. 9 the X represents for the distance in the flow direction, and the R represents for the distance in the radial direction. The symbol point represents for the vortex core. It is observed that in Fig. 9(a), the position of the vortex core is basically the same at different speeds. This is the result of a combination of different rotation speeds and inlet flow velocities. Higher rotation speed will produce more circles and higher inlet flow velocity will speed up the movement of the vortex in the flow direction. In Fig. 9(b), it can be seen that the TSR has a great influence in the position of vortex core. With the TSR increases, the rotating speed of the wind turbine increases, and the vortex shedding frequency also increases, however the inflow wind speed does not change, then the velocity of the vortex moving to the downstream is basically unchanged. Therefore, in the larger TSR case, the axial spacing of two

ACCEPTED MANUSCRIPT adjacent vortex cores is reduced. And the vortex core expands in the radial direction. Fig. 9(c) shows that the turbulence intensity also has little effect in the tip vortex movement. The vortex core just moves a little further with the increase of the turbulence intensity.

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Fig. 9 Tip vortex core position under different conditions

4.2.2 Aeroacoustic noise The acoustic noise results are presented and discussed in this section. The threedimensional parameter necessary for the acoustic computation is obtained from the flow field calculation. Fig. 10 shows the SPL (sound pressure level) value along the line downstream the blade tip in the flow direction. The distribution of the SPL value under different conditions has been presented together with the experiment result of the rated condition (U2, λ2, I2). The error between the simulation and experiment results in the rated condition is within 10% and may be caused by the error of both the experiment and the simulation. In the simulation part, the highly dynamic turbulent flow near the blade tip area is complicate and can lead to calculation errors and

ACCEPTED MANUSCRIPT uncertainties of the dynamic velocity and pressure. Then result in the calculation error of acoustic results. In the experiment part, the experimental errors such as the error of the instrument and the error of the rotation velocity fluctuation can lead to the result error. In Fig. 10(a) it is shown that the higher inlet flow velocity case has the higher SPL value because the blade tip speed is larger. Fig. 10(b) also shows that the sound source increases with the TSR increases, because larger TSR leads to higher blade tip speed. However in the model λ1 the lower TSR causes the slower attenuation of the sound. This is related to the propagation and dissipation rate of the vortex. In Fig. 9(b) it is observed that the vortex spread further downstream when TSR is lower. So the SPL remains at a large value in the downstream. It also proves the relationship between vortex and aerodynamic noise. In Fig. 10(c) it is shown that the turbulence intensity does not affect the sound pressure level at the sound source, but it affects the sound attenuation in the flow direction. The higher turbulence intensity causes the slower attenuation of the sound, which is also consistent to the motion of the vortex core in Fig. 9(c). Fig. 11 shows the SPL value in the line vertical to the flow direction and 0.4 m downstream the blade tip. The distribution of the experimental point arrangement has been presented in Fig. 2. Similar curve shape of the cases has been observed, and the comparison sort by numerical values is consistent with Fig.10. In general, model λ3 gets the maximum sound pressure level, while the model U1 gets the minimum sound pressure level.

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(c) Fig. 10 SPL value along the line downstream the blade tip

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(c) Fig. 11 SPL distribution in the line vertical to the flow direction

Fig. 12 shows the SPL distribution in the horizontal line parallel to the y axis that

ACCEPTED MANUSCRIPT goes throw the center which is 2.5D (D is the diameter of the wind turbine rotational plane) downstream the wind turbine. Y is the y-axis coordinate value and the length of Y is 3D. Fig. 12 shows the results after development from Fig. 11 along the flow direction. The results in Fig. 12 represent the acoustic values in the downstream 2.5D away from the rotation plane, where the sound is slightly lower. The different noise distribution patterns in Fig. 12(b) can be observed and inferred from Fig. 10 and Fig. 11. The sound attenuation is slower in the low TSR condition, so the SPL value in the model λ1 is not the minimum in Fig. 12(b).

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(c) Fig. 12 SPL distribution in the horizontal line of middle section

The acoustic propagation is of course three-dimensional, since the threedimensional free space green function is used in the FW–H integral formulation. The contour line schematic diagram of the SPL in the region of near wake next to the wind turbine is shown in Fig. 13. Each model shows similar three-dimensional directional

ACCEPTED MANUSCRIPT characteristics, therefore the results in the rated condition is shown in Fig. 13 as a representative. The spatial distribution of the acoustic characteristic has been presented and it can be seen that the acoustic distribution has its directivities. In the rotational plane the sound diverge evenly along the radial direction. However, as the SPL is larger near the blade tip, when diverge in the flow direction, the sound spread further in the near tip direction.

Fig. 13 Three-dimensional contour line distribution of SPL in rated condition

4.3 Trade-off analysis between performance and noise Achieving the goal of high power output and low noise emission causes a lot of research interests[10, 28, 29, 34] and benefits to the actual operation. Fig. 14 shows the performance of sound pressure level SPL and power coefficient Cp. The SPL value presents in the figures is the average value in the vertical plane that is 2.5D away from the blade rotational plane. It is observed that the power coefficient keeps almost the same in the different inlet flow velocities and turbulence intensities situations when the tip speed ratio remains the same. But the acoustic noise increases with the increase of the inlet flow velocity and turbulence intensity. In the different inlet flow velocity condition, while the inflow velocity increases, there is little change in the power coefficient, but the noise increases by 72.3% and 117.6%, respectively. This is a way to get a high power factor at low wind speeds. In the different TSR conditions, compared with the rated condition (λ=5), the power coefficient decreases and the acoustic noise increases in the other two conditions. And the condition with the highest TSR is the worst condition. Our future work will extend the table to the entire operating conditions of the wind turbine to guide the operation and regulation of wind

ACCEPTED MANUSCRIPT turbines. Selecting the optimal operating parameters refer to the diagram reasonably under specific operating conditions becomes feasible. Overall, the wind with lower turbulence intensity will be better for the operating of wind turbine, and the wind turbine operation can be optimized by adjusting the rotating speed (TSR) within the inflow wind velocity.

Fig. 14 Sound pressure level against power coefficient

5. Conclusion In order to predict the full-scale, three-dimensional dynamic rotating near wake of the horizontal-axis wind turbine under turbulent inflow conditions efficiently, the LES approach has been employed. Then the aeroacoustic result is obtained with the FW-H acoustic analogy formulation and has been compared with the experimental test. There are variations of the angle of attack, the inlet flow velocity and turbulence intensity in the actual wind turbine operation, therefore the full scale analysis of the influences of the different operating condition parameters on the aeroacoustics and aerodynamics is vital for wind turbine optimization. In the present work, different operating parameters of inlet flow velocity, tip speed ratio and turbulence intensity conditions have been considered separately and the characteristics of the variable operating parameters has been analyzed. The aerodynamics and aeroacoustics characteristics of the wind turbine have been

ACCEPTED MANUSCRIPT investigated. Then the noise and power trade-offs has been proposed based on the wind turbine acoustics and performance results. It is observed that the model is sensitive and accurate for simulating the results of different condition parameters such as the inlet velocity, the angle of attack and the turbulence intensity. In the different inlet flow velocity cases, the results show that the inlet velocity changes along when TSR remains 5 has almost no effect on power and thrust coefficient and the pressure coefficient. However, the higher inlet velocity leads to higher the acoustic noise. In the different TSR cases, the results show that the change of TSR has a great impact on the performance and the movement of the vortex core region of the wind turbine. And it affects both the acoustic noise source and the noise attenuation. The best power coefficient appears when TSR=5. In the different inlet flow turbulence intensity cases, the results show that the effect of turbulence intensity on the performance will be slightly smaller contrast with the other two parameters. However, it affects the attenuation of the vortex and the acoustic noise. According to the above results, the wind with lower turbulence intensity will be better for the operating, and the wind turbine operation can be optimized by adjusting the rotating speed (TSR) according to the inflow wind velocity. When enough operating conditions are available for reference, the simulation results will have reference values and selecting the optimal operating parameters under specific operating conditions according to the noise and power trade-offs graphics becomes feasible. The advantage of this study is that both the aerodynamics and the aeroacoustics have been investigated in one model with high precision and the characteristics of variable parameters have been studied. In addition, the research has experimental support. The disadvantage is that a large amount of calculation is needed to form the complete optimization graph for reference. Therefore, in our future research it is also very important to presenting new, cost-effective optimization methods. Acknowledgements This work is financially supported by the Inner Mongolia Autonomous Region Open Major Basic Research Project (Grant No. 20120905) and the National Natural Science Foundation of China (Grant No. 51366010). We are grateful to that.

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