an efficient noise modeling tool for wind tur- · pdf filebines including sound propagation in...

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AN EFFICIENT NOISE MODELING TOOL FOR WIND TUR-BINES INCLUDING SOUND PROPAGATION IN ARBITRARY WEATHER CONDITIONS

Sterling McBride and Ricardo Burdisso

Virginia Tech – Department of Mechanical Engineering, Durham Hall, Blacksburg, Virginia 24060

email: [email protected]

José David Parra

Universidad Austral de Chile – Instituto de Acústica, General Lagos 2086, Valdivia, Chile

Wind energy is the world´s fastest-growing renewable energy source. Thus, the number of people

exposed to wind farm noise is increasing. Due to its broadband amplitude modulated characteris-

tic, wind turbine noise (WTN) is more annoying than noise produced by other common commu-

nity/industrial sources. Aerodynamic noise on the blades is the dominant noise source of modern

large wind turbines. Physically accurate methods for the prediction of acoustic noise produced

by wind turbines and farms are crucial for their environmental impact assessment, including their

amplitude modulation behavior. The state-of-the-art approach to model the aerodynamic noise

from wind turbines is to divide the blades into a number of radial segments. A noise source is then

associated to each element. From the wind profile, blade geometry and aerodynamic parameters

(e.g. angle of attack, etc.), the strength and directivity of each of the sources are estimated. Finally,

the noise from each source is coupled to a propagation code to account for weather conditions.

This process has to be performed at each angular position of the blades as it completes a full

rotation. This approach results in hundreds of noise sources needed to model a single turbine.

The result of this process is extremely computationally intensive calculations, unfeasible to model

realistic wind farms. We propose a novel method for modeling wind turbine's noise and its atmos-

pheric propagation. The approach consists of computing an equivalent noise source placed at the

turbine hub with a strength and directivity that is a function of the rotor angular position. This

single equivalent source is then coupled to a curved ray tracing propagation code. The approach

is demonstrated for a 5MW modern wind turbine over a flat acoustically soft terrain. The results

show the proposed modeling approach to be computationally efficient and accurate.

1. Introduction

Power production by renewable wind energy has risen sharply and worldwide in the last 20 years.

This energy source has the highest growth rate of all renewable sources at >20% increase of installa-

tions annually. Most of the onshore wind turbines are subjected to noise constraints and ever more

stringent regulations. Additionally, wind turbines are continuously increasing in size to achieve

higher outputs per unit for profitability and efficiency. Thus, these larger turbines are louder and

potentially more annoying to the people living nearby.

The blade broadband aerodynamic noise is the dominant noise source in modern large wind tur-

bines. Moreover, it is also responsible for the amplitude modulation (AM) observed in mainly large

turbines, i.e. pulsating broadband sound [1]. This AM, commonly referred as “swishing”, “whoosh-

ing” or “pulsating noise”, is currently considered as the main cause of annoyance for residents near

wind farms [2,3].

The 23rd International Congress on Sound and Vibration

2 ICSV23, Athens (Greece), 10-14 July 2016

Prediction of the noise emission from wind turbines and farms is very important, in particular for

new farms. Accurate noise predictions can be used for environmental impact analyses, developing

noise control strategies, and optimizing wind farm layout. Accurate predictions require the modelling

of the flow on the blades and the resulting noise source strengths and directivity. For long propagation

distances (a few kilometres), atmospheric conditions including thermal stratification, humidity, and

uneven terrain need to be accounted for.

There are several commercial codes such as SoundPLAN, CadnaA, Predictor-LIMA, WindPRO

and so forth for predicting wind turbine noise with models of increasing degree of sophistication.

However, all of them assume a monopole at the hub, which is a drawback because it does not consider

the actual radiation characteristics of the wind turbine. They simply rely on estimates of the turbine’s

sound power levels from manufacturers’ databases or on user input. The most advanced propagation

model implemented corresponds to NORD2000. It consists of a simplified ray tracing method that

can support moderate atmospheric refraction by assuming that sound speed varies linearly with

height, and that all rays follow a circular path. It also takes into account atmospheric absorption for

refracting and non-refracting media [4].

The state-of-the-art aerodynamic noise from wind turbine is based on solving the 2D flow field

over the blades and using these results to predict the aerodynamic noise using semi-empirical models

[5] or scaling of wind tunnel data [6]. However, these models haven’t yet been coupled to propagation

codes. They use simple straight ray propagation over flat terrains for the prediction of noise near the

turbine [7,8].

In this work, a state-of-the-art model of the turbine noise was coupled to a curved ray tracing code

for the prediction of noise over long distances (several kilometres). However, the code is computa-

tionally intensive with the propagation part taking the overwhelming majority of the computational

effort. Thus, a new modelling turbine noise method is proposed to properly capture the radiation

characteristics of the turbine and the atmospheric variation (wind and temperature profiles) in the

propagation at a reasonable computational effort. The approach consists of defining an equivalent

sound source placed at the turbine hub with a source strength and radiation pattern that is a function

of the turbine rotation or azimuth position of rotor.

2. Turbine noise model

As shown in Fig. 1, the WTN model consists of five modules, which are briefly described here.

The input to the code consists of the turbine and blade geometry, operating conditions, atmospheric

data, ground impedance, and execution control parameters. The turbine blades are assumed rigid, the

terrain flat, and the atmospheric conditions uniform over the domain but arbitrary with height. The

blades are then split in span-wise direction elements and the blade rotation approximated as a discrete

set of azimuth positions. Thus, this approach defines a finite number of positions on the rotor plane

to perform aerodynamic and noise calculations as shown in Fig. 2a. The sound sources characterizing

the turbine noise radiation will be defined at these points. The second module is the Aerodynamic

Module, which uses a blade element momentum method (BEM) to compute the aerodynamic param-

eters needed for noise calculations, i.e. angle-of-attack (AoA) and relative flow Mach for all blade

elements while the turbine is operating [9]. To this end, the airfoil section polars are either computed

using XFoil [10] or taken from data collected in a wind tunnel [6] to account for the induction effects.

Turbine yaw, tilt, and conning angles are accounted for in the calculation of the AoAs. Fig. 2b illus-

trates the resulting AoAs for all the positions shown in Fig. 2a for a particular wind profile.

The flow conditions around the blades of a wind turbine govern wind turbine aerodynamic noise

generation mechanisms. In the Noise Source Module, the aerodynamic noise sources (leading and

trailing edge noise) are computed for the selected blade elements and the set of azimuth blade posi-

tion. This module uses the code NAFNoise [11] or wind tunnel data [6,12] to predict the aerodynamic

noise in 1/3rd octave bands at a single point in the direction normal to the airfoil chord line at a distance

of 1 meter. Five different noise mechanisms for airfoils can be included. They are turbulent boundary


ICSV23, Athens (Greece), 10-14 July 2016 3

layer-trailing edge noise (TBLTE), separation stall flow noise (SSF), laminar boundary layer noise

(LBLVS), trailing edge bluntness noise (TEB), and turbulent inflow noise (TI). The TBLTE, SSF,

LBLVS, and TEB are known as airfoil self-noise. Semi empirical noise solutions to these mechanisms

were developed by Brooks et al. [5] and implemented in NAFNoise. On the other hand, TI corre-

sponds to the noise produced by the interaction of the external inflow with the airfoil [13]. Amiet [14]

and Moriarty [11] developed methods to solve for TI. The radiation directivity of the sources pro-

posed by Brooks et al. [5] are applied to define sound spheres to couple with the propagation module.

An example of the resulting noise spectrum for position 4 in Fig. 2a as computed by NAFNoise is

shown in Fig. 2c. Upon implementing the radiation directivity, the resulting sound sphere centred at

the trailing (or leading) edge of the airfoil element contained is shown in Fig. 2d.

The next step is the Propagation Module that implements a curved ray tracing propagation of the

individual sound spheres. This module uses the wind and temperature profiles and other atmospheric

conditions (humidity, pressure, etc.) to predict the noise at an array of microphones in the domain,

typically over a plane parallel to the ground. To this end, a large number of rays are emitted from the

sound sources. The acoustic losses, due to atmospheric attenuation and ground reflections, and Dop-

pler effect of the moving sound sources are accounted for in this module. The ray tracing code im-

plemented here is based on a NASA code developed for prediction of noise from fixed wind aircrafts

and helicopters [15]. A new Hamiltonian ray tracing formulation will be implemented in the near

future [16]. Fig. 2e illustrates the propagation of rays from a sound source.

The final module, Turbine Noise, concatenates the noise at the microphones produced by all the

blade element sound sources on the 3 blades and rotor azimuth positions for all 1/3rd octave frequency

bands. Fig. 2f shows a typical resulting noise map corresponding to a particular azimuth position.

Figure 1: Turbine noise modelling approach.

The coupling of the blade noise sources to the ray tracing propagation method is one of the novel

aspects of the work presented here. To the best of the author’s knowledge, this is the first time such

coupling of the turbine noise source to a propagation code has been reported in the open literature.

Input/Control

Module

Aerodynamic

Module AoA

Relative Mach

Noise Source

Module

Propagation

Module

Noise Spectrum NAFNoise Wind tunnel data

Input blade geometry, wind and temperature pro-files, ground impedance, and code control param-

eters:

Turbine Noise

Module

Polars XFoil Wind tunnel data

Ray Tracing Conventional Hamiltonian

Turbine Noise Spectra Maps Animation

Noise Spectrum

Sound sphere

Rays

Blade divided

in elements



(a) Points for aero and noise calculations

(b) Angle of attack on rotor plane

(c) Noise Spectrum source 4

(c) Sound sphere 4 at 500 Hz 3rd octave band

(e) Ray tracing propagation

(f) Noise maps

Figure 2: (a) Points on rotor plane for aerodynamic and noise calculation, (b) AoA as the blade makes a rota-

tion in a non-uniform flow, (c) noise source 4 spectrum computed by NAFNoise, (d) sound sphere for

source 4, (e) ray trajectories, and (f) resulting OASPL noise map due to turbine at 48 ̊azimuth position.

3. Proposed simplified turbine noise model

The most computationally intensive process in the noise modeling approach described above is

the propagation module. The main reason is that each sound sphere on the blades at each azimuth

position must be propagated through the domain accounting for the weather conditions. This process

overwhelmingly requires the most computational time (> 95%) which makes this direct approach

very difficult to implement, in particular at the farm level with many installed wind turbines.

In order to mitigate this problem, a simplified approach is proposed consisting of modeling the

turbine noise with a single equivalent sound source placed at the turbine hub that accounts for the

acoustic characteristic of the blades as they rotate. Fig. 3 illustrates the approach. Fig. 3a shows the

rotor at a particular azimuth position and the sound spheres accounting for the aerodynamic noise

AoA (deg)

SPL(dBA)

OASPL(dBA) Azimuth position = 48 ̊

Wind Turbine



produced by the blade sections. The sound spheres have varying strength and directivity from the

hub to the tip as observed in Fig. 3a. This is a consequence of the changing inflow, blade twist and

airfoil geometry along the blade. Furthermore, the sound sphere will also change as a function of the

rotor azimuth position as the blade sees a non-uniform inflow (not shown in the figure). In order to

create an equivalent sound source located at the hub, all the sources observed in Fig. 3a are added in-

coherently. In adding the sound spheres to generate the equivalent one, the key issue is to select a

distance from the hub to perform the addition. In other words, the equivalent sound source at the hub

in Fig. 3b will match the noise due to all the sound sources in Fig. 3a over a sphere centered on the

hub in free-field. Thus, the method being described is accurate only in the geometric far field of the

turbine as a whole. For a case where the distances from the sources to observers are small relative to

the rotor diameter, near field effects take place, where distance and direction information become

important. The equivalent sound sphere is not expected to be an accurate representation of the turbine

noise in the near field. However, the noise prediction away from the turbine is of most interest. It is

suggested that the distance to use for the calculation of the equivalent sound source be a few (2-4)

rotor diameters. Fig. 3b illustrates the result for a specific rotor azimuth position. As expected, the

generated equivalent sound source is characterized by a strength and directivity pattern that changes

with rotor azimuth positions (not shown).

Computational time is significantly reduced with a single equivalent source located at the hub. By

having a single source, the propagation computation has to be performed only once. This should be

contrasted to the full formulation where propagation has to be performed from all the points on the

rotor plane (see Fig. 2a).

Figure 3: (a) Array of noise source representing the turbine noise at a particular azimuth position, (b) the

equivalent noise source representing the in-coherent addition of these sources.

4. Numerical Example

4.1 Description of Turbine and Weather Conditions

A simple example problem is presented here to illustrate the proposed approach. The selected

generator is the NREL 5MW reference turbine [17]. The reason for using this turbine is that the blade

geometry and other parameters are available in the open literature. The rated rotor speed is 12.1 rpm.

The length of the blades is 61.5 meters and its maximum chord is 4.65 meters. The blade airfoil

sections are composed of a series of circular, DU and NACA airfoils. For the simulations, it is as-

sumed the hub height is 100 m and the turbine operates at 10 rpm with an inflow of 10 m/s at the hub.

The turbine yaw and tilt angles are set to zero and the rotor is not conned either. The weather condition

consists of the non-uniform wind and temperature profiles shown in Fig. 4. In this figure, the black

line in the wind profile plot sketches the position of the rotor plane. They were generated by modify-

ing experimentally measured data [18]. There is no vertical wind component in the simulations. The

terrain was assumed flat and covered with short grass with a uniform flow resistivity of 225 rays. The

flow resistivity was used to compute the ground impedance and absorption.

Sound spheres

at blades

Equivalent Sound

sphere at the hub



The blades were divided in 5 span-wise elements and the rotation accounted for by taking 15 azi-

muth positions for a total of 75 sound sources distributed on the rotor plane. A total of 702 rays were

emitted from each sound sphere. NAFNoise was used to predict the trailing edge noise for the 75

sound sources, e.g. leading edge noise was not modelled. As explained before, the equivalent sound

source was computed such as to match the noise levels at a distance of 400 meters from the hub. Once

again 702 rays were emitted from the equivalent sound source and propagated through the medium.

The formulations were implemented using Matlab and the simulations were performed on a 3.42-

GHz quad-core personal computer with 16 GB of RAM. It is important to mention that NAFNoise

and the ray tracing code are coded in Fortran.

Figure 4: Wind and temperature profile used in the simulations.

4.2 Results

The code computes the 1/3rd octave band spectrum for an array of microphones at each azimuth

position of the rotor. In these simulations, a square grid of 1600 microphones was placed on the

ground over an area of 2 km by 2 km. The turbine is at the centre in the domain. Background noise

was not added to the turbine noise results. The resulting noise maps at the 250 and 500 Hz 3rd octave

bands for the rotor in the zero azimuth position for the full or direct (left column) and equivalent

sound source (right column) formulations are shown in Fig. 5. The turbine position and wind direction

are shown in this figure. It is clear that the equivalent source formulation is capable of modelling the

turbine noise reasonably well, in particular in the downwind direction. However, the equivalent sound

source tends to smooth out the results and thus it cannot capture areas where the turbine noise is

amplified, most likely due to the contribution of different sections of the blades that radiates towards

these local regions due to refraction effects. The single equivalent sound source at the hub clearly

cannot capture this behaviour.

Fig. 6 shows a comparison of the average spectrum for microphones at 150 and 400 m distance

downwind the turbine, respectively. It can be observed that the equivalent sound source approach

predicts very well the turbine noise in particular as the observer moves away from the turbine.

Finally, the computational time was significantly reduced from 5.8 hours for the full formulation

to just 40 minutes for the equivalent source approach.

Rotor plane



(a) 250 Hz

(b) 250 Hz

(c) 500 Hz

(d) 500 Hz

Figure 5: Noise map for rotor on zero azimuth position predicted by (a) and (b) full formulation (array of

sound sources on blades) and (c) and (d) a single equivalent sound source at the hub.

Figure 6: Average spectrum in 1/3rd Octave bands for microphones at 150 and 400 meters distance down-

wind from turbine for full and proposed equivalent formulations.

5. Conclusions

A state-of-the-art wind turbine noise prediction tool was presented. The code models the flow

conditions over the blades over a full revolution. The aerodynamic variables are then used to compute

the aerodynamic noise sources along the trailing and leading edge of the blades. These noise sources

are then coupled to a curved ray tracing propagation tool to account for atmospheric effects over long

Mic. at 150 m

Mic. at 400 m

Formulation

Full

Equivalent source

Wind



propagation distances. The main limitation of this tool is the excessive computational tool required

for the propagation of the noise in the atmosphere. To this end, a new modelling approach is proposed

which consists of defining a single equivalent source with azimuth varying strength and directivity

placed at the hub. This single source is then coupled to the curved ray tracing propagation code. The

approach was tested using the NREL 5MW reference turbine. As expected, the results show accurate

predictions in the far field with a significant computation time improvement. However, the approach

did not capture the built up of sound pressure levels at localized areas due to the contribution of

different sections of the blades that radiates towards these local regions because of refraction effects.

Future work includes the implementation of a more computational efficient Hamiltonian ray tracing

tool and the noise predictions of wind farms.

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