Prediction and analysis of infra and low-frequency noise of upwind horizontal axis wind turbine using statistical wind speed model Gwang-Se Lee and Cheolung Cheong Citation: AIP Advances 4, 127117 (2014); doi: 10.1063/1.4904028 View online: http://dx.doi.org/10.1063/1.4904028 View Table of Contents: http://aip.scitation.org/toc/adv/4/12 Published by the American Institute of Physics AIP ADVANCES 4, 127117 (2014) Prediction and analysis of infra and low-frequency noise of upwind horizontal axis wind turbine using statistical wind speed model Gwang-Se Lee and Cheolung Cheonga School of Mechanical Engineering, Pusan National University, Busan, 609-745, Rep of Korea (Received August 2014; accepted 26 November 2014; published online December 2014) Despite increasing concern about low-frequency noise of modern large horizontalaxis wind turbines (HAWTs), few studies have focused on its origin or its prediction methods In this paper, infra- and low-frequency (the ILF) wind turbine noise are closely examined and an efficient method is developed for its prediction Although most previous studies have assumed that the ILF noise consists primarily of blade passing frequency (BPF) noise components, these tonal noise components are seldom identified in the measured noise spectrum, except for the case of downwind wind turbines In reality, since modern HAWTs are very large, during rotation, a single blade of the turbine experiences inflow with variation in wind speed in time as well as in space, breaking periodic perturbations of the BPF Consequently, this transforms acoustic contributions at the BPF harmonics into broadband noise components In this study, the ILF noise of wind turbines is predicted by combining Lowson’s acoustic analogy with the stochastic wind model, which is employed to reproduce realistic wind speed conditions In order to predict the effects of these wind conditions on pressure variation on the blade surface, unsteadiness in the incident wind speed is incorporated into the XFOIL code by varying incident flow velocities on each blade section, which depend on the azimuthal locations of the rotating blade The calculated surface pressure distribution is subsequently used to predict acoustic pressure at an observing location by using Lowson’s analogy These predictions are compared with measured data, which ensures that the present method can reproduce the broadband characteristics of the measured low-frequency noise spectrum Further investigations are carried out to characterize the IFL noise in terms of pressure loading on blade surface, narrow-band noise spectrum and noise maps around the turbine C 2014 Author(s) All article content, except where otherwise noted, is licensed under a Creative Commons Attribution 3.0 Unported License [http://dx.doi.org/10.1063/1.4904028] I INTRODUCTION With decreasing mechanical noise in wind turbines (WTs), aerodynamic noise is the main hindrance to its widespread dissemination.1–3 Sources of aerodynamic noise in modern upwind horizontal-axis wind turbines (HAWTs) are categorized into low-frequency noise, inflow-turbulence noise, and airfoil self-noise.4,5 The blade passing frequency (BPF) noise of a modern large HAWT generally belongs to the infra-sound frequency range up to about 10 Hz because of the very low rotational speed of the turbine In contrast, inflow- and self-noise are considered to contribute mainly as audible noise components As a purpose for identification of main noise source within audible frequency range, acoustic visualization techniques were performed, previously.5 It was found that the trailing edge noise is dominant in the frequency range 500 Hz to 3000 Hz of the spectrum of acoustic waves radiating a Electronic mail: ccheong@pusan.ac.kr 2158-3226/2014/4(12)/127117/14 4, 127117-1 © Author(s) 2014 127117-2 G.-S Lee and C Cheong AIP Advances 4, 127117 (2014) from the pitch-controlled upwind-type wind turbine Numerous studies also reported that trailing edge noise of wind turbines predicted using semi-empirical models showed good agreements with measurements.6–9 This semi-empirical model was developed by using scaling law of trailing edge noise and huge measurements for various flow conditions including attack of angle, flow speed, and geometric size of NACA0012 airfoil.10 For applying the semi-empirical model, the averaged flow field information was obtained from computational fluid dynamics techniques It is seen from these results that the trailing-edge noise is dominant in the middle frequency range of noise spectrum from the large upwind-type horizontal axis wind turbine As described in the IEC 61400-11 which is the international code for the measurement and assessment of acoustic power of wind turbines, infrasound, low-frequency noise, and low-frequency modulation of broadband or tonal noise are important factors to characterize wind turbine noise.11 Although there is needs about research to characterize infra- and low-frequency (ILF) noise from modern HAWTs, few studies have been performed to understand its origin Most previous studies have assumed that the ILF noise consists primarily of BPF noise components Jung et al.12 and Lee et al.13 have reported the ILF noise results from unidentified noise source components that evidently have a broad spectral distribution rather than tonal In reality, the long trajectory of a single blade in a broad range of altitude levels during its slow rotation inevitably subjects the blade to incident wind with speeds varying in time as well as in space, which perturb the blade randomly As a result, acoustic contributions at the BPF harmonics are transformed into broadband noise components, as observed in the radiated noise Since the length scale of turbulent boundary layer (TBL) thickness on the turbine blade is too short to be responsible for low-frequency noise, it is understood that contribution of trailing edge noise to low frequency components is marginal Correspondingly, inflow broadband noise can be considered to be main source of wind turbine noise in low frequency range, and its dominant source is mainly due to atmospheric turbulence in wind of length scale large enough to cause the infrasonic and low frequency noise Several experimental studies reported difficulties in measuring the low-frequency wind turbine noise due to the masking effect of background noise Bray and James14 analyzed the masking effects on the measurement Leventhall15 reported the case where high background noise contaminates the measurement under 40 Hz Fégeant16 reported more serious influences of background noise at higher wind speeds Most previous studies to predict wind turbine noise using hybrid CAA methods were based on the averaged wind speed profile, which is simply computed from empirical formulae in the form of power or logarithmic functions with a parameter describing ground roughness approximately.6–9 Therefore, these methods are not capable of considering time- and space- variations in the lift and drag of the rotating blade sections due to randomly fluctuating incident wind speeds To investigate effects of power fluctuation induced by unsteadiness of incident wind on the control strategy, stochastic wind models at a given altitude have been developed.17 In this study, these stochastic wind models are employed to reproduce more realistic wind profiles varying in space and time, which are considered to cause the broadband components in the ILF noise of WTs By incorporating the hybrid CAA method with the stochastic model for the incident wind speeds, the ILF noise of WTs is predicted in present paper For the hybrid CAA model, the XFOIL code18 and Lowson’s acoustic analogy19 are used The former is used to predict the aerodynamic response of the turbine blade sections, and the latter is used to predict the acoustic wave propagation from the rotating sources to the fixed receiver In Section II, the statistical wind speed model employed in this paper is described In Section III, Lowson’s acoustic analogy is briefly introduced and the acoustic code based on Lowson’s formula is validated by comparing its prediction with that using the Ffowcs-Williams and Hawkings (FW-H) formula20 in the commercial code In Section IV, the measured data for the IFL noise of the target WT is described In Sections V, the results obtained using the present prediction methods are compared with the measurements Finally, in Section VI, the detailed analysis on the IFL noise of the WT is carried out by investigating pressure loading on the turbine blade, narrow-band acoustic pressure spectrum, and noise maps around the turbine 127117-3 G.-S Lee and C Cheong AIP Advances 4, 127117 (2014) II STATISTICAL WIND SPEED MODEL In order to predict the performance of WTs, a real-time wind speed model has been used.17 The real-time wind model is made up of two components: a quasi-steady term obtained from Van der Hoven’s model and an unsteady term computed by a stochastic model Van der Hoven’s wind model is associated with time scales on the order of hours and days, which constitutes a steady model in terms of the acoustic whose time scale is on the order of seconds The stochastic model represents wind speed in time intervals on the order of seconds Both wind speed models contain the same parameters which can be determined from the environmental conditions at the site of the wind turbines Therefore, real-time wind speed, v, including the quasi-steady and the unsteady terms can be expressed in the form: v(t, z) = Vz (z) + σ v (z)vt (t, z), (1) where Vz denotes the steady component, vt represents the unsteady component, and σ v is the estimated standard deviation First, the steady wind speed Vz along altitudes z can be computed using a power or logarithmic model The logarithmic wind speed model is written in the form: z ref H ln z 0ref ln z , Vs = Vz (z) (2) ln H ln z z 0ref z0 where zref is the reference height, 10 m; z is the height of the anemometer; H is the rotor center height; z0 is ground roughness length, which is 0.05 m in the present study; z0ref is the reference roughness of 0.05 m; and Vs is the standardized wind speed.11 Using Eq (2) with Vz at a given height, the averaged wind speeds along heights of z are computed Next, the unsteady wind speed components are determined based on the procedure by Nichita et al.17 In their study, the unsteady components had been defined by colored noise as t vt (t, z) = h(τ, z)w(t − τ)dτ, (3) where h(τ, z) is the impulse response of filters, which is used to describe statistical characteristics of atmospheric inflow, and w(t) is white noise The impulse response may be written as ∞ P(ω, z) cos(ωt)dω, (4) h(t, z) = π where P(ω, z) = Re K t (z) ≈ K t (z) , (1 + jωT f (z))5/6 T f (z) 2π , 1 Ts B , Tf = TL (z) VZ (z) (5) (6) (7) The low pass filter, P(ω, z), to compute the impulse response, is a function of: the gain, Kt; the sampling period, Ts; and a characteristic time scale, T f , which is related to the atmospheric turbulent length scale along the heights, TL In this paper, TL is computed from an empirical formula depending on ground roughness conditions.6 Finally, the stochastic wind speed is computed from the product of the unsteady component vt and the estimated standard deviation σ v The standard deviation may be experimentally determined from regression analysis of the wind speeds measured at a given site The above-described procedure for computation of the fluctuating wind speed is roughly based on the approach of Nichita et al However, numerical quadrature integration is applied to compute 127117-4 G.-S Lee and C Cheong AIP Advances 4, 127117 (2014) FIG Time histories of wind speeds computed by using statistical model of Eq (1) in a height range of to 150 m, for an averaged wind speed of m/s at a height of 10 m and with a ground roughness of 0.05m the impulse in this study, whereas Nichita et al used the discrete impulse h i (t) The accuracy of the discrete impulse function depends on spectral bandwidth ∆ω, which has to be small for reliable prediction If the spectral bandwidth is given, the procedure to compute the discrete impulse function is faster than numerical quadrature integration However, an additional step of estimating the spectral bandwidth is required to optimize the spectral band in this prediction, unless the band is known By applying numerical integration, the optimization procedure can be simplified during the computation of the fluctuating components, and the accuracy of the impulse function is acceptable for large ranges of wind speeds along altitudes without an additional step to estimate the bandwidth Illustrative computation for reproducing real wind conditions is carried out using the steady wind profile along the height in Eq (2) and the turbulent length scale computed from the empirical formula in the form:6 TL (z) = 25z 0.35 z0−0.063, (8) The resultant temporal and spatial wind fluctuations are shown in Fig 1, from which the fluctuations of wind speed due to variations of turbulent kinetic energy and length scale along the altitude can be identified The steady wind speed profile along altitude level is obtained under the assumption that the averaged wind speed at a height of 10 m and the ground roughness z0 are m/s and 0.05 m, respectively To predict aerodynamic characteristics of wind turbine blades subject to this wind, FIG Spectrum of unsteady wind history at a height of 50 m computed by using the statistical model in Eq (1) in the case of the averaged wind speed of m/s at a height of 10 m and a ground roughness of 0.05m 127117-5 G.-S Lee and C Cheong AIP Advances 4, 127117 (2014) incident flow condition on sectional blades which is a function of time and space are computed by interpolating these unsteady wind data Resultant unsteady wind has spectral profile of decaying rate proportional to -5/6 which closely follows the decaying rate of low pass filter in Eq (5), as shown in Fig III LOWSON ACOUSTIC ANALOGY Lighthill derived a method to deal with aerodynamic noise generated by sources in rectilinear uniform motion As an extension of Lighthill’s study, Lowson derived formulas to describe the acoustic fields due to various types of sources: loading, simple source, and acoustic stress in arbitrary motion.19 The equation employed in this study from Lowson’s acoustic analogy is ρ` t = ρ` f + ρ` n , (9) where ρ` is density fluctuation in sound field, and the subscripts ‘t’, ‘ f ’, and ‘n’ denote the total, far-field, and near-field terms, respectively The far- and near-field density fluctuations corresponding to the loading are respectively expressed in the forms ∂Fi Fi ∂ Mr (x i − yi ) + , ρ` f = − Mr ∂t 4πa03r 2(1 − Mr )2 ∂t (10) Fi (x i − yi ) − M − Fi Mi , ρ` n = 2 r − Mr 4πa0r (1 − Mr ) (11) and where a0 is the speed of sound, Fi is the force exerted on the source, M is the Mach number of the moving source, x i indicates the location of the observer, yi represents the location of the source and r is the distance between the source and the observer From Eqs (10) and (11), each contribution from the loading sources can be related to specific physical behavior The acoustic field due to point loading sources in rotational motion is found to consist of superposition of acoustic pressures represented by five terms: Doppler shift, (1−Mr ); unsteadiness of force, ∂Fi /∂t ; acceleration from source to observer, ∂ Mr /∂t; geometric(spherical) spreading of the wave, Fi (x i − yi )/4πr; and convection of the source, Fi Mi In the case of an application using the far-field formula of Eq (10), even though force is steady, ∂Fi /∂t=0, an acoustic field can be generated from the acceleration term For instance, a rotating source of constant force and angular velocity generates acoustic pressure through its relative acceleration with respect to an observer’s fixed position If the sources are in rectilinear motion with constant velocity, Eq (10) indicates that acoustic pressure in the far-field is only due to unsteadiness of the source, as described in Lighthill’s analogy The near-field terms include spherical wave-spreading and convection terms The latter represents acoustic pressure generated by relative motion of the sources compared to a fixed observer while the former simply represents spherical wave-spreading if the sources are fixed To validate the acoustic code developed using Lowson’s acoustic analogy, aerodynamic noise generated by a small vertical type of wind turbine is predicted and compared with the prediction obtained using the commercial code based on the FW-H equation.20 Figure shows the geometry of the target turbine The diameter and height of the turbine are 0.54 m and 0.75 m, respectively The turbine is assumed to rotate at a speed of 16.36 rad/s and to be subject to inflow wind at a speed of m/s First, the flow field around the rotating vertical turbine is simulated using a commercial CFD code, Fluent 14.5 The RANS solver with Shear-Stress-Transport (SST) k-ω model21 in Fluent is utilized Then, acoustic pressures at a specific location are predicted using two methods: the FW-H equation provided in Fluent and the present in-house code based on Lowson’s analogy Both methods used the same source data obtained from the flow simulation results by Fluent, which is the static pressure on the blade Figures and show the acoustic pressure and its spectrum at near field, r/λ 1st > 1, respectively, where r is distance from the center of turbine to an observer and 127117-6 G.-S Lee and C Cheong AIP Advances 4, 127117 (2014) FIG Geometrical shape of target turbine FIG Comparison of acoustic predictions for a vertical wind turbine between the commercial code (acoustic module in Fluent) and the present Lowson code at near field, r /λ 1st > 1; (a) time-variation of acoustic pressure and (b) its corresponding spectrum 127117-7 G.-S Lee and C Cheong AIP Advances 4, 127117 (2014) TABLE I Specifications of the target HAWT for the field measurements Rated power Power control Rotational speed Rotor diameter Hub height 2.0 MW Blade Pitch 16.8 RPM 86.6 m 78.0 m λ 1st is the wavelength corresponding to the first BPF Good agreement is observed between the two methods, confirming the validity of the present method based on Lowson’s acoustic analogy IV MEASUREMENT OF INFRA- AND LOW-FREQUENCY NOISE FROM A HAWT One-third octave band levels are measured in the frequency range of to 40 Hz at wind speeds of and m/s The specifications of the target HAWT are briefly summarized in Table I Measurement instrumentation and procedures based on the IEC61400-11 (2002) standard were used to evaluate the ILF noise emission from the turbines Noise measurements were taken downstream of the wind turbine using a microphone positioned on a circular rigid board The board was placed on the ground to reduce wind noise generated at the microphone and to minimize the influence due to variations in the soil type Previous studies by Lee et al.13 and Jung et al.12 can be refered to for further details on the experimental procedures According to the IEC61400-11, measured sound levels must be corrected to assess the influence of background noise by using the following equation: L s = 10 log10 100.1(L S+n ) − 100.1(L n ) (12) where L s is equivalent continuous sound pressure level during the operation of a wind turbine alone, L s+n is the equivalent level of wind turbine noise with background noise, L n is the background noise Figure shows the measured SPLs with relative contributions of background noise in the relevant frequency bands The IEC61400-11 requests that, if the difference between L s+n and L n is larger than dB, L s is computed by Eq (12) to assess apparent sound power level, and in the case for the difference between to dB, L s+n is corrected by subtraction of 1.3 dB, but the corrected data are indicated with mark of an asterisk, “*”.11 In the case where the difference is under dB, the measured band levels need not to be reported For higher wind speed m/s at the height of 10 m, it is seen that effect of the background noise on the measured noise is stronger in the frequency range under 40 Hz, while at the wind speed of m/s only two band levels are valid Similar difficulties are reported in other studies Leventhall reported that the noise radiating from the wind turbine of 1.5 MW capacity could be separated from the background in the frequency range only above about 40 Hz, where the measurements were FIG 1/3 octave band level L s of wind turbine operating alone estimated by using IEC61400-11: (a), L s for V10 = m/s; (b) L s for V10 = m/s 127117-8 G.-S Lee and C Cheong AIP Advances 4, 127117 (2014) FIG Comparisons between predicted and measured powers according to wind speeds conducted at 65 m apart from the turbine.15 The contamination by background noise becomes more serious for higher wind speeds,16 as confirmed in Fig 6(b) The measurement taken in the case of V10 = m/s is, therefore, chosen to validate the predictions V VALIDATIONS OF CURRENT NUMERICAL METHODS In this section, the ILF noise of the HAWT is predicted using the hybrid CAA methods based on Lowson’s acoustic analogy and the stochastic wind speed model The predicted results are compared with the measured data to validate the method, and then possible underlying mechanisms responsible for generating the ILF noise of HAWTs are discussed As described above, an empirical constant has been used to define the standard deviation of the fluctuating wind speed components The wind speed and its standard deviation can be modeled by FIG Comparisons of 1/3 octave band levels among measurements and predictions using mean and stochastic wind speed: (a) V10 = 6m/s and (b) V10 = 7m/s 127117-9 G.-S Lee and C Cheong AIP Advances 4, 127117 (2014) FIG Comparison of iso-contours of averaged pressure on the blade surface, computed by using the mean (solid lines) and stochastic (dash-dot-dot lines) wind models for V10 = m/s using the measured wind speeds at the target site Nichita et al have applied the following equation to estimate the standard deviation instead of using the measured data: σ v (z) = kσ, vVz (z), (13) where kσ, v is a proportionality constant between the standard deviation and mean wind speed distribution along the altitude This parameter can be determined empirically by considering geometric conditions around the target site where the wind turbine is installed For example, at the coast and offshore sites where the influence of the site’s topography is weak, kσ, v is considered to be small, i.e., 0.1 to 0.15 All of the following computations to predict the fluctuating wind speed components are conducted using Eq (13) with kσ, v = 0.15 Prior to computing acoustic wave propagation, the aerodynamic characteristics of the pressure distribution on the surface of the rotating blade must be computed as acoustic source input data The XFOIL code is utilized to compute the pressure distribution on each section of the wind turbine blades Aerodynamic prediction is validated by comparing the predicted power of the turbine with the measured data, as shown in Figure It is seen that the predicted power for the target wind turbine agrees well with measurements Figure compares predictions with measurements in the frequency range under 40 Hz When only the averaged wind profiles are considered, there are significant discrepancies between the predicted results and the measured data Predictions using the averaged wind profiles can give only BPF harmonic contributions, and thus no contributions above 10 Hz are identified The predictions using the statistical wind speed model show much better agreement to the measurements, though differences FIG 10 Iso-contours of fluctuating surface pressure on the blade computed by using the mean (a) and stochastic (b) wind models for V10 = m/s 127117-10 G.-S Lee and C Cheong AIP Advances 4, 127117 (2014) FIG 11 Predicted spectrum of the ILF noise radiating from the target wind turbine using the mean and stochastic wind models at wind speeds V10 = and m/s at the reference position described in the IEC61400-11.11 are still observed The observed discrepancies below 10 Hz seem to be due to the underestimations of the unsteady wind components in the corresponding frequency range The lower frequency components are more associated with large coherent turbulent structure which can’t be well captured by the current model of which the length scale depends on only ground roughness Difference between the predictions and the measurements in the frequency range above 30 Hz can be explained by additional source mechanism associated with the broadband trailing edge noise As frequency increases, the relative contribution of trailing edge noise is known to gradually rise.7 Except for these differences, the observed agreement confirms that the actual ILF noise generation mechanism of the HAWT is related to the stochastic characteristics of inflow wind velocities in the corresponding frequency range Based on these results, further detailed analysis on the generation and propagation of the ILF noise of the wind turbine is carried out using the current numerical method in the following section VI NUMERICAL ANALYSIS OF INFRASOUND AND LOW-FRQUENCY NOISE OF WIND TURBINE Both wind models result in similar distributions of averaged surface pressure on the blade surface as shown in Fig 9, where V10 = 6m/s In order to analyze induced pressure loading on the blade surface by the stochastic wind model, mean square level of surface pressure L r is computed, FIG 12 Noise maps of the OASPLs on the ground where wind is directed from bottom to top: (a), SPLs using the mean wind model; (b), SPLs using the stochastic wind model 127117-11 G.-S Lee and C Cheong AIP Advances 4, 127117 (2014) FIG 13 Distributions of fluctuating surface pressure computed by using the mean (a, b, c) and stochastic (d, e, f) wind models in the frequency bands of 1st BPF to 10 Hz (a, d), 10 Hz to 20 Hz (b, e), and 40 Hz to 50 Hz (c, f) which is defined in the from, L r (y) = 20 log10 (pRMS(y) / max(pRMS(y))) (14) where pRMS is the RMS of surface pressure, and y is the position of an element on the sectional blade surface in the coordinate fixed on the rotating blade Figure 10 shows distribution of L r to estimate fluctuations of surface pressures as acoustic sources It is seen in both cases that pressure loadings computed using each wind model are concentrated in the vicinity of the leading edge, and consist of two distinctive regions divided by the virtual line of stagnation points in the span-wise direction: a region of intensive loading on the suction side; the other region of relatively weak loading on the pressure side However, different characteristics of the distributions obtained by each wind model are identified When the mean wind model is applied, fluctuations of surface pressure originate from sectional inflow speed and the angle of attack varying along altitude during the rotational motion of the blade Most of the pressure loadings are, therefore, positioned near the blade tip where inflow speed changes strongly In the case using the stochastic wind model, it seems that the acoustic loading is more stretched into the hub This implies that additional source mechanism FIG 14 Distribution of fluctuating surface pressure near the leading edge along the radial direction, computed by using the stochastic wind model for V10 = m/s 127117-12 G.-S Lee and C Cheong AIP Advances 4, 127117 (2014) FIG 15 Comparison of SPLs computed using spherical propagation model used in the IEC61400-11 to assess apparent acoustic power of the wind turbine: empty bars denote SPLs at the position of distance 2r 0; filled bars denote the SPLs at the position of distance r 0, corrected by the factor A spherical accounting for the attenuation due to spherical propagation exists in comparison with the case using the mean wind model, which will be made clear in the noise spectrum predicted using these sources Figure 11 shows the SPL spectrum of the ILF noise predicted in the cases of wind speeds, V10 = and m/s For both wind speeds, fundamental BPF component is significant when the mean wind model is applied while the BPF harmonics and non-BPF component are comparable in the case using the stochastic wind model Together with the results shown in Figs and 10, it can be seen that pressure loading sources predicted using the stochastic model induce non-BPF noise component, which can explain broadband noise character of the ILF noise identified by Jung et al.12 Figure 12 shows noise maps around the wind turbine, which are drawn using the overall sound pressure levels (OASPLs) predicted by using each wind model From Fig 12(a), it is seen that the directivity of the OASPLs predicted using the mean wind model is similar to that due to quadrupolar TABLE II Difference between the predictions and the estimations corrected by spherical propagation model in far-field Reference distance, r r IEC (∼120 m) λ 1st (∼400m) OASPL2r – (OASPLr 0+Aspherical) 2.73 dB 0.38 dB 127117-13 G.-S Lee and C Cheong AIP Advances 4, 127117 (2014) source and shows stronger radiation in the right-hand side due to the Doppler effects of the blades moving toward ground This directivity can be understood from the classical acoustic theory on the propeller BPF noise.22 For the case using the stochastic model, the directivity shown in Fig 12(b) shows the dipolar pattern which is commonly known to be typical characteristics of the wind turbine noise where broadband noise components are dominant These results reveals that the unsteadiness of inflow wind reproduced by the stochastic wind model contributes to the broadband property of the ILF noise of the wind turbine Despite the similar distribution of the surface pressure near the leading edge as shown Fig 10, the acoustic predictions shown in Figs 11 and 12 indicate that the surface pressure caused by the mean wind profile contributes to only a few lower harmonics of BPF components Figure 13 shows distribution of pressure loading near the leading edge in the specific frequency bands In the frequency range below the 10th BPF, the pressure loading computed with each wind model shows similar pattern to each other However, in the case using the mean wind model, the loading sources are negligible in higher frequency bands as shown in Fig 13(b) and 13(c), while in the case using the unsteady inflow the pressure loading computed in each frequency bands are comparable to one another except that the area of pressure loading in higher frequency bands gradually spreads to the hub as frequency increases Figure 14 shows distribution of the relative fluctuating pressure along the elements on the leading edge line Note that the normalized radial location of the sectional blades, r seg/r, is roughly proportional to the relative speed of the mean inflow wind due to rotational speed of the turbine It is seen that the pressure loading approximately follows velocity dependency of sixth power law Since the strength of pressure loading is proportional to the radiated acoustic pressure in free fields, the sixth power proportionality is consistent with the Curle’s acoustic analogy,23 where each single element is regarded as a compact source whose length scale (here, the chord of sectional blade) is less than the wavelength of the radiated sound According to the IEC61400-11, the acoustic radiation is assumed to be spherical waves whose power decays by the attenuation factor Aspherical = -6 for double distance from the source Figure 15 compares the predicted spectrum to verify this assumption Figure 15(b) compare the predicted sound pressure level SPL2r0 at a distance by 2r IEC away from the wind turbine with the SPLr0 at r I EC corrected with the attenuation factor Aspherical = -6, while Fig 15(a) compares the SPL2r0 at a distance by 2λ1st away with SPLr0 + Aspherical within the frequency range up to the 50th BPF Here, λ1st (∼400 m) is the wavelength of 1st BPF and r I EC (∼120 m) is a distance from a tower to a reference point defined in the IEC61400-11 There are better agreement between two predictions in the case of Fig 15(a) than in the case of Fig 15(b) For more quantitative comparison, Table II shows the difference of overall SPLs between two cases In the case of r = r I EC , the model underestimates the OASPL by 2.73 dB In the other case of r 0= λ1st, the difference is only 0.38 dB VII CONCLUSION The ILF noise of an upwind horizontal axis wind turbine is investigated using hybrid CAA techniques combined with the stochastic wind model The predictions using the unsteady wind profile model provide closer agreement to the measurements at higher frequency than 10 Hz, which indicates that the ILF noise of wind turbines is related to the inflow turbulence in the corresponding ILF ranges By analyzing pressure loading on the blade, its radiated acoustic pressure and noise map around the turbine predicted using the stochastic wind model, it is shown that the ILF noise of wind turbine follows the characteristics of inflow turbulence noise In addition, the experimental assessment of acoustic power of the wind turbines according to the IEC61400-11 is found to give an underestimation of it in the infrasonic and low-frequency range However, large discrepancies between the measurements and the predictions are identified in the frequency range below 10 Hz The reason for this difference seems to be the difficulty in modeling large-scale turbulence in the atmosphere The low pass filter defined in Eq (5) is based on Kolmogorov’s law for the inertial subrange where the turbulent energy wavenumber spectrum follows -5/3 power law.24 According to Kolmogorov’s law of turbulence, large-scale turbulence must be resolved with detailed boundary conditions since it exhibits a more coherent structure than small-scale turbulence Improvement in 127117-14 G.-S Lee and C Cheong AIP Advances 4, 127117 (2014) understanding the infra- and low-frequency behavior of large-scale turbulence will help increase the accuracy of the prediction model in this lower frequency range ACKNOWLEDGMENTS This research was supported by Basic Science Research Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Science, ICT & Future Planning(NRF2013R1A1A2012672), and also supported by the Human Resources Development program(No 20134030200290) of the Korea Institute of Energy Technology Evaluation and Planning(KETEP) grant funded by the Korea government Ministry of Trade, Industry and Energy G P Van den Berg, E Pedersen, J Bouma, and R Bakker, Final report (2008) M Nissenbaum, J Aramini, and C Hanning, in 10th International Congress on Noise as a Public Health Problem, London, UK, 24-28, July (2011) D Kurpas, B Mroczek, B Karakiewicz, K Kassolik, and W Andrzejewski, Annals of Agricultural and Environmental Medicine 20, 595 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