Noise prediction via large eddy simulation:Application to radial fansBay

ch 20

ouscoue edureagathel resin nedicightutilineer

ry sub

have made notable contributions to aerodynamics andStudies on aeroacoustics began after the 2nd WorldWar, as the civil aircraft technology has evolved. In hisacoustic analogy, Lighthill1,2 derived an inhomoge-neous wave equation to describe the jet noise, whicharises due to turbulent pressure fluctuations. Curle3

enhanced the analogy by introducing the effect of solidsurfaces on sound generation. Later, Ffowcs Williamsand Hawkings4 added the effect of moving solidsurfaces on sound generation. Most of the modernCAA methods apply the acoustic analogy of Ffowcs

acoustics of fans. Numerical methods for the predictionof fan noise usually solve for the flow field first, andthen predict the acoustic field based on the flow data. Awide range of models are used for both flow simula-tions and acoustic computations. Jeon et al.12 calcu-lated the aeroacoustic signal of a centrifugal fan bysolving the inviscid, unsteady flow-field via vortexmethod and applying an acoustic analogy. Maaloum etal.13 coupled a 2D vortex surface analysis with theFWH analogy to predict the acoustic field of an axialfan. Khelladi et al.14 obtained the acoustic sources bysolving the Reynolds Averaged Navier-Stokes (RANS)equations and calculated tonal noise of a centrifugalfan via FWH analogy. Nallasamy and Envia15 studiedthe broadband noise generated by the rotor wake turbu-lence stator interaction with the help of RANS simula-tions. Wu and Su16 developed a semi-empirical formulato predict noise of axial fans in free-field. Ballesteros-Tajadura et al.17 studied the acoustic characteristics of a

a) Mechanical Engineering Department,Yeditepe University,34755 Istanbul TURKEY; email:sorguven@yeditepe.edu.tr.

b) Vibration and Acoustic Technologies, Arcelik AS 34950Istanbul TURKEY; email: yilmaz.dogan@arcelik.com.

c) Vibration and Acoustic Technologies, Arcelik AS 34950Istanbul TURKEY; email: faruk.bayraktar@arcelik.com.

d) Mechanical Engineering Department, ITU, 34437 IstanbulTURKEY, sanliturk@itu.edu.tr.Esra Sorguvena), Yilmaz Doganb), Faruk

(Received: 23 September 2008; Revised: 11 Mar

In this study, aerodynamics and aeroacby using a hybrid computational aeroafield of both fans is simulated with largare computed based on the pressequation, which accounts for the propacoustic waves, is solved to determineboundary element method. Numericadata. Sound pressure level distributiondirectivity of the acoustic waves are prResults of the LES provide an insgeneration mechanisms, which can be 2009 Institute of Noise Control Engi

Primary subject classification: 11.4.1; Seconda

1 INTRODUCTION

Flow induced noise prediction in industrial applica-tions is essential in order to control the noise emissionand to comply with the noise regulations and consumerdemands. Experimental methods involve drawbackslike time and investment expenses and measurementerrors like reflection problems. Thanks to the advance-ment in computational technology, computationalaeroacoustics (CAA) became a valuable method fornoise prediction and reduction. Hybrid CAA methodsare especially efficient and relatively inexpensive, sincethey solve for the different scales of aerodynamics andaeroacoustics separately.Noise Control Eng. J. 57 (3), May-June 2009raktarc) and Kenan Y. Sanliturkd)

09; Accepted: 11 March 2009)

tics of two radial fans are investigatedstics method. Unsteady turbulent flowdy simulation (LES).Acoustic sourcesfluctuations. Inhomogeneous wave

tion, diffraction and scattering of thefar field sound pressure level with theults are validated with experimentalarrow band frequency spectrum andted numerically with a high accuracy.into the turbulent flow and noisezed to reduce fan noise.ing.

ject classification: 75

Williams and Hawkings (FWH) to predict the noiseradiated directly from a source to the observer inabsence of any barriers in-between. If there are barriersbetween the source and the observer, like the casing ofa radial fan, then wave equation is to be solved to takepropagation, diffraction and scattering of the wavesinto account. A detailed review on computationalaeroacoustics has been provided by Colonius andLele5.

Studies involving analysis, prediction and reductionof fan noise are active research areas because of thewidespread use of axial and centrifugal fans in indus-try. Gutin6, Carolus7, Bommes et al.8 and Neise911169

b) Fcentrifugal fan based on a three-dimensional numericalsimulation. zyrk and Alpman18 predicted soundfields of ducted fans based on the solution of thefrequency-domain form of the Euler equations, whichare linearized about an axisymmetric non-uniformbackground flow. Merits and limits of these uncoupledaeroacoustic predictions basically depend on theaccuracy of the flow simulation. More complex flowsimulations result in more accurate acoustic sourceterms; however, increase the computational cost.

Low noise emission has become one of the designcriteria for fans. Therefore studies with an emphasis onnumerical noise prediction and reduction in earlydesign stages attract attention. Lin and Huang19

designed a small forward curved centrifugal fan for anotebook computer with the emphasis on the bladeshape, blade inlet angle and the outlet geometry of thehousing. Grard and Berry20 developed an inverseaeroacoustic model to reconstruct the aerodynamicforces (dipole strength distribution) acting by the fanblades on the fluid.

In this paper, a hybrid CAA method is employed toinvestigate the aerodynamics and aeroacoustics ofradial fans. Aeroacoustic computations are performedin two steps: i) computing the unsteady flow field andii) computing the acoustic pressure fluctuations in thefar field. The flow field is solved with large eddysimulation (LES) where the large and energetic scalesof turbulence are resolved and the small and dissipativescales are modeled. LES resolves the dominant noisesources without any modeling error and thereforeprovides a reliable basis for acoustic computation.Acoustic sources are computed based on the turbulentpressure fluctuations. The inhomogeneous waveequation is solved via multi domain boundary elementmethod (BEM) analysis by taking propagation, diffrac-tion and scattering of the acoustic sources into account.Finally, the far field sound pressure level and soundpower level are obtained.

This methodology is applied to two radial fans.

Fig. 1Geometry of the investigated fans a) Fan I,170 Noise Control Eng. J. 57 (3), May-June 2009Detailed experimental measurements are performedand compared to numerical results to validate thenumerical methodology. Sound pressure level distribu-tions at discrete microphone points are compared innarrow band frequency spectrum. Prediction of thedirectivity of the acoustic waves is validated bycomparing the sound intensity maps on fictitioussurfaces surrounding the fans. These comparisonsshow that the numerical method can predict the direc-tivity and sound pressure level in narrow bandspectrum satisfactorily.

Computing the aerodynamic and aeroacoustic dataof two fans also shows how the CAA methods providean insight to the turbulent flow and the noise generationmechanisms. By comparing the detailed numerical dataof both fans, regions of high noise emission can beidentified. Such information can be utilized to decreasethe overall sound pressure level of a fan and to designlow noise fans.

2 INVESTIGATED FANS

Two Sirocco type radial fan systems consisting of aradial impeller, volute, inlet and outlet pipes are inves-tigated. Both of the investigated radial fan systems arenearly 50 cm long and have a rotational speed of2800 rpm (Fig. 1). The impeller of Fan I, with 37forward curved blades has an outer diameter of130 mm and a depth of 55 mm. Accordingly, Reynoldsnumber based on the blade tip diameter and speed isRetip=136,000 and Mach number at the tip is Mtip=0.05. The impeller of Fan II, with 25 forward curvedblades, has an outer diameter of 120 mm and a depth of85 mm. Accordingly, Reynolds number based on theblade tip diameter and speed is Retip=110,000 andMach number at the tip is Mtip=0.05. Performancecurves of both fans show that Fan II provides slightlyhigher pressure heads throughout the whole flow raterange (Fig. 2). At the operating point, the flow rate ofFan I is 72 lt/ s and of Fan II is 80 lt/ s. Numerical

an II.

The governing equations employed for LES are350 Fan I - experimentalsimulations and experimental measurements areperformed at these operating points.

Overall sound power level of the fans increase withthe flow rate. At the operating point, the first radial fansystem (Fan I) has a 6 dB higher overall sound powerlevel than the second one (Fan II).

3 COMPUTATIONAL METHODS

3.1 Computational Fluid Dynamics

Modern hybrid CAA techniques can be separatedinto two steps; the first step being the determination ofthe unsteady flow data and the second step being thecomputation of the acoustic data. Time-dependent flowcalculation can be performed via unsteady Reynoldsaveraged Navier-Stokes equations (uRANS), LES ordirect numerical simulation (DNS). URANS requiresrelatively low computational time and power; however,because of intrinsic modeling errors, it cannot repre-sent turbulent fluctuations accurately. On the otherhand, DNS aims to solve the Navier-Stokes equationswithout any modeling by resolving the whole range oftime and length scales; from integral scales to Kolmog-orov scales. With DNS, one can resolve all the scalesand obtain the mean, turbulent and acoustic parts of theflow parameters. The main disadvantage of suchmethods is the enormous computational cost. Thereforeonly relatively simple flow configurations at modestReynolds numbers are studied with DNS.

In this paper LES method, which resolves the largeand energetic scales of turbulence and models the smalland dissipative scales, is used to calculate the unsteadyflow field. LES is a good compromise for acousticcalculations, since the dominant noise sources aregenerated by the energetic eddies and these areresolved without any modeling. Flow simulation isperformed with the commercial software Fluent21.

0

50

100

150

200

250

300

0,00 10,00 20,00 30,00 40,00 50,00 60,00 70,00 80,00 90,00

PressureHead[Pa]

Flow rate [lt/s]

Fan II - experimental

Fan I - numerical

Fan II - numerical

Fig. 2Performance curves of Fan I and Fan II(solid lines: experimental data; dots: nu-merical data).Noise Control Eng. J. 57 (3), May-June 2009obtained by filtering the time-dependent Navier-Stokesequations. The filtering process effectively filters outthe eddies whose scales are smaller than the filter widthor grid spacing used in the computations. The resultingequations thus govern the dynamics of large eddies.The filtering operation of any flow variable isdenoted with an overbar in Eqn. (1), and defined as theconvolution integral of the field with a filter kernel G.Filtering substantially reduces the amplitude of thehigh-frequency spatial Fourier components.

x = DxGx,xdx 1

Here, D is the domain of concern. The filtered form ofthe incompressible, unsteady continuity and transportequations for LES are

uixi

= 0 2

and

uit

+uiujxj

=

xjui

xj p

xiijxj

3

where ui is the flow velocity in i direction, p is pressure, is the fluid viscosity and ij is the subgrid-scale stress,which is defined as

ij uiuj uiuj 4

The subgrid-scale stresses resulting from the filteringoperation are unknown, and require modeling. Themajority of subgrid-scale models in use today are eddyviscosity models of the following form:

ij 1

3kkij = 2tSij 5

where ij is the Kronecker delta function, t is the

subgrid-scale turbulent viscosity, and Sij is the rate-of-strain tensor for the resolved scale.

Sij =1

2 uixj

+ujxi 6

A popular subgrid-scale model was proposed bySmagorinsky22 and further developed by Lilly23. In theSmagorinsky-Lilly model, the eddy viscosity ismodeled by

t = Ls2S 7

where Ls is the mixing length for subgrid scales and

S2SijSij. In this study a modified version of theSmagorinsky-Lilly model is employed, where Ls iscomputed as171

Ls = mind,CsV1/3 8

where =0.42, d is the distance to the closest wall, Csis the Smagorinsky constant and V is the volume of thecomputational cell. Lilly derived a value of 0.23 for Csfrom homogeneous isotropic turbulence in the inertialsubrange. However, this value was found to causeexcessive damping of large-scale fluctuations in thepresence of mean shear or in transitional flows. Cs=0.1 has been found to yield the best results for a widerange of flows; hence in this study the default value forCs is set to 0.1.

Computational mesh for individual fans comprisesapproximately 2.5106 control volumes (Fig. 3).Density of the control volumes is increased in thevicinity of the fan blades, where most of the soundemission occurs. Cell distribution is forced to be fineron the walls to resolve the boundary layer and in theneighborhood of the blade. The dimensionless walldistance y+ is kept about 1 over the whole propellersurface and the use of a wall model is omitted. Meshelements surrounding the impeller are structured andhexahedral, whereas unstructured tetrahedral elementsare used in the volute.

For aeroacoustic computation, flow has to besimulated with small time steps for a large time inter-val. This causes even with parallel processing a suddengrowth in computational time. In order to preventunfeasible CPU-time requirements, the number ofcontrol volumes is kept as low as possible. In order tocheck for the numerical error due to grid dependency,an additional simulation is performed with a finer gridinvolving 11106 control volumes. Both time-averagedand instantaneous results of the coarse mesh (i.e.2.5106) are in agreement with the results of the finemesh (i.e. 11106). For Fan II, the coarse mesh resultedin a pressure head of 50 Pa at a flow rate of 80 lt/ s,

Fig. 3Detail of the computational mesh.172 Noise Control Eng. J. 57 (3), May-June 2009whereas the fine mesh resulted in 48 Pa. Figure 4shows that the instantaneous pressure distributionspredicted with the coarse and fine meshes concur.

The computational domain is divided into twozones, one surrounding the rotating impeller and othersurrounding the stationary volute. Zones are coupledvia a sliding interface and mass balance is forcedacross the sling interface. In order to minimize inter-polation errors, the ratio of the control volumes acrossthe sliding interface is kept below 4:1. The employedboundary conditions are no-slip at the walls, constanttotal pressure at the inlet and constant static pressure atthe outlet. The computational domain is initialized withthe flow data obtained from a steady RANS simulation,in order to accelerate convergence. Spatial discretiza-tion is performed with 2nd order central differencingscheme and temporal discretization with 2nd orderimplicit dual time stepping scheme. The aerodynamicaland acoustic time steps are set equal as 1 104 s, i.e.about 1 of rotation of fan is simulated at each timestep. Accordingly, the maximum frequency resolved is5000 Hz.

Acoustic data are extracted from the LES results andrecorded after nearly ten rotations of the fans, so thatonly statistically steady data are evaluated in the acous-tic computation. After the statistically steady state isachieved, flow simulations are continued further forabout 5 revolutions of the fans, i.e. for 0.107 seconds.

3.2 Acoustic Computation

Flow induced noise of a fan consists of monopole,dipole and quadrupole sound sources. Monopolesources are originated by the volume displacement ofthe rotating blades. Monopoles create pure tones,which are negligible for flows with Mach numbers lessthan 0.6. The quadrupole sources are emitted due to the

Fig. 4Pressure distribution on the volute of FanII (color map shows the results of the finemesh (i.e. 11106 CV) and the solid con-tour lines show the results of the coarsemesh (i.e. 2.5106 CV)).

turbulent fluctuations of fluid elements away from the

solid surfaces. Quadrupoles inherit the characteristicsof the broadband noise and these are negligible as longas the Mach number remains less than 0.8. Pressureand viscous stress fluctuations on solid surfaces createdipole sources. Dipole sources involve both tonal andbroadband components. In this paper, only the dipolesare computed, since among the three types of soundsources (i.e. monopoles, dipoles and quadrupoles) thedipole terms dominate the sound emission in turboma-chinery operating at low Mach numbers24.

The input of aeroacoustic computation is thetime-dependent pressure fluctuations on surfaces,which are obtained from the flow simulation. The flowdata are used to calculate the acoustic source terms onthe right hand side of the inhomogeneous waveequation (Eqn. (9)).

2

t2 c0

22

xi2 =

xipijf fxj 9

Here, is density fluctuation, c0 is the speed ofsound in the mean flow and is the dirac deltafunction. The solid surfaces, on which dipole sourcesare calculated, are defined with the function fx , t as

fx,t = 0 in the solid body=0 on the boundary0 outside the solid body

10The compressive stress tensor is defined as

pij = pij ij 11

On the solid surfaces pressure forces are about tentimes larger than the viscous forces. Therefore thecompressive stress tensor is approximated to pijpij.

Dipole sources are created on the surfaces of theblades and casing. Since the blades are rotating at aconstant speed, the dipoles created on the blades causea tonal noise in far field at the blade passing frequencyand at its harmonics. On the casing, dipoles are createddue to turbulent motion with various length scales, likethe periodic excitation of the blade motion, vortexseparation at the trailing edge and boundary layerseparation. Sources on the casing are stationary andcontribute both to the tonal and broadband noise. Theexperimental measurements of the investigated fansshow that the tonal noise at the blade passing frequencyis negligibly small compared to the broadband noise.Hence, in this study, the rotating dipole sources are nottaken into account and only the stationary dipoles onthe casing are assigned as the acoustic sources. TheNoise Control Eng. J. 57 (3), May-June 2009stationary dipoles are fed into the software LMSSysnoise25, where the acoustic computation isperformed.

Aeroacoustic computation involves two steps: i)assigning the dipole sources on the acoustic mesh andii) solving for the propagation of the sound sourcesinside and outside the casing. Approximately sevennodes are required to resolve a wavelength in aeroa-coustic computation. Therefore, the numerical grid forthe acoustic computation is much coarser than the LESgrid. Hence, the time-dependent flow data at each nodeof the fine LES mesh are mapped onto the coarseaeroacoustic mesh via interpolation algorithms. Thetime-dependent data are then converted to frequencydomain and acoustic dipole sources are calculated.

Inhomogeneous wave equation is solved via a multidomain BEM analysis. The analysis consists of twomodels, which are the direct BEM interior and thedirect BEM exterior models (Fig. 5). Dipole sourcesare assigned on the interior model as discrete soundsources on the nodes of the acoustic mesh. Both modelsare linked at the openings of the duct, through a fluid-fluid coupling. The coupling satisfies the boundarycondition at the openings, equivalent to the ambientpressure boundary condition. The boundary conditionapplied on the duct surface is the rigid wall boundarycondition. Interior and exterior models are solvedsimultaneously.

A fictitious surface, which is used forpost-processing in both experiments and numericalcalculations, is shown in Fig. 6. The fictitious surface isin form of a rectangular box and surrounds the fansystem. Field points, i.e. nodes of the fictitious surface,are located in the acoustic far field. Acoustic signal areevaluated at the field points to compute sound pressureand sound intensity values. Sound pressure level isdefined as:

Fig. 5Acoustic coupling of the interior and exte-rior multi domain BEM models.173

SPL = 10 log10 p2pref2 , 12where p is the sound pressure at a field point. The refer-ence pressure in air is chosen as pref=210

5 Pa. Thesound intensity, which shows the average rate at whichsound energy is transmitted through a unit area, isdefined as

I =p2

0c13

Where 0 is mean density and c is the speed of sound.Correspondingly, the sound intensity level is definedas:

IL = 10 log10 I2Iref2 14The reference sound intensity is chosen as Iref

=1012 Watt/m2 in accordance with pref.

4 EXPERIMENTS

Acoustic measurements are performed in a fullanechoic room, which has a background noise level ofless than 20 dB. Dimensions of the anechoic room areso that the minimum measurable frequency is 63 Hz,which is beyond the frequency range of interest in thisstudy. The experimental setup is placed in thegeometrical center of the anechoic room. Both physicaland numerical setups include a reflective surface under-neath the fan. Figure 7 shows the experimental setupsand the reflective surface underneath these setups.

Two sets of measurements are performed for bothfans. First, sound intensity maps are measured on thegrid points of the measurement surface shown in Fig. 6.Sound intensity mapping is selected as a measurementtechnique, because the intensity vector defines theacoustic energy and radiation pattern which gives themaximum information about the acoustic field. In theexperiment, a five axis robot arm is used to control the

Fig. 6BEM model and fictitious surface foracoustic post-processing.174 Noise Control Eng. J. 57 (3), May-June 2009intensity probe with 50 mm spacer over the hypotheti-cal surface and intensity measurements are performedusing two-microphone technique. Microphone pair ismatched for phase and amplitude response andcalibrated for the frequency range of interest. Measure-ment setup is placed on a wire cage that divides thechamber in two vertical directions. Wire diameter andcage pattern provide a good suspension and also avoidscattering and refraction from the ground. Data fromthis measurement are evaluated in 1/12 octavefrequency spectra. These data are used to validate thenumerical prediction of directivity.

Secondly, sound pressure levels (SPL) are measuredat discrete microphone points in narrow bandfrequency spectra. For these measurements micro-phones are placed in acoustic far field. These data areused for a detailed validation, since SPL distribution innarrow band provides an insight to the characteristicsof the sound signal and indicates the frequencies whichare excited most by the acoustic sources.

5 RESULTS

5.1 Aerodynamics

Vorticity is one of the main causes of flow-inducednoise. Figure 8 shows the instantaneous distribution ofthe vorticity magnitude at a cross-section across theimpeller. Vorticity is produced mainly around theblades; especially on the trailing edge. Vorticity magni-tudes around the blades are of the same order in bothfans. In Fan I vorticity is transported further with theflow through the volute; however, in Fan II vorticity isdissipated immediately. In Fan I, large vorticity magni-tudes indicate a strong tongueblade interaction andpoor flow conditions through the volute due to thelimited area increase in rotational direction. Hot spotsupstream of the blades indicate that inflow is notaxisymmetric, which causes an unbalanced loading onthe blades.

Figure 9 shows instantaneous vorticity contours on alongitudinal cross section. A helical flow is visible inthe outlet pipe of the Fan I, which generates vorticity

Fig. 7Experimental set-up for both sound inten-sity and sound pressure measurements(left: Fan I, right: Fan II).

the ithrough the outlet pipe. On the other hand, large vortic-ity magnitudes are only visible in the neighborhood ofthe impeller in Fan II.

Vorticity contours indicate that powerful soundsources are distributed on the whole geometry in Fan I,whereas these are located only on the impeller in Fan II.Accordingly, one can conclude that Fan II has fewernoise sources than Fan I. Experimental measurementsare in agreement with that statement and show that theoverall sound power level of Fan II is 6 dB less thanFan I.

5.2 Aeroacoustics

Figures 10 and 11 show the comparison of thenumerical and experimental sound intensity maps atdifferent frequencies. Sound intensity maps aresketched at the cavity mode and at its first harmonic forboth fans, which are 210 and 420 Hz for Fan I, and 270and 520 for Fan II. Directivity of the acoustic signaldepends both on the type and position of the soundsources and on the material and shape of the casing,which surrounds the sound sources. At high frequen-cies (420 and 520 Hz) sound waves are directedthrough the two openings of the casing; i.e. inlet andoutlet regions. Sound intensity levels at the middle of

Fig. 8Instantaneous vorticity distribution across

Fig. 9Instantaneous vorticity distribution on a loNoise Control Eng. J. 57 (3), May-June 2009the top, left and right surfaces are low. At low frequen-cies (210 and 270 Hz) noise is directed towards inletrather than outlet.

In addition to the sound intensity measurements,SPL are measured at discrete microphone points innarrow band spectrum, to perform a detailed valida-tion. SPL spectra recorded at the middle field point ofthe outlet surface are shown in Figs. 12 and 13.Numerical calculations can predict the level of broad-band noise correctly. In Fig. 12, a shift in the numericalSPL curve both in frequency and amplitude is visible,especially at low frequencies. The first peak at 520 Hzis overpredicted; however, frequency and amplitude ofthe second peak at 840 Hz is predicted accurately.Numerical SPL prediction of Fan II matched theexperimental curve satisfactorily.

Experimental SPL curves are smoother than numeri-cal curves. The smoothness of the SPL curve is relatedto the amount of data used to obtain these results. In theexperiments, the acoustic signal is measured for about10 s. However, in the simulation the total time for theacoustic evaluation is about 0.107 s, which corre-sponds to about 5 rotations of the propeller. In theexperiments the acoustic signal of about 500 rotationsis evaluated for both fans. The frequency analysis is

mpeller a) Fan I, b) Fan II.

dinal cross-section a) Fan I, b) Fan II.ngitu175

performed with far less data in the simulation than thedata available in the experiment. Therefore, the numeri-cal SPL curve involves more fluctuations than theexperimental curve. If the acoustic computation iscarried out for a longer time, these fluctuations willdisappear, but the general shape of the curve willremain the same.

6 CONCLUSION

In the frame of this work, far field noise of two radialfans is predicted numerically and the numerical methodis validated with the help of experimental measure-ments. In the first step of the hybrid CAA method, the

Fig. 10Numerical and experimental sound intens(1/12 octave band)).

Fig. 11Numerical and experimental sound intens(1/12 octave band)).176 Noise Control Eng. J. 57 (3), May-June 2009time-dependent turbulent flow field is simulated viaLES. The pressure fluctuations are extracted from theflow field to calculate the dipole sources. The waveequation is solved via multi domain BEM to computethe far field acoustic signal. Experimental data are usedto validate both narrow band SPL spectra at discretemicrophone locations and sound intensity maps atdifferent frequencies. Numerical predictions for bothfans are in good agreement with experiments.

There are some discrepancies between the experi-mental and the numerical SPL curves, which can beattributed to the limited time span of the numericalacoustic signal and to the grid resolution in flow

aps for Fan I (left: 270 Hz and right: 520 Hz

aps for Fan II (left: 210 Hz and right: 420 Hzity mity m

simulations. The main limitation of this study is therequired computational power and CPU-time. For theacoustic computation LES is performed for about 10full revolutions of the impeller to achieve the statisti-cally steady state and than for 5 more revolutions torecord the acoustic signal. Throughout the flow simula-tion, a total number of 5000 time steps are solved forboth fans. Computations took nearly 2 months ofCPU-time in a 16-processor HP computer. Despite thelarge computational effort, some numerical errors dueto grid resolution and low acoustic time span are inevi-table.

Fig. 12Numerical and experimental sound pressuI).

Fig. 13Numerical and experimental sound pressuII).Noise Control Eng. J. 57 (3), May-June 2009A merit of this methodology is that the acousticprediction is based on detailed flow simulations. Sincethe aerodynamical mechanisms which generate thenoise sources are simulated, this information can beused to compare the aeroacoustical characteristics ofdifferent designs. For example, the comparison of LESresults of the investigated fans showed clearly that highvorticity levels appear through the whole flow domainin the noisier fan (Fan I). LES shows a correlationbetween the large vorticity magnitudes and the locationof powerful dipole sources. Accordingly, dipole sourcesare located only on the impeller in Fan II; whereas in

vel distributions in narrow band spectrum (Fan

vel distributions in narrow band spectrum (Fanre lere le177

Fan I, powerful dipoles occur on impeller, tongue,volute and outlet pipe. As a future work, this informa-tion may be utilized to reduce noise of existing fans orin the inverse design of low-noise turbomachinery.

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