AIAC-2005-092

AEROACOUSTIC NOISE PREDICTION OF AN AXIAL FAN IN A CIRCULAR DUCT WITH LES

Mehmet Çavuş

*

Istanbul Technical University

Istanbul, Turkey

Ergin Arslan

†

ARCELIK A.S.

Istanbul, Turkey

Esra Sorgüven

‡

Yeditepe University

Istanbul, Turkey

Aydın Mısırlıoğlu

§

Istanbul Technical University

Istanbul, Turkey

*

MSc student in Aerospace Engineering, Email: cavusme@itu.edu.tr

†

R&D Specialist, Email: ergin.arslan@arcelik.com

‡

Assistant Professor in Mechanical Engineering, Email: sorguven@yeditepe.edu.tr

§

Associate Professor in Astronautical Engineering, Email: misirli@itu.edu.tr

ABSTRACT

The aim of this paper is to present a computational

method to predict the aeroacoustic noise in ducted

fans. Here, the method is applied to an axial fan

running at 1980 rpm in a circular duct. Two

commercial softwares, FLUENT and LMS

SYSNOISE, are employed for this purpose.

Unsteady flow analysis is performed with Large Eddy

Simulation (LES) using FLUENT and then passed to

LMS Sysnoise in order to compute the acoustic

radiation. The numerically obtained acoustic field

around the duct is compared with the experimental

data and fairly good agreement is observed.

INTRODUCTION

Aeroacoustic noise generation and propagation

attracts ever increasing interest of both academic

and industrial community [2, 3]. The control of the

aeroacoustic noise is a principal concern in different

industries such as aerospace, automotive, and home

appliances. Usually, the most significant contribution

to the noise level comes from fans. Especially in

home appliances, the small and medium sized axial

and radial fans are the dominant noise emitters [7].

Recently, with the exponential growth in the

computer technology, computational methods in

aeroacoustics became a powerful tool for noise

prediction [6, 10]. This paper presents a

computational aeroacoustics method, which involves

the coupling of a flow solver and a wave-equation

solver. With the help of CFD, the time-dependent

turbulent flow data is obtained. Depending on

turbulent fluctuations, noise sources in the flow field

are computed. The propagation of the emitted noise

is computed via Helmholtz equation in interior and

exterior domains. Thus, the sound pressure level

distribution in both near and far fields is obtained.

A major advantage of this method is that, the whole

sound pressure level spectrum is calculated directly.

There is a rich literature concerning the prediction of

the discrete noise at the blade passing frequency or

the broadband noise [8, 9]. However, there is a lack

of a method, which can predict the sound pressure

level (SPL) at every frequency in the range of

interest as a continuous curve. The originality of the

presented method arises from the capability of

predicting the SPL distribution over the whole

frequency range of interest. SPL at the blade

passing, cavity resonance and structural resonance

frequencies are predicted with one computation.

The numerical computations are performed in mainly

two steps. In the first step, the turbulent flow is

computed with Large Eddy Simulation (LES). LES is

a good compromise for the sake of the accuracy and

CPU-time in comparison with Direct Numerical

Simulation (DNS) and Reynolds Averaged Navier

Stokes Equations (RANS) [4]. The commercial flow

solver FLUENT is employed for this task.

In the second step, the time dependent flow data is

fed into the aeroacoustic module where the wave

equation is solved and the noise propagation is

computed. Structural modal analysis is also

performed and taken into account. Sound

propagation is computed by the LMS SYSNOISE.

Not only the tonal noise radiation, representing Blade

Passing Frequency (BPF) and its harmonics, but

also the excitation at the cavity and structural

resonance frequencies are investigated. As a result,

the sound pressure and intensity levels are predicted

AIAC-2005-092 Cavus & Arslan

in near and far field. In order to validate the

computational method, numerical results are

compared with experimental data. Both the

aerodynamic and the aeroacoustic steps of the

method are validated.

In this paper, the method is applied to an axial fan,

which is running in a circular duct. Reynolds number

based on the velocity and the chord length at the tip

is about 72,000 and the tip Mach number is about

0.04. Thus, a turbulent, incompressible flow is

present.

The comparison of numerical results with

experiments indicates that the proposed

methodology inherits a high accuracy and potential

to apply to more complicated systems.

MODEL DESCRIPTION

The CAD model of the investigated test case is

shown in figure 1. The circular duct has an inner

diameter of 156 mm and a length of 960 mm. The

outer diameter of the fan is 125 mm. The axial fan

with a tip chord length of 77.5 mm, is running at 1980

rpm, which corresponds to a blade passing

frequency of 132 Hz. A motor, which is placed

upstream the fan, is driving the fan.

Figure 1: The CAD model of the test case

NUMERICAL METHODOLOGY

To predict the aeroacoustic signal in the far field,

first, the flow induced acoustic sources are computed

with a transient CFD analysis. Next, the computed

acoustic sources are transferred to an acoustic

module to compute the propagation of sound waves.

In the following, the CFD and aeroacoustic models

are described.

Computational Fluid Dynamics (CFD):

The finite

volume method based commercial flow solver

FLUENT is employed for the CFD analysis. FLUENT

is capable of solving unsteady, incompressible flows

on unstructured grids with different turbulence model

[14]. For Computational Aeroacoustics (CAA), LES is

preferred to Direct Numerical Simulation (DNS) and

to Reynolds Averaged Navier-Stokes (RANS)

simulation [5]. With the available computational

sources today, DNS is restricted to low Reynolds

number flows with simple geometries since it

requires very fine grid resolution and therefore very

high CPU-time [1]. RANS method is the fastest

approach, however fails in accuracy due to the fact

that it is based on averaging the flow variables [4].

LES is capable of resolving large scales of the flow,

which are more efficient than the small ones in

generating sound [11]. Thus, LES is the most

appropriate method for CAA.

The fan motion is modeled with the sliding mesh

approach, which is implemented in FLUENT. This

model is chosen in order to simulate the relative

motion between the rotating fan and the stationary

motor appropriately. The result of a steady Multiple

Reference Frames (MRF) simulation is employed as

an initial guess for the unsteady sliding mesh

simulation in order to speed up the convergence.

The time discretization scheme is second order and

implicit. The pressure-velocity coupling is calculated

with the SIMPLE algorithm. Bounded central

differencing scheme is used for discretization of the

convection. The boundary conditions employed in

the simulations are total pressure at the inlet and

static pressure at the outlet, which are equal to

101325 Pa. The subgrid scale model employed in

LES is the Smagorinsky-Lilly model, which is an

algebraic turbulence model.

Two simulations are performed with different

computational grids, having different resolutions. The

grid of the first simulation consists of approximately

570.000 control volumes, which is coarse for an

LES. However, it is aimed to establish a basis for

modeling flow-induced noise in complex geometries

with this simulation. The second simulation involves

a finer grid with about 2.2×10

6

control volumes. The

simulation with the coarse grid results in

dimensionless wall distance, y

+

, values as about 20,

whereas the fine grid decreases the y

+

values to

about 5. The figure below shows the surface grid on

the fan of the second simulation.

Figure 2: The computational grid on the fan (totally

about 2.2×10

6

control volumes)

The time step is set to match the required

aerodynamic and acoustic time resolutions. The time

step is 10

-4

s, which corresponds to a rotation of 1.2

degrees. A complete revolution of the fan is

performed each 300 time steps which takes

approximately 10 hours with 8 processors of SGI

2

Ankara International Aerospace Conference

AIAC-2005-092 Cavus & Arslan

Origin 2000 for the coarse grid simulation. It extends

to about 40 hours of CPU-time for the fine grid.

When the statistically steady-state condition is

reached, acoustic data sampling is started. This

typically takes five complete fan revolutions.

In the following, results of the LES are presented.

Figure 3 shows the instantaneous pressure

distribution on the suction and pressure sides of the

fan. Pressure contours on the blades are not axi-

symmetric, which is due to the asymmetric

positioning of the motor upstream the fan. The

contours especially on the pressure side exhibit

vortical structures, which are time-dependent. The

pressure fluctuation on any point on a blade is about

80 Pa.

Figure 3: Instantaneous pressure distribution on both

sides of the fan

Figure 4: Instantaneous vorticity distribution along

the pipe cross-section (upper: coarse grid, lower: fine

grid)

The instantaneous vorticity distribution along the

pipe cross-section is shown in figure 4 to point out

the dependency on grid resolution. The upper

snapshot is from the simulation with coarse grid

whereas the lower snapshot belongs to the

simulation of fine grid. Both simulations result in a

similar vorticity range. However, the vorticity levels

are still high downstream the fan in the fine grid

case. Thus, turbulent structures are not dissipated

immediately as in the coarse grid case; but carried

further with the flow.

Aeroacoustics:

The aeroacoustic modeling is

performed with the aeroacoustic module of the

vibroacoustic solver LMS Sysnoise. Sysnoise is

capable of solving wave equation in interior and

exterior domains with different discretization

techniques like Boundary Element Method (BEM)

and Finite Element Method (FEM) [13].

The input for aeroacoustic module is time-dependent

pressure and velocity data, which is obtained from

the CFD solution. The flow data from FLUENT is

used to calculate the acoustic source term, which is

on the right hand side of the wave equation. The

aeroacoustic grid, on which the wave equation is

discretized, is much more uniform and coarser than

the CFD grid. The interpolation between two grids is

performed by an in-house developed post processor.

The turbulent flow around a ducted axial fan induces

three types of acoustic sources: monopoles, dipoles,

and quadrupole [12]. In this test case, the dipoles are

the dominant sources since the fan blades are thin

and the flow is subsonic. Therefore, only the dipoles

are taken into account. On the fan blades, the

dipoles are rotating, which are responsible for the

tonal noise at the Blade Passing Frequency (BPF)

and its harmonics. The contribution of the rotational

dipoles is computed based on the force fluctuations

on one blade.

In order to model the interior and the exterior

domains simultaneously, the Multi-Domain BEM

analysis is performed. The analysis consists of two

models, which are the Direct BEM Interior, and the

Direct BEM Exterior models. Both models are linked

at the openings of the duct, through a fluid-fluid

coupling. The coupling automatically satisfies the

boundary condition at the openings, which is

equivalent to ambient pressure boundary condition.

The boundary condition applied on the duct surface

is the rigid wall boundary condition. The stationary

dipole sources on the duct surface are defined as

discrete sound sources on the nodes of the acoustic

grid.

An additional coupled structure - fluid model is set up

in order to simulate the tonal fan noise at the BPF

and its harmonics and the dipole structural

contribution. In this coupled model, the inputs to

Sysnoise are the force fluctuations on a fan blade

and the structural modes of the duct from a structural

modal analysis.

To finalize the aeroacoustic computation, the results

of the Multi-Domain BEM model and the coupled

structure - fluid model are superposed. The achieved

aeroacoustic solution involves:

3

Ankara International Aerospace Conference

AIAC-2005-092 Cavus & Arslan

• Tonal fan noise at BPF and its harmonics

• Excitation of the cavity modes

• The dipole structural contribution

Structural Modal Analysis:

The experimental setup

does not include any structural attachments from the

fan to the duct directly. The structural contribution

arises only from the dipoles and is very weak

compared to the fan source and to the dipolar direct

contribution.

In order to identify the natural frequencies of the

duct, which is made of steel, structural modal

analysis is performed and validated. The

experimental and numerical results are in good

agreement as shown in figure 5.

-80

-40

0

40

80

100 200 300 400 500 600 700 800

Frequency [Hz]

FRF [m/s^2/N]

Experimental

Numerical

Figure 5: Comparison between experimental and

numerical results of structural modal analysis

Acoustical Modal Analysis:

In order to determine the

natural frequencies of the duct cavity, an additional

analysis is performed with the LMS Sysnoise and it

is experimentally validated. In the numerical model, a

multi domain BEM model is set up and the natural

frequencies of the duct cavity are excited by a

discrete monopole source. The strength of the

source is specified arbitrarily. The experimental and

numerical results are given in figure 6.

30

40

50

60

70

80

90

100 200 300 400 500 600 700 800

Frequency [Hz]

SPL [dB] ref = 20 uPa

Experimental

Numerical

Figure 6: Comparison between experimental and

numerical results of acoustical modal analysis

EXPERIMENTAL METHODOLOGY

Two sets of acoustic measurements are performed

in the anechoic room. First, sound pressure levels at

different microphone positions in the acoustic far

field around the duct are measured in narrow band

spectrum. Secondly, the sound pressure and

intensity are mapped in 1/3 octave band frequencies

in order to determine the directivity of sound on a

measurement field. The sketch of the experimental

setup for intensity measurement is illustrated in

figure 8. The duct is placed at the center of the

measurement field and the acoustic data is obtained

by an intensity probe, which collects pressure data

on the nodes of the measurement field.

Unfortunately, the experimental setup does not allow

the probe to scan the under hood of the system

because of the supporting boxes as shown in figure

7.

Figure 7: SPL measurement setup

Figure 8: Sketch of the sound intensity measurement

setup

RESULTS

The numerical and the experimental color maps of

the sound pressure level distribution in 1/3 octave

band are presented in the figures below. These

figures designate the directivity of sound at 125 Hz

and 250 Hz. The distributions in both numerical and

4

Ankara International Aerospace Conference

AIAC-2005-092 Cavus & Arslan

experimental maps are similar. The discrepancies

can be attributed to the experimental setup. In the

numerical analysis, only the fan and the duct are

taken into account. The wooden box, supporting the

circular duct, is neglected in the computations. Since

the reflections due to the box are not considered, the

numerical results exhibit a perfect symmetry around

the duct. However, the reflective surfaces under the

duct cause asymmetry and higher noise levels.

Figure 9: Numerical sound pressure level distribution

at 125 Hz. (1/3 Octave band)

Figure 10: Experimental sound pressure level

distribution 125 Hz (1/3 Octave band)

Figure 11:Numerical sound pressure level distribution

at 250 Hz (1/3 Octave band)

Figure 12: Experimental sound pressure level

distribution at 250 Hz (1/3 Octave band)

Figure 13: The experimental and numerical sound

pressure level distribution (coarse grid) at a point in

far field (narrow band, ∆f=2 Hz)

5

Ankara International Aerospace Conference

Figure 13 represents the sound pressure level

distribution in the narrow band at a far field point,

with a distance of about 0.75m from the center of the

fan. The numerical curve sketched in this figure is a

AIAC-2005-092 Cavus & Arslan

result of the LES with the coarse grid. The sound

pressure level of the tonal noise at the BPF (132 Hz)

is predicted with a high accuracy by the numerical

model. The first cavity mode is at 162 Hz, and the

second mode is at 324 Hz. Both cavity modes are

visible in both numerical and experimental data. The

tendency of both curves are similar, however there is

a deviation in the amplitudes. The numerical SPL

values are below the experimental values.

The amplitude of the numerical SPL-curve

decreases for frequencies higher than 350 Hz

rapidly. This is caused due to the insufficient

resolution of turbulent structures in the flow. Since

the spatial filter size in LES is too large to capture

the small-sized, high frequent turbulent structures,

the accuracy in noise prediction at high frequencies

decrease.

Figure 14: The experimental and numerical sound

pressure level distribution (fine grid) at a point in far

field (narrow band, ∆f=2 Hz)

The acoustic result of the LES with the fine grid

shows a better agreement with the experimental

data, as seen from figure 14. Finer grid resolution

enables the simulation of a greater range of turbulent

scales, and results in a higher accuracy for a large

range of frequencies.

The SPL spectrum shows peaks at

• The BPF (132 hz)

• The first cavity mode of the duct (162 Hz)

• The second cavity mode of the duct (324 hz)

• The third cavity mode of the duct (486 Hz)

No peaks at the structural resonance frequencies are

visible since the duct is made of steel and the

acoustic sources are too weak to excite the

structure.

CONCLUSION

Flow induced noise generated by an axial fan

running in a circular duct is predicted. The time-

dependent turbulent flow is simulated with FLUENT

and the aeracoustic field is computed with the LMS

SYSNOISE. The comparison between the numerical

and the experimental data shows a good agreement,

and indicates that the numerical method can be

applied to more complex geometries.

References

[1] Andersson, N., Eriksson L.E., Davidson L.,

Investigation of An Isothermal Mach 0.75 Jet and Its

Radiated Sound Using Large Eddy Simulation and

Kirchoff Surface Integration, Int. J. of Heat And Fluid

Flow, Vol. 26, p: 393-410, 2005.

[2] Maaloum, A., Koudri, S., Rey R., Aeroacoustic

Performance Evaluation of Axial Flow Fans Based

on the Unsteady Pressure Field on the Blade Surface,

Applied Acoustics, Vol. 65, p: 367-384, 2004.

[3] Envia, E., Wilson, A., Huff, D.L., Fan Noise: A

Challenge to CAA, Int. J. of Computational Fluid

Dynamics, Vol. 18, p: 471-480, August 2004.

[4] Sorgüven, E., A Computational Aeroacoustic Method

Using Large Eddy Simulation And Acoustic Analogy,

Phd thesis, University of Karlsruhe, Germany, 2004.

[5] Magagnato, F., Sorgüven E., A., Gabi M., Far Field

Noise Prediction by LES and FWH Analogy, 9

th

AIAA/CEAS Aeroacoustics conference and exhibit,

South Carolina, 2003.

[6] Tournour, M., El Hachemi, Z., Read, A., Barone F.,

Investigation of The Tonal Noise Radiated By

Subsonic Fans Using The Aeroacoustic Analogy,

Proceedings of Fan Noise Symposium, CETIM

Senlis, 2003.

[7] Chung, K., Kim, K.Y., Na S.U., Development of Fan

Design Program for High Performance and Low

Noise Fan, Proceedings of Fan Noise Symposium,

CETIM Senlis, 2003.

[8] Cho, Y., Moon, Y.J., Discrete Noise Prediction of

Variable Pitch Cross-Flow Fans by Unsteady Navier-

Stokes Computations, Journal of Fluids Engineering,

Vol. 125, p: 543-550, 2003.

[9] Hanson, D., Broadband noise source studies for a fan

with coupled rotor/stator, 9

th

AIAA/CEAS

Aeroacoustics conference and exhibit, Hilton Head,

South Carolina, 12-14 May 2003.

[10] Mendonça, F., Allen, R., Charentenay, J., Lewis, M.,

Towards Understanding LES and DES for Industrial

Aeroacoustic Predictions, International Workshop on

‘LES for Acoustics’, DLR Göttingen, Germany,

2002.

[11] Mankbadi, R., Review of Computational

Aeroacoustics in Propulsion Systems, Journal of

Propulsion and Power 15 (4), p: 504-512, 1999.

[12] Goldstein, M.E., Aeroacoustics, McGraw-Hill Book

Company, New York, 1976.

[13] LMS Sysnoise Rev 5.6: Computational

Vibroacoustics, User’s Manual, and LMS

International.

[14] Fluent 6.2, User’s Guide, FLUENT Inc.

6

Ankara International Aerospace Conference

## Σχόλια 0

Συνδεθείτε για να κοινοποιήσετε σχόλιο