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ABSTRACT

In this paper, side window buffeting analysis of

the Sedan car model is studied computationally.

Flow over an open side window in car exhibits

similar characteristics as the flow over an open

cavity. Hence, a grazing flow simulation over an

overhang cavity is done as a benchmark. The

benchmark results, frequency and sound pressure

levels of feedback and resonance modes, matched

well with the available experimental data. The

analysis is done in two parts. First a steady state

solution is obtained using Reynolds Averaged

Navier Stokes (RANS), and then the computed

flow field information is used as input for Non

Linear Acoustic Solver (NLAS). After validation,

the buffeting spectrum generated from flow over

open front side window of generic Sedan car

model is analyzed. Vortex generation at the root

of A-pillar, its propagation and growth

downstream and its breaking mechanism at the B-

pillar are well captured. The obtained frequency

of buffeting at the location of the driver's ear and

the corresponding sound pressure level (SPL) are

as expected when compared to general Sedan car

side window buffeting spectra. The acoustics

computations have been performed on the

supercomputer Eka in 70 hrs real time on 15

million cells mesh using 128 processors. The

scalability studies have also been carried out for

the acoustic calculations.

Keywords: Cavity noise, Buffeting, Feedback

resonance, Helmholtz resonance, CFD

INTRODUCTION

Buffeting is a type of cavity noise when the entire

passenger compartment acts as a cavity in case of

an open sunroof or a side window. (Rossister,

1964, Ota et al., 1994). The unstable shear layer

generated at the upstream edge of the cavity is the

cause of the cavity noise. Disturbances (in the

form of vortices) are created at the front edge of

the opening (in the present case the A-pillar) and

are convected along the flow. When they strike

the rear end (the B-pillar) a pressure wave is

generated which propagates inside and outside

the passenger compartment (the cavity). When

the wave reaches the front end it triggers another

shedding of the disturbance. This periodic

occurrence results in the shear layer generating a

specified buffeting frequency. In automobiles this

frequency is very low (~ 25Hz) but the intensity

is very high (> 100 dB) (Sovani et al., 2003). This

fluctuation is felt by the passengers as a pulsating

force which can be very troublesome. Hence, it is

important to keep buffeting in view while

designing an automobile and also to understand

the underlying physics of cavity noise.

In the past, ample amount of work is done to

understand aerodynamic noise generated by flow

over open cavities. Rossister, 1964, had done a

pioneering work, where he did extensive

Buffeting Noise Computations for an Open Side Window

Ashish Singh

Deepanshu Rajvanshi

CRL Ltd,

CRL Ltd,

Pune, Maharashtra, India.

Pune, Maharshtra, India.

ashish@crlindia.com

deepanshu.rajvanshi@crlindia.com

Kishor Nikam

CRL Ltd,

Pune, Maharashtra, India.

kishor@crlindia.com

Proceedings of the 37th National &4th International Conference on Fluid Mechanics and Fluid Power

December 16-18,2010,IIT Madras,Chennai,India.

FMFP10 - AM- 13

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experiments to understand the physics of noise

sources emanating from flow over open cavities.

It was observed that apart from random pressure

fluctuations, discrete tones related to feedback

loop and resonance with cavity mode were

present for different length to depth ratio of

cavity. Another example is the case of flow over

overhang cavity (Henderson, 2004), where both,

feedback and cavity mode, are seen to be present

at the same time. Cavity modes occur when the

frequency of periodic oscillation matches with

Helmholtz cavity mode (Howe, 1997). The sound

pressure level exceeds 110 decibels due to these

dominating modes. The noise produced by

overhang cavity at low speeds has resemblance

with aerodynamic noise generated by wheel bay,

weapon bay, sunroof and side window buffeting

depending upon different free stream conditions.

In case of automobiles, the car compartment acts

as a cavity and these modes emit temporally

varying forces of pressure fluctuations of high

intensity and low frequency, which is in turn

known as buffeting. In automotive engineering,

buffeting refers to an undesirable noise. This

causes passenger discomfort which is the primary

cause of investigation of buffeting. Similar to

overhang cavity noise level, buffeting noise

emanating from open sunroof or open side

window of a passenger car may exceed 110 dB.

Ota et al., 1994, performed an early CFD

simulation of sunroof buffeting (a type of cavity

noise), where a 2D flow-field along the symmetry

plane of a passenger car was considered. Another

early 3D sunroof buffeting study was carried out

by Ukita et al., 1997, on a larger model size. With

increase in the computational capacity, more

detailed CFD models have been studied. Karbon

et al., 2002, did sunroof buffeting studies using

complex vehicle models that included detailed

representation of the passenger compartment.

Recently, Ashish et al., 2008, performed 2D

CFD/CAA simulations for general sedan car

sunroof buffeting, and, detailed the number of

modes of oscillations based on Rossister’s

formulation. Also, the focus was to modify rear

end of sunroof for noise suppression rather than

improving the front end. Significant improvement

is shown in noise levels by avoiding vortex

breakdown at rear end.

Sovani et al., 2003, and Chang-Fa, 2004,

performed a number of simulations to study side

window buffeting and the effect of various

parameters like velocity, yaw angle, receiver

location and turbulence models on the peak

frequency and SPL. Their study found out that

CFD predictions of buffeting SPL and frequency

matched well with the experimental results. SPL

was predicted within an accuracy of 4 dB and the

frequency within 1 Hz. Moreover, the trends of

buffeting frequency and SPL with the variation of

other parameters also matched well with the

experimental results.

Conducting experiments for acoustic noise

measurements for full car model like SEDAN Car

are highly cost and time intensive. To overcome

experimental set up challenges, researchers are

now using Computational Aero Acoustics (CAA)

approach in conjunction with Computational

Fluid Dynamics (CFD) to simulate aerodynamic

noise and sound pressure level. CAA simulation

requires time dependent computations on highly

dense grid near sound sources as well as in the

computational domain. These simulations are

highly compute intensive which is provided by

High Performance Computing (HPC) platform

like CRL’s supercomputer facility, EKA.

The purpose of the present study is to perform

computational aeroacoustic simulations with high

accuracy and high end scalability.

This study goes through baseline validation first

(which increases the confidence in CAA

methodology), for which the test case (Fig. 1a) is

selected from NASA’s 4

th

CAA workshop in

2004. The detailed experimental data is available

from Henderson, 2004, to compare the results. In

the present paper, comparison is done with the

work of Henderson, 2004.

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After baseline validation, the present paper

investigates side window buffeting noise. Sedan

car vehicle configuration is considered in which

front side window is kept completely open. The

methodology for predicting the buffeting

frequencies and the corresponding sound pressure

levels (SPL) is discussed first followed by a

discussion of meshing considerations and solver

settings. The simulations were carried out using

CFD++ and CAA++. A speed up study is also

done for CAA simulations to quantify effects of

parallel computing on simulation time.

BENCHMARK PROBLEM THEORY AND

DESCRIPTION:

The overhang cavity as a benchmark problem is

shown in Fig. 1a. The dimensions are taken to be

similar as used in experiments (Henderson,

2004). Flow is fully turbulent over the cavity with

inlet free stream velocity of 50m/s. Three receiver

points, at the center of left, right and bottom

cavity wall (Fig. 1b) are considered to validate

frequency spectrum with experimental results.

(a)

(b)

Fig. 1: (a) Benchmark problem at 4

th

CAA

workshop: Overhang Cavity (b) Receiver

Locations

Overhang cavity is the practical example of

producing discrete tones related to longitudinal

waves (fluid dynamic oscillation) and transverse

waves (Helmholtz resonance) for low Mach

number flows. The frequency of fluid dynamic

oscillations is approximated by Rossister, 1964,

as

kMnLUfs

o

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eff

H

Vd

Ac

f

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(represented by '). Statistical mean (RHS) can be

obtained by steady RANS simulations whereas

the equation can be solved for perturbations

present in LHS of the equation. NLAS, like

CFD++, uses a finite-volume spatial

discretization, in which a continuous integral of

the conservation variable fluxes over an arbitrary

control volume is replaced via a discrete

representation of the flux across each face.

(4)

Spatial derivatives, used to compute both viscous

and inviscid flux terms, are first computed via a

least-squares approximation at the element or

control-volume vertices. Then a polynomial

representation of solution is constructed, whose

integral value matches that of the initial

(piecewise constant) cell volume data.

The smallest wavelength structures are modeled

and so there is no coupling or feedback from the

highest frequency acoustics waves, but large

scale vortical structures can be resolved directly

and those larger scales will interact with any

acoustic waves in the system.

The time step for NLAS computation is set to be

2x10

-5

, which provides sufficient stability on

existing mesh. This time step is not small enough

to resolve all higher frequencies; however, it is

acceptable for this kind of problem where first

few peaks corresponding to longitudinal and

transverse waves are dominant. The reduced

domain for NLAS solver is decomposed into 4

parts to run it in parallel on 4 processors. Pressure

signals are recorded at specified receiver

locations and Fast Fourier Transform (FFT) tool

is used to get frequency spectrum from simulated

data. Baseline pressure history is shown in Fig. 4

a-d for all three receiver locations. The Sound

Pressure Level (SPL) is calculated using

following relation

ref

ppLogSPL

(5)

Where, p is the perturbation amplitude and p

ref

=

20e-06 Pa.

RESULTS & DISCUSSIONS

The frequency and SPL are compared with the

work of Henderson, 2004. Frequency and SPL,

obtained from pressure history (Fig. 4a) are

compared in Table 1. Figures 4 b-d shows the

noise spectrum obtained at right left and bottom

centers respectively.

(a)

(b)

(c)

iitii

S

t

S

t

SqnnFdSqnnFdSqnnF

i

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(d)

Fig. 4: (a) Pressure History (b) Spectra at right

center (c) Spectra at left center (d) Spectra at

bottom center

Table 1: Comparison of baseline results with

experiment

In the present study, first tone at 1678 Hz is very

close to experimental prediction, 1624 Hz, but

SPL is over predicted by 15-20 dB, while

compared with test data (Henderson, 2004). The

reason for this over prediction in SPL may be

attributed to simulation of a practical 3D problem

with 2D approach where relaxation effects in the

span, coupled with physically spherical spreading

of acoustic wave radiation causes a higher

attenuation of these wave energies. There are no

discrete tones observed corresponding to

feedback loop resonance and cavity resonance

(Helmholtz mode) but a single tone around 1678

Hz is numerically obtained. In experiments,

frequency of these tones was found to be 1624

and 1504 Hz respectively (at all receiver

locations). It can be attributed that in present

numerical analysis, both higher tones, feedback

and cavity resonance, are coinciding at 1678 Hz.

Henderson, 2004 also stated in his work that the

first cavity mode of this kind of cavity occurs at

3016 Hz which is based on the assumption that

acoustic wavelength is four times the cavity

depth. This can be obtained from the Helmholtz

Eq. (2). In the same context, the second mode of

cavity can be assumed half of the 3016 Hz

(1503). Obtained frequency 1678 Hz (for cavity

mode) is closer to the second mode 1504 Hz in

experiments. Hence frequencies in current

analysis (1678 Hz) are very close to experimental

value (1624 Hz, 1504 Hz) for feedback and

cavity resonance. Two separate peaks

corresponding to two different tones may be hard

to predict numerically if they are resonating with

each other.

SEDAN CAR - PROBLEM DESCRIPTION

AND METHODOLOGY

Only one vehicle configuration is considered in

which the side window corresponding to the A-

pillar is kept completely open as shown in Fig 5.

Inside features of the car, e.g. chairs and human

dummies are not considered here. So the model is

kept as simple as possible to behave like a hollow

cavity. The methodology for predicting the

buffeting frequencies and the corresponding

sound pressure levels (SPL) is discussed first

followed by a discussion of meshing

considerations and solver settings. The

simulations were carried out using CFD++, a

finite volume based solver.

Fig.5: Sedan Car model with front window open

Frequency (

Hz)

SPL (dB)

Present

1678

138

Exp

(Henderson,

2004)

1624 118

eive

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GRID GENERATION

In the present study, the total mesh size is 15

million cells without boundary layer mesh. The

vehicle is placed inside a parellopiped

computational domain (as shown in Fig 6). The

inlet and outlet of the domain are at an

approximate distance of 3 and 6 body lengths

from the front and rear face of the model

respectively. This is chosen so that the incoming

flow is developed to a good extent and the eddies

downstream of the vehicle are captured well. The

passenger compartment of the car is connected to

the external flow via the open window fluid layer.

The vehicle surface is meshed in detail (Fig 7 a,

b) to capture the development of the flow.

Especially, the areas of A-pillar, open window

and B-pillar are modeled to a great detail as

shown by Fig 7 b-c. The cell edge length in these

areas is around 3 mm, sufficient enough to

resolve the wavelength of interest. This spacing

insures at least 5 cells per wavelength. In the

passenger compartment the edge length is around

15 mm. The model does not include underhood

and underbody details as its effect on the

buffeting frequency is assumed to be negligible.

Fig 7a shows the surface over the model. Fig 7b

shows the area where the mesh is refined in order

to capture the shear layer phenomena. Fig 7c is

the enlarged view of Fig 7b.

Fig. 6: Computational domain with boundary

conditions

(a) Surface mesh of the model

(b) Refined mesh near the open window

region

(c) Magnified view of the selected area in

Fig.7: Meshing details

SOLVER SETTINGS AND BOUNDARY

CONDITIONS:

The two equation cubic k-

turbulence model is

used for the steady RANS closure. This case was

run on 64 processors for 500 iterations in which

Inflow

Outflow

Road

Side wall

Top wall

wall

Car body

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convergence (of the order of 10

-5

) was achieved.

The approximate turnaround time for this

simulation was around 8 hrs. The steady state

data obtained is then used to initialize the

unsteady runs (NLAS). For the NLAS run, the

time step chosen was 0.0004s. This transient

simulation used 128 parallel processors and the

turnaround time was around 70 hrs.

RESULTS AND ANALYSIS

A.Steady State

The pressure contours in the form of coefficient

of pressure (Cp) for the steady state simulation

are shown in Figs 8 a, b. Cp distribution is as per

expectation but in the absence of any

experimental data it is difficult to comment more

on it.

(a) Front view

(b) Rear view

Fig. 8: Pressure Contours over the car

Figures 9 (a-f) shows the development of the

vortex at the A-pillar region of the open window,

its gradual convective growth and finally it’s

breaking on the B-pillar. Pictures are shown at

cut section planes placed 0.2 m apart. It is

observed that the inception of the instability at the

lower end of A-pillar contains very high energy

in comparison to others generating from length of

A-pillar. Root cause of buffeting is the breaking

mechanism of this instability in the form of high

energy vortex hitting at the B-pillar, emission of

pressure waves (acoustic waves) at B-pillar, their

resonance with different harmonics of Helmholtz

mode of car cavity, and new instability generation

at the A-pillar again to continue the process.

(a)

(b)

(c)

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(d)

(e)

(f)

Fig.9: Convection and development of the

vortex, cut planes with an interval of 0.2 m, a-f

Vortex breakdown at B-pillar is captured in more

detail in figure 10.

Fig.10: Vortex breaking at B-pillar

B.Transient Analysis

NLAS solver is used for transient CAA

simulations. Pressure data is recorded at every

time step and FFT is applied on the acoustic

pressure obtained at receiver location. The 12

th

octave band produced spectrum of SPL vs.

frequency as shown in Fig 11. The peak SPL,

124dB corresponding to 28 Hz is as expected

when compared to available data.

Fig. 11: SPL vs. frequency plot showing peak

SPL and the corresponding frequency

SPEED UP STUDY

All simulations have been performed in parallel

on the supercomputer Eka. Figure 12 shows ideal

and actual speed up curves for 64 to 512 CPUs.

In the next plot (figure 13) the parallel efficiency

F~ 28 Hz, SPL ~ 124 dB

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of the solver is shown for Eka architecture. For

the defined range of CPUs, speed up is almost

parallel to ideal with over 90% efficiency for

entire CPU range.

Fig. 12: Ideal and actual speed up of CFD++ on

Eka architecture

Fig. 13: Parallel efficiency of CFD++ on Eka

architecture

CONCLUSIONS

Computational Aero Acoustic validation is done

using overhang cavity problem. Feedback and

Helmholtz modes are captured at the same

frequency with less than 3% error when

compared to experiments. Sound Pressure Levels

were over predicted and the root cause is

attributed to 2D simulation of a practical 3D

turbulent problem. Furthermore, side window

buffeting analysis of the Sedan car model using

CFD and CAA has been investigated in this

paper. The methodology, corresponding grid

resolution, solver settings were discussed in

detail. Predicted buffeting frequency and SPL are

closer to buffeting levels of general sedan car

model (28 Hz, 124 dB). Parallel simulations of

CFD and CAA methods accelerated the

turnaround time for complete CFD/CAA analysis.

Full transient simulation of 15 million mesh with

sufficient time resolution took around 70 hrs on

128 processors to generate the noise spectrum.

ACKNOWLEDGMENTS

We are thankful to Metacomp Technologies for

their coherent support in this work. Also, we

would like to thank Computational Research

Laboratories for providing Supercomputing

facility, Eka.

REFERENCES:

Ashish Singh, Saravana Kumar, J. S. Rao., 2008.

Numerical Analysis of Sunroof Buffeting, SAE

paper 2008-28-0059.

Batten, P, Ribaldone, E, Casella, M,

Chakravarthy, S, 2004. Towards a Generalized

Non-Linear Acoustics Solver, AIAA 2004-3001,

10

th

AIAA/CEAS Aeroacoustics Conference.

CFD++ User Manual, Version 7.5, 2008.

Metacomp Technologies, USA.

Chang-Fa An, Alaie S.M, Sovani S.D, Scislowicz

M.S and Kanwerdip Singh., 2004. Side Window

Buffeting Characteristics of an SUV, SAE paper,

2004-01-0230.

0

1

2

3

4

5

6

7

8

9

0 100 200 300 400 500 600

Speed up

Number of CPUs

Speedup Curve

Ideal Speed Up

Actual Speed Up

80

85

90

95

100

105

110

115

120

0 100 200 300 400 500 600

Parallel Efficiency

Number of CPUs

Parallel Efficiency vs No. of CPUs

Parallel Efficiency

Page 11 of

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Henderson, B., 2004. Problem 2 of Category 5 –

Sound Generation by Viscous Flows, 4

th

Computational Aero Acoustics (CAA) Workshop

on Benchmark Problems, NASA/CP-2004-

212954.

Hendriana D, Sovani, S.D and Schiemann M.K.,

2003. On Simulating Passenger Car Side Window

Buffeting, SAE paper, 2003-01-1316.

Howe, M.S., 1997. Edge, Cavity and Aperture

Tones at Very Low Mach Numbers, J. Fluid

Mech., vol.330, pp. 61-84.

Karbon, K., Kumarasamy, S., Singh, R., 2002.

Applications and issues in automotive

computational aeroacoustics, 10th Annual

Conference of the CFD Society, of Canada,

Windsor, Canada, June 9-11.

Nelson, P. A., Halliwell, N. A., and Doak, P. E.,

1981. Fluid Dynamics of Flow Excited

Resonance, Part I: Experiment, Journal of Sound

and Vibration, vol. 78 (1), pp 15-38.

Nelson, P. A., Halliwell, N. A., and Doak, P. E.,

1983. Fluid Dynamics of Flow Excited

Resonance, Part II: Flow Acoustic Interaction,

Journal of Sound and Vibration, vol. 91(3), pp

375-402.

Ota, D.K., Chakravarthy, S.R., Becker, T., and

Sturzenegger, T., 1994. Computational study of

resonance suppression of open sunroofs, Journal

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Rossister, J.E., 1964. Wind Tunnel Experiments

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Subsonic and Transonic speeds, Aero. Res.

Counc. R & M., 3438.

Ukita, T., China, H., and Kanie, K., 1997.

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flow visualization, SAE paper 970407.

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