Aeroacoustics Simulation of an

Automotive A-Pillar Rain Gutter

Hervé Dechipre and Dr. Michael Hartmann

Group Research, Automotive Techniques, CAE

Volkswagen AG, Wolfsburg, Germany

SYNOPSIS

The recent successes achieved to reduce tire and engine noise have resulted in a higher

contribution of the wind noise in the overall perception of noise by car passengers. Up to

now, wind noise has been largely assessed by wind tunnel testing; however there is an

increasing need for numerical methods in order to evaluate the design as early as possible. If

direct noise computation (DNC) is not reachable for industrial applications, hybrid methods

based on acoustical analogy or stochastic modelling have already demonstrated good

aptitudes. Nonetheless, as a first step, an accurate CFD simulation must be performed. In

this study, the flow-induced noise of an automotive rain gutter has been investigated for

Reynolds numbers based on the rain gutter height between 20 000 and 130 000. As both

rain gutter’s height and oncoming flow’s direction vary along the A-pillar, two configurations

have been designed to investigate these effects separately. In addition, two different profile

sections of rain gutter were tested. This paper mainly focuses on the computation of the

structures of the flow and surface pressure level; the propagation of sound in far field will be

addressed in a further publication. The topology of the flow was assessed using steady

RANS computation. Unsteady SAS-SST and DES models have been used to compute

surface pressure fluctuations. As the flow was stable for the conventional SAS-SST model,

forcing terms were used to switch to unsteady mode. Experimental data will be presented

and used to validate the results of the numerical simulations.

1. Introduction

Various aspects determine the comfort inside a vehicle. One of them is the interior noise

experienced by the passengers. Due to successes in reducing motor and road/tire noises,

airflow induced noise (also called ‘wind noise’) becomes more and more important. Over 100

km/h wind noise is generally the dominant noise source and can make it difficult to converse

or listen to the radio, but it can also add fatigue on a long highway trip. A potential buyer

might even consider high wind noise levels as poor design or build quality and may lead to

dissatisfied customers. Therefore, car manufacturers have to pay close attention to wind

noise and try to minimise it.

If one could expect that low drag vehicles would also have low wind noise level; this is not

found to be true in practice. One explanation for this lack of correlation comes from the fact

that aerodynamic drag depends largely of the airflow over the rear of the vehicle and its wake

EASC 2009

4th European Automotive Simulation Conference

Munich, Germany

6-7 July 2009

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while interior wind noise depends largely on details of the exterior airflow around the A-pillar

and windscreen. Small openings or gaps around doors, windows, or many other components

on the outer body such as roof-racks or side-view mirrors (or under floor) may largely

contribute to wind noise; conversely they will have little or no effect on aerodynamic drag.

A prominent feature contributing to the overall wind noise level in the cabin is the A-pillar rain

gutter (fig. 1). Like a long narrow channel along the A-pillar, its purpose is to collect and drain

the water that would otherwise flow from the windshield and past the A-pillar along the side

windows; reducing then the visibility.

varies along the A-pillar. Almost perpendicular (90°) at the base of the A-pillar, the oncoming

flow can reach an angle with the rain gutter of 30° near the roof.

In order to evaluate these two parameters independently, two configurations were designed.

Fig.2.b represents a rain gutter with a height varying linearly from 10 to 42 mm (called

‘Linear’). The configuration shown in fig.2.c (designated as ‘Curved’) was designed to study

the impact of different flow angles on the rain gutter. The angle between the flow and the rain

gutter varies from 90° to 35°. It then leads to 4 cases (2 profiles × 2 configurations).

The experimental spectra obtained for the 5 flush-mounted microphones on the medium

section of the linear configuration for both profiles are presented on fig. 4. The 2 furthest

upstream microphones (Pos. 1 and 2) present for both profiles almost identical spectra. Only

directly in front of rain gutter at position 3, the measured noise level is about 3 dB higher for

the first profile. The main differences are found behind the rain gutter. The first profile

presents its maximum surface pressure level (SPL) directly after the rain gutter (Pos. 4) while

the noise level drops down after the second profile. As additional information, the first profile

was measured to be 13 dB noisier than the second one in the far field (at about ~ 1,8 m).

Fig 4: Experimental narrow band spectra for both profiles at flow stream velocity of 45 m/s.

3. Numerical Methodology

Steady and unsteady simulations using different models were used to assess the

characteristics of the flow over the different profiles and configurations. After briefly

presenting the models and methods, a description of the meshes and set-up will be

proposed.

3.1. Turbulence Modelling

The main topology of the flow was assessed by performing steady Reynolds Averaged

Navier-Stokes (RANS) model using the SST k-ω turbulence model with the Software ANSYS

CFX [12]. In order to get surface pressure fluctuations, transient computations need to be

performed. The most widely accepted unsteady approach is certainly the Large Eddy

Simulation (LES) where the Navier Stokes equations are filtered using a spatial operator with

a filter-width proportional to the local space grid spacing. This later aspect makes a direct

connection between the level of resolution of turbulence scales and the mesh refinement,

and can make the method very expensive for certain complex industrial applications. In

attempt to overcome this restriction, Spalart [13] proposed a hybrid method which combines

features of classical RANS formulations with elements of the LES methods. This concept

called Detached Eddy Simulation (DES), is intended to take advantage of both methods by

covering the boundary layer by a RANS model and switching into a LES mode in detached

regions. This allows the calculation to capture the instability of the shear layer, and the

development of the coherent structures in the wake, with more accurate prediction of the

unsteady forces than can be obtained by steady or unsteady RANS methods. A major

drawback of the method is the explicit grid dependency of the method which can lead to a

premature flow separation. A solution was proposed by Spalart [14] so called Delayed DES

to overcome this problem. The switch for the DES is achieved by comparing the modelled

EASC 2009

4th European Automotive Simulation Conference

Munich, Germany

6-7 July 2009

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turbulent length scale and the grid spacing and is obtained by correcting the destruction term

as follows:

ε= β’ k ω β’ k ω F

DES

with F

DES

= max

Δ

1,

DES

t

C

L

where Lt is the turbulent length scale predicted by the RANS model, is the local grid

spacing and β’ and C

DES

are 2 constants.

As an alternative approach, Menter and al. [15] have investigated the development of an

improved URANS method which can provide a LES-like behaviour in detached flow regions.

The scale adaptive simulation (SAS) concept is based on the introduction of the von Karman

length scale into the turbulent equation and defined as follow in 1D:

22''

/

/

yU

yU

U

S

L

vK

∂∂

∂∂

==

κκ

where S is the absolute value of the strain-rate and κ = 0.41.

The information provided by the von Karman length scale allows the SAS models to

dynamically adjust to resolved structures in a URANS simulation, which results in a LES-like

behaviour in unsteady regions of the flow field. At the same time the model provides

standard RANS capabilities in stable regions. It allows the development of a turbulent

cascade up to the grid resolution into the detached regions without or small grid dependency.

3.2. Meshing and set-up

The geometry for the different configurations were realised with Catia V5 before to be

imported into ANSYS IcemCFD to be meshed. Block-structured meshes composed of

hexahedral cells were realised. To get information on the flow topology over both profiles,

steady RANS and unsteady DES computations were firstly performed on a small part of the

rain gutter with constant height. Steady RANS computations were then realised on the 4

cases. Finally, unsteady simulations using different turbulence models (SAS-SST, DES and

LES) were carried out on profile 1 with linear configuration.

3.2.1. Comparison between profiles – 3D geometries with constant height

Fig 5: Computational domain and meshes

To get a first understanding of the flow over the two profiles and the main differences

between them, fluid simulations were performed on an extrusion over 40 mm in the span-

0,55 m

0,15 m

0,04 m

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4th European Automotive Simulation Conference

Munich, Germany

6-7 July 2009

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wise direction for both profiles. The entire meshes consist in 3 400 000 nodes. The

resolutions in the span-wise direction are for the first and second profile 0.5 mm and 0.33

mm respectively to ensure the coherence of the structure. At the inlet, a free stream velocity

of 45 m/s (162 km/h) corresponding to a Reynolds number based on the height of the rain

gutter and a Mach number of 60 000 and 0.13 respectively and a medium turbulence

intensity were imposed. Pressure outlet was applied at the outlet, while symmetry was

applied at the top and periodic conditions were applied on the sides. The boundary layers

was fully resolved with a y+ = 1, with a first cell of 12 µm at the wall. The height of the rain

gutter is about 20 mm. The time-step used for the unsteady DES simulations was 5 s.

3.2.2. Steady 3D cases

To get information on the flow topology, steady RANS computations were performed on the 4

cases. The tunnel open jet section and the space around the model were reproduced for the

computation. The mesh was then composed of 2 domains linked by a GGI interface. The

core domain was meshed with a low Reynolds mesh, while a coarse mesh was used for the

outer domain. Fig 6 and Table 1 gives information on the computational domain and the size

of the meshes. An averaged velocity profile was given at the inlet to get a boundary layer

thickness in front of the rain gutter in the same order as in the experiment. Three inlet

velocities were tested (30, 45 and 60 m/s); table 2 gathered the Reynolds number

corresponding.

3.2.3. Unsteady 3D case (Profile 1 with height variation)

Due to the low resolution of the previous meshes in the span-wise direction, acceptable

unsteady results could not be expected. At the opposite a complete refinement in the span-

wise direction of the mesh would not be realistic as it would lead to far too high number of

nodes. It was then decided to only compute the unsteady flow in a 100 mm wide box centred

on the middle section of the rain gutter. Due to the slope, no periodic conditions or symmetry

were acceptable; therefore the chosen alternative was to use interfaces. Three domains with

GGI interfaces were then created. The core domain consists in a 7 000 000 hexahedral block

structured mesh. The maximum size of the cells is 10.3 mm, allowing theoretically a spatial

resolution of wavelength of 21 mm i.e. 16 kHz. The second and the outer domains were

respectively composed of 1 120 000 and 273 000 nodes leading to an overall number of

nodes of 8 400 000. A hybrid scheme was used for the convective terms, which automatically

switches from second order upwind in RANS regions to second order central differencing in

scale-resolved flow regions. The time-step used for the simulations was set to a value of 10

s, maintaining a Courant number around 1. Thus, an averaged velocity profile

corresponding to 45 m/s, as well as a turbulence viscosity ratio given at the inlet, and an

averaged static pressure at the outlet. The flow was simulated on about 50 ms.

Fig 8 gives a representation of the unsteady (computed with DES) and a steady flow for both

profiles. The pressure field is represented on the side. Figures b) and d) represent the mean

flow using streamlines and the variable Q at 5×10

6

s

-2

was used to represent the turbulent

structures at a time step on (a) and (c). The variable Q is defined as follow:

( )

ijijijij

i

j

j

i

SS

x

u

x

u

Q ΩΩ−−=

∂

∂

∂

∂

−=

2

1

2

1

As first comment, the site of the recirculation area is bigger for the first profile. The level of

turbulence and pressure fluctuations appear also smaller behind the second profile. This

correlates well with the experimental results and tends to confirm that the first profile is

noisier than the second one.

4.2. 3D Steady Topology

The friction line patterns of the 3D linear configurations for the part with the highest section

for both profiles obtained experimentally (oil visualisation) and from the simulations are

represented on fig.9. It can be seen that as expected, the size of recirculation area in front

and behind the rain gutter increase almost linearly with its height. Close to the border of the

rain gutter (right side), the streamlines are highly deviated to the exterior showing another 3D

effect. Compared to the previous quasi 2D cases, the plate behind the rain gutter was a bit

higher, reducing the size of the recirculation and leading for the 2

nd

to almost no recirculation

behind the rain gutter. On the 2

nd

profile, one also see the reattachment line occurred on the

middle of the thicker part of the rain gutter which is confirmed by the flow visualisation.

at the DES blending functions, it was also controlled that the core domain was running in

LES mode around the rain gutter. Compared to the simulations on the quasi 2D cases (fig. 8)

also computed with DES, the structures in front of the rain gutter could not be so well caught

although similar span-wise resolution was used. One element of explanation for this

difference can be the higher thickness of the boundary layer in this case which tends to

damp the instabilities and does not generate resolved turbulence before the rain gutter.

Fig. 13 Visualisation of the turbulent structures using isosurface of Q (Q =5×10

6

s

-2

) shaded

with velocity simulated with the SAS model without (left) and with (right) forcing

Fig. 14 Visualisation of the turbulent structures using isosurface of Q (Q =5×10

6

s

-2

) shaded

with velocity simulated with DES (left) and LES (right)

This reason was also advanced to explain the unability of the SAS-SST model to switch to

unsteady mode. To overcome this problem, it was decided to use the SAS-F model with

forcing terms recently introduced by Menter [17]. The idea is to introduce forcing terms in the

momentum equations in order to transfer modelled turbulence energy into the resolved

energy for flows which does not exhibit sufficient strong instability to switch to unsteady

mode. A volume stochastic source term and sink term are then respectively introduce in the

momentum equations and modelled turbulent k in a confined user-specified flow region. In

this test-case, the zone corresponds to the volume in the core domain in front of the rain

gutter. The computation was done with ANSYS Fluent 12 and the results presented on

fig13b shows that the structures in front and behind the rain gutter could then be resolved.

4.3.2. Surface Sound Pressure Level

The experimental and computed with DES and SAS-F spectra for 3 flush-mounted

microphones are represented on fig. 15. Microphones 1 and 5 were outside the core domain.

At point 2, both models could not catch the pressure fluctuations so far (83 mm) in front of

the rain gutter confirmed by fig13 and 14. At point 3, the SAS-F presents a fair agreement

with the experimental results while for the DES simulations, the spectrum is very low

confirming the unability of the model to catch the fluctuations in front of the rain gutter. For

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4th European Automotive Simulation Conference

Munich, Germany

6-7 July 2009

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point 4, the SAS-F presents again a good agreement with the experiment. The results of the

DES simulations are only up to 1-3 kHz in good agreement with the experiment. After 3 kHz,

on all DES spectra, a brutal variation of the slope can be observed which might indicate a

cut-off frequency due to the mesh (~ 1mm). This would also tend to confirm the lower

dependency of the SAS model to the size of the mesh.

6. Conclusions

In this study, the topologies of simplified 3D configurations of A-pillar rain gutter have been

investigated using steady and unsteady flow simulations. The 1

st

profile presents the highest

level of turbulence and the higher noise level. It was clearly shown the difficulty to get the

unsteady flow structures particularly in front of the rain gutter. The addition of the forcing

terms in the SAS-SST model turns out to be necessary in this case and allows getting good

agreements with the experimental results. At the opposite, the DES simulations could only

catch the structure after the separation. The authors would also point out that only short

simulation times were performed and that longer simulation times would allow to smooth the

spectra and to improve their quality to be compared with the experiment. Further work

particularly on the acoustical part will conducted and will be presented in a further paper.

7. Acknowledgement

The authors would like to thank M. Menter and M. Müller from ANSYS Germany for their

advice and contributions in the CFD part as well as Prof. Delfs and M. Pott-Pollenske for their

advice on the experimental part.

References

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acoustic analogy, 12

th

AIAA/CEAS Aeroacoustics Conference Massachusetts, 2006

[3] B. Arguillat et al., Measurements of the Wave-Number Frequency Spectrum of Wall Pressure

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Propagative Wavenumbers form Wall Pressure Dataset, JASA, vol. 123-5, 2008

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6-7 July 2009

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