Aeroacoustics Simulation of an Automotive A-Pillar Rain Gutter


Feb 22, 2014 (3 years and 1 month ago)


Aeroacoustics Simulation of an
Automotive A-Pillar Rain Gutter

Hervé Dechipre and Dr. Michael Hartmann
Group Research, Automotive Techniques, CAE
Volkswagen AG, Wolfsburg, Germany

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
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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

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
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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
with F
= max


where Lt is the turbulent length scale predicted by the RANS model,  is the local grid
spacing and β’ and C
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:

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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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

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
was used to represent the turbulent
structures at a time step on (a) and (c). The variable Q is defined as follow:
( )
Q ΩΩ−−=


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
to almost no recirculation
behind the rain gutter. On the 2
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
) 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
) 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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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
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.

[1] R. Siegert et al., Numerical Simulation of aeroacoustical sound generated by generic bodies
placed on a plate: Part II – Prediction of radiated sound pressure AIAA Paper 99-1985, 1999
[2] H. Posson, F. Pérot, Far Field evaluation of the noise radiated by a side mirror using LES and
acoustic analogy, 12
AIAA/CEAS Aeroacoustics Conference Massachusetts, 2006
[3] B. Arguillat et al., Measurements of the Wave-Number Frequency Spectrum of Wall Pressure
Fluctuations under Turbulent Flow, 11th AIAA/CEAS Aeroacoustics Conf. AIAA Paper, 2005
[4] S. Debert et al., Fluctuations under Turbulent Flows Enhanced Methods for Separation of
Propagative Wavenumbers form Wall Pressure Dataset, JASA, vol. 123-5, 2008
[5] A. Kumarasamy, K. Karbon, Aeroacoustics of an Automobile A-Pillar Rain Gutter: Computational
and Experimental Study, SAE Paper 1999-01-1128
[6] N.N. Automotive Rain Gutter Noise, Application briefs from FLUENT, EX227, 2005
[7] S. Becker et al., Experimental and Numerical Investigation of the flow induced noise from a
forward facing step, AIAA 2005-3006, 2005
[8] I. Ali et al., Aeroacoustic Study of a forward facing step using linearized Euler equations,
Physica D: Nonlinear Phenomena, Volume 237, Issues 14-17, Aug. 2008, pp 2184-2189
[9] Y. Bae et al., Investigation of Flow-Induced Noise from a Forward Facing Step, 19th Symposium
of Japan CFD, Tokyo 2005
[10] M. Pott-Pollenske, J. Delfs, Enhanced Capabilities of the Aeroacoustic Wind Tunnel
Braunschweig, 14th AIAA/CEAS AIAA-2008-2910, Aeroacoustics Conference, Vancouver, 2008
[11] R.K. Amiet, Correction of Open Jet Wind Tunnel Measurements for Shear Layer Diffraction,
AIAA 75-532, 1975
[12] F.R. Menter, Zonal 2-equation k-ω turbulence model for aerodynamic flows, AIAA 2906, 1993.
[13] P.R. Spalart et al., Comments on the feasibility of LES for Wings and on a Hybrid RANS/LES
Approach, Advances in DNS/LES, Proc. 1
AFOSR Int. Conf. on DNS/LES, 1997.
[14] P.R. Spalart et al., A new version of detached eddy simulation, resistant to ambiguous grid
densities. Theor. Comput. Fluid Dyn. 20: 181-195, 2006
[15] F.R. Menter et al., A Scale-Adaptive Simulation Model for Turbulent Flow Predictions, AIAA
paper 2003-0767, 41st Aerospace Science Meeting & Exhibit, Reno, Nevada, USA, 2003
[16] Rung et al., Sound radiated of the vortex flow past a generic side mirror, AIAA-2002-2340, 2002
[17] F.R Menter et al., Scale Adaptive Simulation with Artificial Forcing, 3
Symposium on Hybrid
RANS-LES Methods, Gdansk, Poland, 2009
[18] H. Dechipre et al., Aeroacoustic Simulation Based on Linearised Euler Equations and Stochastic
Sound Source Modelling, Acoustics 08 Paris, pp 1071-1076, 2008
[19] M. Hartmann and H. Dechipre, Numerische Aeroakustik für Anwendungen im Automobilbereich,
ATZ VDI-FVT-Jahrbuch 2009, pp 90-99, 2009
[20] R. Ewert, Broadband slat noise prediction based on CAA and stochastic sound sources from a
random particle mesh (RPM) method, Computers & Fluids, Vol. 37-4, May 2008, pp 369-387
[21] S. Caro et al. A New CAA Formulation based on Lighthill’s Analogy applied to an Idealized
Automotive HVAC Blower using AcuSolve and Actran/LA, AIAA05-3015
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