snegirev

8/3/2019 Snegirev

1/12

WEST-EAST HIGH SPEED FLOW FIELD CONFERENCE

19-22, November 2007

Moscow, Russia

USE OF HIGH PERFORMANCE COMPUTING FOR

SIMULATIONS OF AERODYNAMICALLY GENERATED NOISE

IN TURBULENT SEPARATED FLOWS

A.Yu. Snegirev*, S.V. Lupulyak*, Yu.K. Shinder*,

B.S. Grigoriev*, K.Yu. Zamotin* and B. Khalighi**

* Laboratory for Applied Mathematics and Mechanics

Saint-Petersburg State Polytechnic University

Polytechnicheskaya, 29, Saint-Petersburg, 195251, Russia

Email: [email protected], web page: http://lamm.spbstu.ru

** General Motors R & D Center

MC 480-106-256 30500 Mound Rd., Warren, MI 48090 USA

Email: [email protected]

Key words: Computational aeroacoustics, turbulent separated flow, high-performance computing.

Abstract. Computational studies are presented of sound generation in turbulent separated flows behindthe bluff bodies replicating vehicle side mirrors. The decoupled two-stage methodology is applied that

requires massive CFD simulations to collect pressure variations at solid surfaces followed by acoustic

post-processing of the data recorded. In flow simulations, URANS, DES and LES turbulence modeling

approaches are applied, and the flows around two mirror models are studied for different flow velocities.

Curles formulation in low Mach number limit is used to calculate acoustic pressure produced by pressure

fluctuations at solid walls. Reasonable agreement between measurements and predictions has been

achieved both for flow and sound characteristics. It has been found that the URANS and DES approaches

heavily underestimate surface pressure fluctuations and therefore result in underestimated sound pressure

levels. Alternatively, LES approach (particularly that with the dynamic subgrid viscosity model) provides

better agreement for time-averaged surface pressure distributions and for sound pressure levels.

1. INTRODUCTION

Quantitative predictions of aerodynamically generated noise require simulations of

separated turbulent flows coupled with modeling of sound production and spread. In

this work, a computational study is undertaken of turbulent separated flows around two

vehicle side mirror models to assess its noise generation potential. The methodology

used is applicable to low Mach number unsteady flows where the radiated noise is a

small byproduct of the flow that is not altered by it. This assumption is central to

Lighthills formulation of aerodynamic sound generation which implies that sound

characteristics are obtained in two stages. Turbulent flowfields are predicted at CFD

stage followed by sound post-processing stage. One of the objectives of this study is to

select turbulence modeling technique that is computationally affordable and yet capable

in producing appropriate data for further acoustic post-processing. To attain the

8/3/2019 Snegirev

2/12

A.Yu. SNEGIREV et al./Aerodynamically generated noise

objective, we explored a range of turbulence modeling approaches including RANS,

DES and LES.

To calculate the acoustic pressures at the sound post-processing stage, we use the

Curles formulation. The latter uses a Greens function-based solution for the sound

propagation equation with the Lighthills form of noise source-terms in the special caseof immovable and impermeable boundary submerged in an inviscid fluid. Note, that

such a decoupled methodology is normally referred as a hybrid approach [1,2] to noise

prediction and is widely used to avoid simultaneous resolution of both flowfield and

acoustic scales. Thus, another objective of this work is to develop acoustic post-

processing methodology and associated software.

Flow simulations presented in this paper were conducted using 48-core AMD Opteron

cluster (161.3 GFLOPs)1 at the Laboratory for Applied Mathematics and Mechanics

(Faculty of Physics and Mechanics, Saint-Petersburg State Polytechnic University).

2. MODEL DESCRIPTION

2.1. Statement of the problem and computational grids

Flows around two different full-scale mirror models were considered. Computational

domain (Figure 1a) replicates the wind tunnel conditions. In the nozzle outlet, a

uniform mean velocity profile was assumed with turbulence intensity of 0.01, and

integral length scale of 0.7 m. The inlet flow velocities, 20, 30 and 40 m/s, have been

considered in this work.

a) b)Figure 1. The test section of the wind tunnel computational domain (a) and a computational grid (b)

used in flow simulations

Multi-block structured computational grids (Figure 1b) used in the simulations were

constructed with Ansys ICEM CFD 10.0. The computational domain was divided onto

several sub-domains, each covered by a separate curvilinear structured grid. The grid

blocks were constructed according to the following principles.

Near-surface grid resolution in boundary layers (in the vicinity of the mirror

model and the ground surface in near wake downstream the model) should provide

1 At the moment of writing this paper, 256-core cluster (64 nodes, AMD Opteron 280, 1035 GFLOPs) is

also used.

8/3/2019 Snegirev

3/12


sufficiently small value (from a fraction of one in the recirculating zone to about 20

in the wake).

+y

Grid resolution in a detached shear flow away from the surfaces should be fine

enough to resolve large scale flow vortical structures. The recirculating zone and

reattachment zone need a highest grid resolution whilst the remaining sub-domains maybe covered by coarser grids.

The ratio of neighboring grid element sizes inside every sub-domains should not

exceed the value of 1.3.

Total number of grid elements was about 2 millions.

2.2. Turbulence modeling

In flow modeling and simulations, large scale flow vortical structures, dominant

frequency, overall flow topology were investigated. In the unsteady RANS approach,

the shear stress transport (SST) turbulence model by Menter [4,5] was used. Finer flowstructures and wider frequency range were predicted using a hybrid URANS-LES

methodology (DES [6,7]) as implemented inAnsys CFXand pure LES with a range of

subgrid models. The conventional Smagorinsky model, the dynamic Smagorinsky

model, and the kinetic energy equation model were exploited in this work. Two latter

subgrid models were used withAnsys Fluent[8], since the current (at the time when the

simulations were carried out) version of Ansys CFX[9] offers only conventional

Smagorinsky model.

3. FLOW SIMULATION RESULTS

3.1. Flow topology

In flow structure analysis, URANS simulations provided valuable information that was

then further interpreted in terms of DES and LES data. Several distinctive flow regions

have been identified when analyzing the URANS results:

Stagnation region with high pressure at the windward mirror side;

Rarefaction region at mirror side and top surfaces. Adverse pressure gradient

may inspire flow separation here. If such a separation does not occur, it happens behind

the sharpened edges; Massive separation zone with recirculating flow inside (recirculating zone). This

zone is characterized by a considerable pressure drop below the reference pressure;

Zone of flow reattachment to the ground surface (later referred as reattachment

zone) behind the recirculating zone (pressure recovers here producing values above the

reference one) followed by the wake.

8/3/2019 Snegirev

4/12


1

1

1

2 2

3

4

6

5

1

Model A Model B

Figure 2. Large-scale flow structures identified in URANS simulations around two mirror models (wind

speed 30 m/s,Ansys CFX). 1 transient vortices shed by the mirror side surface, 2 streamwise vortex

tube, 3 and 4 steady zones of horizontal circulation, 5 and 6 steady zones of vertical circulation.

Upper Figure 2 (Model A) clearly shows formation of pressure spots at the ground

surface downstream the recirculating zone of the mirror model. The spots indicate local

minima and maxima of the pressure which in its turn are associated with the vortexes

shed by the left mirror side. Local pressure maxima and minima correspond toalternating direction of fluid rotation in the vortexes. The vortexes reach the ground

surface and corresponding pressure spots appear in the flow reattachment zone. One of

the vortexes of this type is highlighted and designated as 1 in Figure 2 (Model A).

Such a flow behavior is similar to formation of von Karman vortex street behind a bluff-

body.

Alternatively, for the mirror model B shown in upper Figure 2 b flow reattachment zone

is represented by a single pressure maximum just weakly fluctuating in time. No large

scale vortex shedding has been observed in the simulations.

In the wake, streamlines concentrate in the vortex tube aligned with the bulk flow and

designated as 2 in Figure 2.In the recirculating zones produced by both mirror models fluid circulation has been

observed not only in horizontal (x-y) but also in vertical (x-z) direction. Vertical

8/3/2019 Snegirev

5/12


circulation drives fluid particles from the region near ground surface towards the mirror

top and back. For the mirror model A, the flow in this zone is heavily fluctuating and

more homogenous than that for the mirror B. For the latter, both vertical and horizontal

stratification can be concluded from flow visualizations. Indeed, top view and side view

shown in Figure 2 (Model B) demonstrate existence of separate recirculating sub-zones

with vertical (designated as 3 and 4) and horizontal (designated as 5 and 6) axisof rotation.

Thus, in the flows studied, several distinctive flow structures have been observed

(Figures 2 and 3).

Transient vortices shed by the mirror side surface (1).

Streamwise vortex tube (2).

Steady zones of horizontal (3 and 4) and vertical (5 and 6) circulation.

Combination of the zones 3 to 6 form an arch-type structure.

Flow topology is more convenient to be visualized and investigated by plotting the iso-surfaces of the strain-vorticity invariant , where and are the

magnitudes of strain rate and vorticity tensors with the components

22 = S S

+

=

i

j

j

i

ijx

u

x

uS

2

1,

=

i

j

j

i

ijx

u

x

u

2

1. (1)

Figure 3 demonstrates the = const iso-surfaces for the two mirror models and the

wind speed of 30 m/s. The above mentioned flow structures are shown by iso-surfaces

corresponding to a negative (blue surface) and positive (red surface) values of . Quasi-

periodic vortex shedding (1) from the left side of mirror model A and formation of the

streamwise vortex tube (2) is clearly evident. Our simulations have also shown thatvelocity increase from 30 to 40 m/s does not cause qualitative transformation of the

flow.

I

(a) (b)

(c) (d)

Figure 3. Flow structures resolved by URANS (a, c) and DES (b, d) approach in 30 m/s flow(Ansys CFX). (a, b) - mirror model A; (c, d) - mirror model B

8/3/2019 Snegirev

6/12


Use of LES (and DES) approach allows for fine turbulent structures to be resolved. In

Figure 3 a and b, simulation results obtained by URANS SST and DES SST (as

implemented in Ansys CFX) are compared for the mirror model A. First, it can be

concluded that the overall large-scale flow structure predicted by URANS can also be

recognized in DES results. Indeed, quasi-regular shedding of vortical structures is

observed from the left mirror side. However, those structures are further composed ofseveral sub-structures with opposite signs of (Figure 3 b). Pressure spots movingover the ground surface are rather disordered compared to those predicted by URANS

(Figure 3 a). Worth noting that finer details are now predicted of the surface pressure,

which is very important for further use of this information in simulations of noise

produced by dipole sources.

Dissimilar to the Model A, remarkable difference can be concluded between the

predictions by URANS and DES methods for the flow around another mirror model

considered (Figure 3 c and d, Model B). The DES methodology predicts a long and

unstable wake, very distinctive from that predicted by URANS. However, the

conclusion on formation of an arch-type trailing vortical structure that surrounds therecirculating zone behind the mirror is still applicable to DES predictions as well.

Ground surface pressure distributions

Adequate prediction of surface pressure distributions is necessary for accurate

simulations of noise originated from the dipole sources. Considerable attention has

therefore been paid in this work to comparisons of the predicted surface pressure

distributions with those experimentally measured in General Motors Research and

Development Centre. In the experiments, static pressure was measured along the

centerline and in three points at the reflecting surface of the mirror models shown in

Figure 4 a and b. Not surprisingly, choice of turbulence modeling methodologysignificantly affects the pressure field resolution. Figure 4 shows the result obtained

with the dynamic Smagorinsky subgrid model as implemented in Ansys Fluent. Note

that the instantaneous flow differs remarkably from the average one. Indeed, Figure 4 a

demonstrate formation, transport and decay of surface pressure spots. Furthermore, it

was observed that vortex break-up also occurs producing doubled street of pressure

spots moving streamwise along parallel directions. The averaged flow field (Figure 4 b)

does not show the above features, and it appears to be quite similar to those predicted by

the steady RANS model.

a) b) c)

Figure 4. Surface pressure distributions for 30 m/s flow around the mirror model by LES (dynamic

Smagorinsky subgrid model,AnsysFluent). a) Instantaneous resolved field, b) Time averaged field,

c) Centerline surface pressure distributions. Vertical bars correspond to root mean square fluctuation

magnitude. The streamwise locations of the reflecting surface points are not representative

8/3/2019 Snegirev

7/12


To validate simulation results the latter were compared with the static pressure

measurements. Surface pressure distributions along the centerline y = 0, z = 0 were

normalized to obtain mean-pressure coefficient,

( )

= q

pxp

CP0

, (2)

where 22 = Vq is the dynamic pressure and is the reference pressure.Comparison of predicted and measured pressure distributions along the centerline and in

three points at the reflecting surface is shown in Figure 4 c, which shows, first, good

agreement for the time-averaged data and, second, quite a significant variance of the

numerically resolved field.

0p

It was observed in the simulations that in RANS and DES simulations (Ansys CFX)

pressure drop behind the mirror revealed underestimated pressure drop behind the

mirror. In the pure LES simulations, the latter pressure drop increased giving

observably better agreement with pressure measurements, particularly at the reflectingsurface. Use of dynamic subgrid-scale modeling provides further improvements in

reproducing the measured flow. That indicates the importance of appropriate subgrid

model in LES.

4. ACOUSTIC MODEL AND SOUND SIMULATION RESULTS

4.1. Acoustic model description

The decoupled (hybrid) methodology used in this work includes two consecutive stages.At the first one, the CFD simulations are performed including transient simulations

required to establish statistically steady flow followed by simulations in statistically

steady flow to record transient surface pressure distributions. At the second stage,

acoustic post-processing is carried out of the recorded surface pressure distributions.

Acoustic post-processing of the recorded surface pressure distributions includes:

1. Calculating transient acoustic pressure ( )tp ,~ x at a given location(s) using theequation:

x

( )

( ) [ ] ( )

[ ]

+

= S S

iiiiii

dpr

nyx

dt

p

r

nyx

ctp yyx 320 4

1

4

1

,

~

, (3)

where yx =r is the distance between the source point at the sound radiating solid

surface and the observer position ; corresponds to a component of the unit

normal vector at the wall surface; is the speed of sound. Square brackets in Eq. (3)

indicate the retarded time,

y

S x in

0c

0crt . This is the Curles formulation in low Mach numberlimit [3] that takes into account sound radiation by solid walls at rest whilst neglecting

volumetric (quadrupole) sound emission.

2. Calculating the power spectral density (PSD) ( )fS ,x of sound pressure ( )tp ,~ x at a given location(s) using the fast Fourier transform technique (FFT). About 16thousands of time steps recorded during flowfield simulations were used in acoustic

post-processing. It was found in sensitivity studies that further increase of the

x

8/3/2019 Snegirev

8/12


observation period (the time step is constant and equal to 510-5

s) does not alter the

results.

3. Calculating sound intensity( ) ( )( )xx pc

I ~var100

= = ( )

000,1 dffS

cx , (4)

and sound pressure level

( ) ( )

=02

,1

log10 dffSp

SPLref

xx (5)

at a given location(s) , wherex ( )( )xp~var is the sound pressure variance, is theundisturbed air density, = 2.0410

0

refp5

Pa is the reference threshold pressure.

a) Mirror only b) Mirror and ground surface

Figure 5. Predicted sound pressure spatial distributions in the horizontal plane located at elevation of

18 in above ground surface (Model B, 30 m/s wind speed, LES, Smagorinsky subgrid viscosity model,

Ansys CFX). Wind direction is given by the arrow. a) contribution of the mirror model surface only,

b) total contribution of the mirror surface and the ground surface

8/3/2019 Snegirev

9/12


4.3. Acoustic pressure and sound pressure level

Instantaneous spatial distributions of acoustic pressure over the horizontal plane at an

elevation of 18 in above the ground surface are shown in Figure 5 for one of the mirror

models considered. Note that both the surface of mirror model and the ground surface

contribute into the sound emission. The role of mirror model is to disturb the flowdownstream thereby affecting the power of sound generated by the ground surface. The

greater is the intensity of pressure fluctuations in the recirculating zone behind the

model, the higher is the noise generation potential of the given model geometry.

It can be observed that the mirror-induced acoustic pressure is fairly symmetric

(Figure 5 a) while the spatial distribution of total acoustic pressure simultaneously

produced by mirror and ground surface is shifted downstream (Figure 5 b).

Sound pressure level (SPL) simulations have been performed in a number of points at

an elevation of 18 in above the ground surface. The results for wind speed of 30 m/s are

shown in Figures 6 and 7 for the two turbulence modeling approaches used (only two

positions located above the centerline, y = 0, are shown conventionally designated as 2and 5). The following conclusions can be made.

Figure 6. Acoustic post-processing of DES results at two positions at elevation of 18 in (Model A, 30 m/s

wind speed, DES SST,Ansys CFX): a) acoustic pressure variation in time. b) SPL frequency

distributions

Figure 7. Acoustic post-processing of LES results at two positions at elevation of 18 in (Model A, 30 m/s

wind speed, Smagorinsky subgrid viscosity model,Ansys CFX): a) acoustic pressure variation in time.

b) SPL frequency distributions

8/3/2019 Snegirev

10/12


1. Predicted SPL frequency distributions are qualitatively similar to the measured

ones2. They exhibit low-frequency spike followed by weaker frequency dependence (at

higher frequencies). Note that frequency range beyond 2 kHz is strongly affected by

numerical errors rather than controlled by fluctuations resolved in the simulations.

Indeed, the range of acoustic frequencies that can be captured in LES depends primarilyon the value of tl in the dominant noise-generating region ( is the integral lengthscale of turbulent motions, and is the local grid spacing). Maximum eddy-frequency

resolved in LES is estimated in [1] using the model turbulence spectrum:

tl

maxf

3/2

max

2

1

tt

l

f

f, (6)

where = =tf1t tlu , u being the rms fluctuation velocity. For the flows of interest,

the turbulence integral scale can be estimated as = 0.07 m (half of the characteristic

size of the mirror models), fluctuation velocity is of order of u

tl

= 0.5 = 15 m/s andthe characteristic value of = 310

V-3

m can be used for the grid spacing. A numerical

estimate of the maximum LES-resolved eddy-frequency is therefore of order of

1 kHz. For the frequency estimated, the corresponding sound wavelength in air is of

order of 0.3 m. Note, the wavelength that is equal to the characteristic size of the mirror

models (0.14 m) would correspond to the frequency of order of 2.4 kHz. It can therefore

be concluded that for the frequency range resolved in LES, the mirror models can be

approximately regarded as acoustically compact bodies (which is not the case for higher

frequencies).

maxf

2. SPLs computed using the flowfield predictions by DES are observably lowerthan measured values (see Figure 6 b). This indicates that intensity of near-surface

pressure fluctuations is underestimated by this turbulence modeling approach. Although

these are certainly useful to determine important characteristics of the flowfield (drag,

dominating frequency, shape and size of separated zone), higher numerical resolution is

required for subsequent acoustic post-processing.

3. Predicted SPLs are quite close to the measured ones if LES results are used for

the acoustic post-processing (see Figures 6 c).

4. The effect of wind speed on the SPL frequency distributions has also been

investigated. As expected and in accordance with the experiments, sound pressure level

increases as the wind speed increases.

4.4. Sound intensity

Recall that both the mirror model surface and the ground surface contribute into the

sound emission. Furthermore, as it was suggested in Figure 5, the ground surface

contribution appears to be greater.

Distributions of sound intensity produced by the mirror surface and ground surface

together are shown in Figure 8 for 1 m radius sphere centered in the coordinate origin

inside the mirror model. It can be seen that there is the single maximum of the intensity

angular distribution, and most of acoustic energy propagates in a downstream direction.

2 Sound measurements have been performed in the experiments with the mirror models mounted on the

table rather than on the ground surface as assumed in this work. Investigation to assess the effect of this

difference is currently in progress.

8/3/2019 Snegirev

11/12


Distributions of sound intensity produced by the mirror surface itself (shown in

Figure 9) is just a fraction of total intensity. Dissimilar to the total intensity, its angular

distribution reveals two propagation directions. In addition to the primary downstream

direction, a pronounced local maximum is visible in opposite (upstream) direction (see

Figure 9 b). It was concluded that such a distribution is coupled to the pressure variance

distribution over the surface.

Figure 8. Predicted sound intensity spatial distributions over 1 m radius sphere (Model A, 30 m/s wind

speed, Smagorinsky subgrid viscosity model,Ansys CFX). Wind direction is given by the black arrow.

a) side view, b) top view. Shown is the total sound emission by the mirror model and ground surface.

Figure 9. Predicted sound intensity spatial distributions over 1 m radius sphere (Model A, 30 m/s wind

speed, Smagorinsky subgrid viscosity model,Ansys CFX). Wind direction is given by the black arrow.a) side view, b) top view. Shown is the contribution of the mirror models only, sound emission by the

ground surface is not accounted for. Note the intensity scale is 10 times less than that in Figure 8

Note that integration of the intensity over the solid angle results in the total value of

power of sound emission; the latter can be readily used as an estimate of noise

generation potential of a given mirror model.

9. CONCLUSIONS

In the paper, computational studies are outlined of sound generation in turbulentseparated flows behind the bluff bodies replicating vehicle side mirrors. The decoupled

two-stage methodology is applied that requires massive CFD simulations to collect

8/3/2019 Snegirev

12/12


pressure variations at solid surfaces followed by acoustic post-processing of the data

recorded. Curles formulation in low Mach number limit is used to calculate acoustic

pressure produced by pressure fluctuations at solid walls, with volumetric (quadrupole)

sound emission assumed to be negligible. A range of turbulence modeling approaches

including URANS, DES and LES is applied, and the flows around two mirror models

are studied for flow velocities from 20 to 40 m/s. Reasonable agreement betweenmeasurements and predictions has been achieved both for flow and sound

characteristics. In the latter case, it has been found that the DES (not to mention

URANS) approach results in heavily underestimated surface pressure fluctuations and

therefore in underestimated sound pressure levels. Alternatively, LES approach

(particularly that with the dynamic subgrid viscosity model) provides better agreement

both for time-averaged surface pressure distributions and for sound pressure levels.

9. ACKNOWLEDGEMENTS

This work was funded by General Motors Corporation (GM Research and Development

Center). Contribution by Dr Alexander Smirnov and Mr Eugeny Shinder in software

development for acoustic post-processing and support by Dr Alexey Ushakov (Russia

and CIS R&D Science Office) are gratefully acknowledged.

10. REFERENCES

[1] T. Colonius and S.K. Lele,Computational aeroacoustics: progress on nonlinearproblems of sound generation, Progr. Aerospace Sci., 40, 345 416 (2004).

[2] B.A. Singer, D.P. Lockard and G.M. Lilley. Hybrid Acoustic Predictions.Comp. Math. App., 46, 647 669 (2003).

[3] N. Curle. The influence of solid boundaries upon aerodynamic sound. Proc.

Roy. Soc. (London), A 231, 505 514 (1955).

[4] F.R. Menter. Two-equation eddy-viscosity turbulence models for engineering

applications,AIAA-Journal, 32, 1598-1605 (1994).

[5] F.R. Menter and M. Kuntz. Adaptation of Eddy-Viscosity Turbulence Models to

Unsteady Separated Flow Behind Vehicles. Proc. Conf. The Aerodynamics of

Heavy Vehicles: Trucks, Busses and Trains, Asilomar, Ca, 2002.

[6] M. Strelets. Detached eddy simulation of massively separated flows, AIAAPaper, 2001-0879 (2001).

[7] A. Travin, M. Shur, M. Strelets and P.R. Spalart. Physical and numerical

upgrades in the Detached-Eddy Simulation of complex turbulent flows, Fluid

Mechanics and its Applications, 65, 2002, Advances in LES of Complex Flows,

Proc. of EUROMECH Colloquium 412, pp. 239-254, Kluwer Academic

Publishers, Dordrecht/ Boston/ London.

[8] FLUENT6.2 Documentation, Fluent Inc, 2005.

[9] ANSYS CFX Solver, Release 10.0, 2005.

snegirev

Documents