Large Eddy Simulation for Train Aerodynamic Noise · PDF fileLarge Eddy Simulation for Train...

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Large Eddy Simulation for Train Aerodynamic Noise Predictions Andreini, Bianchini, Facchini,Giusti

University of Florence Energy Engineering Department, “S.Stecco”

Via di S.Marta, 3; 50139; Firenze; Italia [email protected]

Bellini, Chiti, Grazzini

AnsaldoBreda Materials & Technologies Department

Via Ciliegiole 110b ; 51100; Pistoia; Italy

Abstract It is known that for high speed trains the main noise source is the aerodynamic noise; however also at medium speed the sound connected with the air stream around the train body is certainly not negligible, especially concerning both operator and passenger comfort due to the relatively close location of the sources, and environmental impact.

Therefore it is fundamental to assess the effects of aerodynamic noise both in terms of integral sound level developed and localization of acoustic sources. It is indeed quite difficult to separate the different noise contributions and to localize the sources in on-track measurements, anyhow decoupling of aerodynamic noise from the other sound generation can be achieved both in wind-tunnel test or with aeroacoustic numerical simulations.

The purpose of this work is to develop a tool to perform aerodynamic noise predictionsfrom a direct simulation of the flow field around the train surface. In the first step of this complex aim, blind numerical simulations are going to be performed on a mock-up to be later experimentally tested in wind tunnel experiments.

This will be achieved by means of an OpenSource CFD platform called OpenFOAM implementing operator based high order finite volume discretization. Transient incompressible NavierStokes equations are solved with a pressure based algorithm exploiting PISO loop to cope with nonlinearity. Turbulence is partially resolved using Detached Eddy Simulation technique; SpalartAllmaras one equation model is taking into account sub-grid effects. Validation of the procedure was performed on a simpler test case concerning a generic side mirror exposed to a uniform airstream above a flat plate for which detailed experimental results were available from literature.

The test case to be simulated is a 1:5 scale mock-up of a first car plus an appendix of half the last car to get the correct aerodynamic wake. Train speed is fixed at 70 m/s and open field boundaries are assumed around the body. The effect of ground is considered only in terms of acoustic reflection, i.e. uniform velocity profile on the front of the train. Hybrid prismatic-tetrahedral numerical grid of 7 millions cells was created with ICEM-CFD mesh generator.

The pressure fluctuations are monitored in time at specific locations considered as the most representative in order to perform spectrum analysis and averaged on the entire train surface to give the mean sound intensity of every component.

Introduction Among the various sound sources aerodynamically generated noise is certainly not the most significant at low and moderate speed. Anyhow already at medium speed such as 200 km/h it becomes an important source for both externally transmitted noise and internal noisiness. The former effect is connected with the additional noise provided to the environment by the passing train and is


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mainly affected by the global sound level generated on the train vehicles.The latter viceversa is related to passenger comfort and is strongly influenced by the localization of such noise sources on the vehicle itself. It is indeed fundamental to evaluate the sound generated on the various parts of the car to properly design sound-proof coating. In fact opposite to most of the other sound sources present in a running train that are located below the pavement, the aerodynamic noise is generated, at various levels, all around the train body close to usually less covered surfaces such as the roof or the lateral sides, responsible for the increased noise level in the car.

Moreover the feasibility of dedicated experimental campaign is limited by the difficulty to extrapolate the aerodynamic noise from the global noise registered in on-track tests and by the pressing requirements for suitable wind tunnels in terms of dimensions, wind speed and acoustic properties. In wind tunnel tests in fact, the huge dimensions of real cars impose a downscaling of the geometry reducing the level of noise generated that, in combination with the sound produced by the blower to achieve high testing speeds, makes the measurements quite challenging. At the same time for on-track campaign, even in case single contributions of noise such as wheel rail contact could be identified, it is not easy to associate the aerodynamically generated noise to the various components of the wagon.

To overcome such drawbacks, computational techniques might help in estimating the noise generated by the fluid flow around the car body. Noise in fact is developed by the chaotic behavior of turbulent flows triggering unsteady pressure fluctuations. However the direct simulation of the turbulent flows, even though possibly applied also to sound transmission, results to be too costly already at moderate Reynolds number and cannot be considered a solution. Dimensional analysis anyhow shows how the sound sources connected with the turbulent stress typical of shear layers are negligible at Mach number of interest (0.2-0.3) and a partially resolved turbulent field may be a reliable tool for the estimation of surface sources. Hybrid methods such as Detached Eddy Simulation has been proved to offer several advantages for the computation of aerodynamic noise source on complex geometries and strongly separated flows.

In this context AnsaldoBreda and University of Florence started a project aimed at developing computational tools to predict the generation of aerodynamic noise on real trains.

The project is composed of three phases. First one is devoted to the development of the needed computational tools in the framework of the open-source code OpenFOAM that offers the optimal starting point and the fulfillment of blind test aimed at identifying most noisy parts to be later experimentally investigated in detail, at defining the geometrical details to be included in a mock-up to perform wind tunnel test and finally at evaluating the feasibility of the numerical procedure. Second one plans to carry out the experimental survey on the proposed model in a suitable wind tunnel and an equivalent numerical analysis to validate, possibly tune and adjust the procedure. Third and final part will be focused on the development of lower order models to permit noise source evaluation by means of steady state CFD analysis exploiting the validated results obtained in phase two.

This paper reports the details of the methodology in use, the specification of the geometrical model and the results obtained with the blind test at 70 m/s.

Numerical methods The following computations have been performed with an open-source CFD code called

OpenFOAM(OpenCFD). It implements finite volume implicit and explicit operators with high order schemes for unstructured polyhedral grids. Besides the flow solvers already available, its main strength is derived from the source code structure optimized to simplify customization and implementation of new models. The code runs in parallel exploiting domain decomposition mode; MPI communication protocol is used for inter-processor data sharing.


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With the aim of evaluating aerodinamic noise sources,the turbulent flow field around the train mock-up is calculated with a three dimensional computation solving the Navier-Stokes equations integrated by means of finite volume discretization. Sound generation is associated to pressure oscillations caused by turbulence thus unsteady fluctuations need to be solved directly, at least partially, requiring a transient time resolved solver. In particular a segregated pressure-based approach implementing the PISO loop (Pressure Implicit Splitting Operators) (Issa, 1986)is in use with 4 predictor corrector steps to resolve pressure-velocity coupling. Considering a maximum Mach number in the domain of about 0.2 the fluid was modeled as incompressible and no energy equation was solved.

The filtered Navier-Stokes equations are solved to directly reproduce the largest and more energetic turbulent structures and to model the smallest and dissipative scales following the principle of Large Eddy Simulation. Actually in order to avoid the fine near wall clustering required for a wall resolved LES, an hybrid subgrid scale model was employed gradually changing from a RANS (Reynolds Averaged Navier Stokes) modeling to a LES treatment in the far field with increasing wall distance. This technique, known as Detatched Eddy Simulation (DES), has been applied to several turbulence models in the course of the years; in this work the original framework of the Spalart-Allmaras model was used(Spalart, Allmaras, Strelets, & Jou, 1997).

Convective schemes employed for this analysis vary between a bounded linear scheme for the validation test and the second order upwind scheme of the TVD (Total Variation Diminishing) class proposed by VanLeer(Van Leer, 1992).

Geometry The geometrical complexity of a real train does not allow a complete reconstruction of the

details both concerning the numerical predictions and the mock-up for the wind tunnel experiments to be preformed. The model was consequently simplified and reduced taking care of including all the expected aerodynamic noise sources such as the bogie,the HVAC system canopy,the gangway and the pantograph tank. The first wagon with the two bogies is included in the model as well as a second half wagon with an aerodynamic tail resembling the last wagon of the real train. As it is possible to see in Figure 1, each expected source is present in the model, exception made for the external auxiliary components such as buffers, pantograph and electric components in pantograph tank.

Figure 1 Overview of geometrical model

Main simplifications were introduced in the bogie, see Figure 2, parts where many components were erased and global shape was partially redesigned trying to maintain a global balance between solid and empty spaces. In detail the opening between the left and right side of the car was guaranteed to maintain the flow recirculations associated.


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Figure 2 Model detail in the bogie part

A preliminary research about suitable wind tunnels for large scale vehicles pointed out that the housing of objects longer than 8 m subject to air stream at 70 m/s was problematic even for the most dedicated facilities. Due to the large dimensions of such a model (> 40 m in real scale) a downscaling is therefore necessary for the experimental mock-up to fit available wind tunnels; the same downscaling was applied to the numerical model as well. A 1:5 scale was chosen as the best compromise between the dimensional constraints connected with the housing in the test section of the chosen wind tunnel (Pininfarina) and the noise reduction; the dimensions of the reduced model are 8x0.8x1.5 m.

Computational domain and grid The computational domain built around the mock-up is a box of length 16 m, width 8 m and

thickness 4 m, see Figure 3. The dimensioning and the positioning were performed to minimize reflection effects from lateral and top boundaries and to reproduce equivalent aerodynamic conditions as in an equivalent open air test. With the chosen dimensions, blockage is maintained below few percents (1.25% in this case) thus the effect of confinement is reduced. Inlet surface is positioned 2 m upstream the mock up to let the gas flow adapt to the stagnation point on the vehicle leading edge. The correct development of the wake is guaranteed by a downstream domain extension equivalent to 75% of the model length. Finally the ground effect is modeled by means of a flat plate integral with the train.

Figure 3 Computational domain overview

The computational mesh in use is a hybrid unstructured grid composed of 7 millions cells and generated using ICEM-CFD. Tetrahedral elements fill up the far field region while thenear wall zone is


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made of 5 layersof prismatic elements to maximisenear wall orthogonalityon the viscous surfaces. Due to the high geometrical complexity these prismatic layers were omitted in selected regions where spikes, high curvatures and surface closeness below required tolerance made the insertion of the prisms problematic. Those regions were however limited to regions where flow is to be highly separated and the current approach is anyhow not totally suited to capture the complete flow details. Such approximation is believed not to affect the global accuracy of the procedure.

Surface mesh was generated assigning different resolution to the various surfaces trying to respect the geometrical complexity and the expected noise source distribution. The far field is also refined in selected regions to augment the spatial and temporal scale directly resolved by the Large Eddy Simulation. A first refinement was introduced around the vehicle body while a second and lighter one follows the expected wake as depicted in Figure 4.

Figure 4 Computational mesh – Z plane overview

Some zones such as the bogies and the front spoiler were further refined to enhance solver stability and include more turbulent scales in the solution, see Figure 5.

Figure 5 Computational mesh – Front details


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Boundary conditions The boundary conditions applied follow a classic scheme for incompressible flow with velocity

prescribed at the inlet and static pressure assigned on the outlet boundary. The lateral surfaces as well as the top and lower boundary were modeled as inviscid and acoustically reflecting (symmetry plane). The lower boundary mimics the effect of the ground while the other ones are so far from the model that acoustic pressure waves are sufficiently damped in the far field region to neglect such acoustic reflection.

The investigated train speed is 70 m/s; a constant and uniform velocity profile oriented normally to the boundary and parallel to the train axis is imposed. On the outflow boundary, pressure is maintained sufficiently close to the reference pressure anyhow, to permit pressure wave transmission, an advective boundary conditions is imposed.

Such condition implements convective transport across the boundary, see

0

npU

tp

Equation 1, however this original formulation does not guarantee the

mean boundary pressure to stay close to the reference pressure.

0

npU

tp

Equation 1

Linear relaxation method was employed to avoid pressure drift and at the same time to maintain the non reflective behavior. The right hand side is thus rewritten to include reference

pressure and a relaxation coefficient K, see p pU K p pt n

Equation 2.

p pU K p pt n

Equation 2

The relaxation coefficient is evaluated exploiting the characteristic local velocity and a reference length scale:

lUK

Equation 3

In this analysis l∞ = 1 mand p∞ = 0 Pa.

Results The stimulus to evaluate the aerodynamic noise sources basically comes from the possibility to include such noise sources in specific acoustic solvers. To characterize the sound generated by turbulent fluid motion which is a broadband noise, a spectrum of sound level should be prescribed at any computational point. However the generation of noise is localized close to the solid surfaces hence sound pressure levels will be monitored only on the surface meshes. Anyhow to calculate frequency dependent pressure spectra it is necessary to store the time history of the dynamic pressure; the huge amount of surface nodes (order of 105) and the number of time steps (order of 104) make the storage of all data prohibitively. Only a selection of points, representative of most noisy


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regions, was thus monitored in time to collect the full signal p(t). A spectral analysis is then computed by means of a Discrete Forurier Transform:

tpDFTPF Equation 4

Finally reduced with respect to reference pressure to express the pressure fluctuation level as a function of frequency:

refpPFPFL

log20

Equation 5

This virtual probe output corresponds directly to the pressure signal recorded by an hypothetic surface microphone locate at the same position and can be filtered with typical filtering curves prescribed by standards to adapt sounds to the sensitivity of human hear. In the following Z and A weighted values will be presented.

In order to monitor the distribution of noise generated on all the viscous surfaces and the relative weight of the various sound sources, for all surface points the effective (mean square) value of pressure fluctuation is monitored and a global measure of sound pressure level is calculated. In practice at each iteration pressure fluctuations are computed and averaged over the entire simulation time:

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______2

'' T T

p dt p p dtp

T T

Equation 6

The same reduction as in

refpPFPFL

log20

Equation 5 is applied to compute the

sound pressure level:

______

2

2

'10 log

ref

pSPL

p

Equation 7

Since the frequency dependency is lost for this kind of outputs, no filtering is possible and only Z weighted values are available.

This kind of global parameter cannot be measured directly, at least not in every point, but some experimental techniques, such as the beam forming, permit to reconstruct such values cross-correlating the data coming from a multi-microphone array.

Before analyzing the results obtained on the train mock-up, results obtained on a test case used to validate the procedure are presented.

Validation test The reference test case chosen because of available experimental and other numerical data, the limited geometrical complexity and the quite challenging flow structure, is a simplified car side mirror known in literature as Generic Side Mirror(Hoeld, Brenneis, Eberle, Schwarz, & and Siegert, 1999). The geometry consists of half a cylinder mounted vertically on a flat plate (the equivalent of the car side window) with the free end of the cylinder tapered by a quarter of a sphere. The height of the cylinder is equal to its diameter d (d=0.2 m) for a total height of 1.5d. An air free stream parallel to the


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flat plate moves towards the curved surface of the side mirror: the free stream velocity is U=38.9 m/s which results in a Reynolds number based on the mirror diameter Red=5·105. The free stream Mach number is about 0.11.

The results presented here have been obtained using the same computational setup (in terms of domain size, simulated time and boundary conditions) of the simulations performed by De Villiers (DeVilliers, 2006) except for the velocity condition at the inlet boundary where an uniform velocity equal to U has been imposed (a sensitivity analysis to the inlet condition was performed showing that the velocity profile and the level of turbulence only affect the upstream region whilst they have a negligible effect in the flat plate wake region and near the mirror edges).

In the following the PFL spectra computed at two different locations are reported. The first one (

Figure 6) is located near the mirror edge whilst the second one (

Figure 7) is placed on the flat plate, in the wake region. Numerical results have been compared with experimental measurements in(Hoeld, Brenneis, Eberle, Schwarz, & and Siegert, 1999). Z filtering is


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applied hence no correction is imposed and every frequency weights the same. The agreement is quite good, especially in the wake region whilst the worst prediction near the mirror edge is essentially due to the SpalartAllmaras DES model failure to replicate the laminar flow separation that characterizes the front side of the mirror (Ask & Davidson, 2009).

This kind of agreement was considered sufficient for the purpose of application to train mock-up, since the dimensions in play would lead to an early laminar to turbulent transition and most of the vehicle will observe a fully turbulent flow.

Figure 6 side mirror – near edge pressure probe

Figure 7 side mirror – wake pressure probe

Sound Pressure Spectra In this section the virtual probes pressure fluctuation levels are presented at two points corresponding to the bonnet lateral edge and the internal part of the front bogie. Experimental data collected in a


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dedicated experimental campaign carried out at the Aerodynamic and Aeroacustic Research Center of Pininfarina(Pininfarina) were exploited to check the reliability of the numerical technique. Both the signal from the surface microphone and the virtual probe were A weighted and made dimensionless.

Figure 8 reports the Discrete Fourier Transform of the pressure probe located on the bonnet lateral edge.

Figure 8 PFL/PFLref on the model surface – bonnet lateral edge

Apart from a somewhat underdefinition of the numerical spectrum at the low frequencies the trend of decay at the most relevant frequencies (500-5000 Hz) as well as the peak values are quite well predicted. The estimate of peak frequency is slightly anticipated.

Figure 9 shows the results for a probe located on the surface facing the top bogie cavity. Experimentally the exact probe could not be investigated however two equivalent locations were monitored and reported. As it is possible to note the trend of decay is even better reproduced for this probe, numerical values confirm a slightly slower decay above 5000 Hz where experiments reports a steeper profile.


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Figure 9 PFL/PFLref on the model surface – internal front bogie

The overall agreement is considered sufficient for the purpose of including the estimated aerodynamic sound sources in external acoustic solvers.

Sound Pressure Level Plotted in Figure 10 are the sound pressure levels on the train mock-up; data are suitably scaled against a reference sound pressure level. Main acoustic sources are localized as expected on the bogie, especially on the front one, and on the pantograph compartment on the roof of the second car. Notably the high fluctuations levels connected with the bogies expand downstream on the wagon and also concern the second car in the case of the back bogie. Inside the pantograph compartment the most noisy zone is the one corresponding to the back edge where high velocity flow impacts the flat confinement surface.

(a

(b

(c

Figure 10 SPL/SPLref maps on the train mock-up – a) lateral; b) top; c) bottom view

Three other critical zones, even though at lower intensity, are located on the side of the front window, on the back window near the symmetry plane and on the gangway. The sound sources on the front window are confined in a precise area corresponding to the bonnet lateral edge and immersed in a large low noise area extending from the vehicle nose to the recess and including all the


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roof of the first car. The other two secondary sources are viceversa more spread and less intense but are located in zones characterized by a rather high background noise.

Last thing to be remarked is the relative silenceness of the top and lateral sides compared to the bottom one where the two bogies promote higher sound pressure levels.

Conclusions A computational method to predict surface pressure fluctuations aiming at evaluating aerodynamic noise sources was implemented. Exploiting hybrid unstructured meshes, Detached Eddy Simulation and specific acoustic post-processing tools a CFD method was developed in the framework of the open-source code OpenFOAM.

A 1:5 scaled model including one and a half wagon and two bogies cavities was simulated at the nominal speed of 70 m/s. Pressure fluctuations at selected points were monitored in time and analyzed in frequency while overall sound pressure levels were computed on the full mock-up body and presented as bidimensional maps. Comparison with experiments showed a good agreement both in terms of frequency dependency and of peak values registered.

Bibliography Ask, J., & Davidson, L. (2009). A Numerical Investigation of the Flow Past a Generic Side Mirror and its Impact on Sound Generation. Journal of Fluids Engineering , 138.

DeVilliers. (2006). The potential of Large Eddy Simulations for the modeling of wall bounded flows.London: Imperial College, PhD Thesis.

Hoeld, R., Brenneis, A., Eberle, A., Schwarz, V., & and Siegert, R. (1999). Numerical simulation of aeroacoustic sound generated by generic bodies. AIAA Journal , 99-1896 .

Issa. (1986). Solution of the Implicitly Discretized Fluid Flow Equation by Operator Splitting. Journal of Computational Physics , vol. 62, pp. 40±65.

OpenCFD. (n.d.). OpenFOAM web site. Retrieved from http://www.openfoam.com/

Pininfarina. (n.d.). Aerodynamic and aeroacustic research center. Retrieved from http://arc.pininfarina.com/english/index_en.html

Spalart, Allmaras, Strelets, & Jou. (1997). Comments on the Feasibility of LES for Wings and on the Hybrid RANS/LES Approach. Advances in DNS/LES, Proceedings of the First AFOSR International Conference on DNS/LES.

Van Leer. (1992). Flux-vector splitting for the Euler equations. Lecture Notes in Physics, Springer , 507–512.

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