Numerical Simulation Methods for Aeroacoustics With An Overview of Industrial Test Cases.

Alex Read, Fred Mendonça CD adapco Group, 200 Shepherds Bush Road, London, W6 7NY, UK

Abstract

A modelling process, for evaluation of Aeroacoustic cases, is presented: with emphasis on model optimization through post-processing of steady-state results and validation of transient calculations. Examples are then given, of the application of this process on industrial problems, with comparisons to experimental results. In each example, very good agreement with experiment was attained within a limited frequency range.

1. INTRODUCTION

A design criterion of increasing importance, particularly in the transport industries’ consideration of passenger comfort, is noise induced by fluid dynamics: Aeroacoustics (AA). Historically, little computational work - within ‘real world’ design cycles - has been carried out: due to the prohibitive computational cost of performing time varying Computational Fluid Dynamics (CFD) calculations; the requirement for Large Eddy Simulation (LES) turbulence models and their ensuing mesh sizes; and the volume of data involved in obtaining frequency domain information. With the continued decrease in hardware cost and the increase in performance, Computational Aeroacoustics (CAA) calculations are now being performed by an ever-increasing number of industrial CFD engineers. The responsibility then falls on commercial software vendors to obtain understanding of these phenomena, their computational requirements, define ‘best practices’ and provide a toolkit with which the industrial sector can work. The CD adapco Group (CDaG), the purveyors of the commercial CFD code STAR-CD, have answered this responsibility by implementing and validating such a toolkit.

Embedded in the implementation and validation of this process is the collaboration of industrial partners: both end-users and other software vendors. The worked detailed within was, and continues to be, carried out within the framework of the DESTINY-AAC and DESTINY:2 projects: a consortium of companies progressing the experience of predictive aeroacoustic techniques in an industrial framework. The AA process consists of three steps; A steady-state calculation, post-processing and case optimization; Transient calculation and acoustic source quantification; Noise propagation.

This paper outlines the AA process that we have developed, then shows it applied to industrial applications with comparisons with experiment.

2. THE AEROACOUSTICS PROCESS

2.1 Steady-state

Prior to running a transient calculation, a steady-state simulation is very often performed: the most frequent reason being to supply a representative flowfield to be used as an initial guess. Due to the significant CPU time required for transient calculations, the more understanding that can be obtained from steady-state work, the better. With this in mind, two steady-state post-processing tools have been developed to enable the user to optimize their case set-up at this early stage.

2.1.1 Lilley source term

Fluctuating velocity components are re-synthesized [1] using information from steady-state calculations with RANS-based turbulence models. Once re-synthesized, the fluctuating velocities may then be used to approximate quadrupole sources, as defined by some suitable acoustics analogy [2]. Figure 1 illustrates the approximate sources arising from the flow over an idealised side-view mirror [3]: showing an iso-surface of the magnitude of the Lilley-analogy turbulent shear term. The largest shear-noise sources originate from the flow-shear between the separated and bulk flow regions. There are three benefits in using this method. First, relative source magnitudes show which locations are predominant, or which comparative designs are superior. Also enabling the user to focus mesh refinement on regions which are of most interest. Secondly, it provides useful information as to where to monitor quadrupoles in a transient calculation. Thirdly, the method is quick, adding only a few percent to the steady-state calculation time as a post-

processing function. To extract acoustic spectra it is necessary to run a transient calculation [4].

Figure 1: Iso-surface of a high value of the Lilley source term, on the idealised side-view

mirror.

2.1.2 Mesh cut-off frequency

This enables the user to estimate, from a steady-state flowfield, up to what frequencies local acoustic sources can be captured on a specific mesh. Therefore, if the mesh is insufficiently refined to resolve the frequencies of interest, this can be identified and rectified before progressing to the more time consuming transient calculation. Figure 2, shows a contour plot of the cut-off frequency estimate for the idealised side-view mirror.

Figure 2: Contour of mesh cut-off frequency and volume mesh for idealised side-view

mirror.

In the main acoustic source region, corresponding to the region identified in Figure 1, the mesh is able to resolve frequencies up approximately 1000 Hz. The sharp changes in resolvable frequency, observed in Figure 2, relate to the corresponding change in mesh density at these points.

2.2 Transient calculation

2.2.1 Detached Eddy Simulation (DES)

Narrowband AA noise sources are associated with large periodic motions, e.g. vortex shedding or blade passing, whereas broadband AA noise sources are associated with small-scale turbulent structures. It is important for any simulation tool to be able to capture both types of motion. This requires a LES-like turbulence model to be used, since the more commonly used Reynolds Averaged Navier-Stokes (RANS) methods have been found to be too dissipative [4,10]. In order to obviate the near-wall mesh requirement of LES modelling, Detached Eddy Simulation [9] is suggested [5] which is implemented in STAR-CD together with a full compliment of variants (Spalart-Allmaras, k-ε and k-ω-SST).

2.2.2 Hybrid wall function Particular problems, when solving transient calculations, are incurred when trying to satisfy the y+ constraint for a given near-wall turbulence model; since the velocity in the near-wall cells will differ considerably at different points in time. In order to manage this potential source of error, the hybrid wall function was implemented. This changes from a low- to a high-Reynolds number wall function, dependent on the local, instantaneous, y+ value.

2.2.3 Idealised Side-view mirror example The pressure spectrum at a (typical) point in the wake [3] is shown in presented in Figure 3. In the low frequency range, up to 1kHz, the comparison with experiment is excellent. A tail-off in the predicted spectrum is observed at approximately 1000Hz, which compares well with the estimated steady-state mesh-related cut-off frequency (Figure 2). This model contained approximately 1million cells. The calculation took 1.9-days on 8 x 2.80GHz processors to complete 0.5 seconds of elapse time, using a time-step size of 0.4E-04 seconds.

Figure 3, STAR-CD versus experiment for monitor location 121 [3].

2.2.4 Data output for Computational Acoustics (CA) codes Due to the low amplitude of acoustic pressure waves, higher-order discretisation schemes are necessary to model their propagation. Present general CFD practices for unstructured meshes guarantee second order accuracy, but not higher. For this, coupling to a propagation tool is required e.g.[5,7]. Therefore, coupling has been implemented between STAR-CD, and SYSNOISE and Actran/TM. Since the process of assembling equivalent acoustic source for a CA code requires storing the time history of flow variables, file sizes can become very large. In order to reduce these file sizes, a coarser acoustics mesh, sufficient for CA requirements, can be defined prior to running a transient CFD simulation and data output onto the acoustics mesh points only.

3. INDUSTRIAL EXAMPLES

3.1 BEHR Isolated HVAC duct The HVAC system is a predominant source of noise: clearly audible in the passenger cabin, especially at high fan operating speeds. This is a major cause of complaints from customers: long-term exposure to low levels of noise can also increase driver fatigue. Component suppliers who demonstrate quiet operations forge a competitive advantage. In order to reduce the number of unknowns to be evaluated, BEHR defined an HVAC duct, isolated from the rest of the HVAC system and simplified by fixing the position of the flaps, closing off all but one flap and by removing all liners and trims. The geometry can be seen in Figure 4, below.

Figure 4: Isolated HVAC geometry and monitor locations. Next, experimental measurements were taken in the BEHR’s semi-anechoic chamber, of pressure fluctuations with time, at points in the wake of the flap: see Figure 4.

Figure 5: Section slice through computational mesh generated with pro-STAR’s automatic mesh generation module.

A mesh was then generated using pro-STAR’s automatic mesh generation module. pro-STAR uses trimmed cell technology, whereby the core volume of the domain is fill with perfect cubes. Where these cells abut the surface, they are trimmed to fit the geometry. This means that the bulk of the domain is filled with zero-skewness cells: minimizing the amount of numerical dissipation: with respect to the numerical scheme. This methodology has repeatedly been shown to perform excellently for AA calculations. The mesh contained approximately three and a half million cells.

Figure 6: Mesh cut-off frequency and Lilley source term on section through domain. Figure 6 shows the estimate of the resolvable frequency, as obtained from the steady-state result. In the main acoustic source generation region, frequencies up to 900 – 1000 Hz can be resolved. This was considered sufficient, since it is close to the peak sensitivity of human hearing and also because obtaining higher frequencies will directly impact on the mesh size, and subsequent transient run times. For the present calculation, the run time was 6.5-days on 8 x 2.80GHz processors to complete 0.5 seconds of simulated time, using a time-step of 0.4x10-4 seconds.

Figure 7: Snapshot of velocity magnitude from DES simulation.

Figure 8: Comparison of STAR-CD (in red) and experiment (in green) at monitor

location 2, for Sound Pressure Level (SPL) versus frequency. Figures 7 and 8 show a snapshot of the velocity magnitude from the DES simulation as well as a comparison of the transient result with experiment. There is excellent agreement between the computed and measured result up to the mesh cut-off frequency (approximately 1000 Hz): as predicted by the steady-state approximation of the maximum resolvable frequency.

3.2 Denso HVAC radial fan The noise from the blower fan in a typical automotive HVAC system was evaluated in isolation: the experimental geometry for which, can be seen in Figure 9.

Figure 9: Experimental prototype, with inlet cylinder and outlet filter removed, and

location of far field monitor locations. Experimental measurements were taken, in the semi-anechoic chamber at Denso Thermal Systems, at far field points. The prototype rotates at 3770 RPM and is composed of: One blower housing (scroll); One impeller with 47 blades; One electric motor;

One cylindrical inlet duct; One rectangular outlet duct with a filter at the outlet.

A mesh was generated using the automatic mesh generation module of pro-STAR, consisting of approximately one and a half million cells. The case was initially run steady-state using the Multiple-Rotating Frames of reference (MRF) method. It was then restarted running full moving-mesh, with the k-ε variant of DES. The flowfield was evolved to a pseudo-periodic condition, which was adjudged to have been reached after eight full rotations of the fan. Data was then output for propagation in CA codes, to the far field observer locations. Two methods were used for the propagation. A Ffowcs-Williams Hawkings (FW-H) rotating dipole method (in the CA code SYSNOISE) and the triple plane matching method (in the CA code Actran/TM). The FW-H method requires the storing of forces on an individual blade of the fan (taken to be representative) over one complete rotation of the fan. The triple plane matching method requires storing pressure fluctuations on planes in the inlet and outlet duct, over one complete fan rotation. Full details of this work can be found in [6] and [7]. Figure 10: Comparison of steady-state RANS and snapshots from the DES calculation.

Figure 10 shows a velocity magnitude on a section slice through the domain for the RANS calculation and snapshots from the DES calculation, at different points on one rotation of the fan. The convection of large eddies can clearly be seen in the DES snapshots. Time-averaged values, from the transient calculation, in the outlet duct were compared with Laser Doppler Anemometry (LDA) results. Good agreement was attained. One complete rotation of the blower takes 0.8-days on 10 x 2.80GHz processors.

Figure 11: Surface acoustic pressure on the exterior model and acoustic pressure in the

far field, in SYSNOISE.

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Figure 12: Computed and measured dB(A) levels at the nine microphone locations, from

SYSNOISE, at the blade passing frequency (BPF). Figures 11 shows the propagated sound fields on the exterior of the blower housing and propagated to far field locations. Figure 12, gives a comparison of the computed result and the experiment for the blade passing frequency (BFP). Good agreement is observed: within 5dB. There is a slight over-prediction at points downstream of the outlet duct. This is attributed to the fact that the filter in the outlet duct in the experimental set-up, was not accounted for in the acoustic radiation.

Figure 13: Computed and measured dB(A) from the triple plane matching method from

Actran/TM, at the BPF. Figure 13, shows the results as obtained from Actran/TM, at the BFP. Good agreement is obtained, except for points 2 and 4, where vibrations of the blower casing are thought to be the dominant noise generation mechanism, but were not included in this (purely) AA model.

3.3 Audi A2 side-view mirror The aeroacoustic behaviour of the external aerodynamics of the Audi A2 [8], with focus on the side-view mirror, is reported in this section. The strong interaction between the A-pillar and wing-mirror wakes, with each other and the side window, is known to contribute to the ‘buffeting’ effect when the window is in its open position. Extensive experimental tests were made in Audi’s AA wind tunnel, of surface pressure fluctuations on the side-view mirror, side window and A-pillar. A steady-state calculation was made on the whole car. Next, a mesh, local to the wing-mirror, was generated (this can be seen in blue in Figure 14). The mesh local to the mirror contained approximately 3.8 million cells. The boundary conditions for the local model were obtained by mapping the result from the full vehicle simulation onto the local domain boundaries. This technique allows detailed analysis of individual components, without the significant overhead of simulating the whole car.

Figure 14: Full vehicle geometry, with localized domain shown in blue [8].

Figure 15: Instantaneous velocity magnitude field.

The time varying flow was then modelled using the DES turbulence model. During this calculation, the pressure variation with time was monitored at points on the surface of the vehicle, which corresponded to the location of monitor points from wind tunnel experiments. A comparison between the computed and measured results can be seen in Figure 16, below.

Microphone 4

Figure 16: STAR-CD versus experiment for SPL against frequency at monitor point 4.

The agreement between the results is very good, even up to very high frequencies. At frequencies below 100 Hz, there is an over prediction, which can be improved by increasing the elapse time of the sample processed.

4. CONCLUSIONS

A computational process, for evaluating Aeroacoustic cases, was detailed, and examples of the application of this process were given on industrial geometries and problems. Particular emphasis was put on model optimization through intelligent post-processing of steady-state results. Results were then compared with experimental data, both for acoustic sources (from STAR-CD) and propagated noise (either from SYSNOISE or Actran/TM). It was shown that the steady-state method gave a good approximation of the frequencies that can be resolved in a transient calculation; on a given mesh and flowfield. In all cases the results match experiment well: validating the approach used.

5. ACKNOWLEDGEMENTS

All of the work presented in the paper was undertaken as part of the DESTINY projects, with much of the work being performed by individual partners. Thanks must be given to them for allowing us to present their work. They are listed below, in the same order as the applications are detailed in this paper: Friedrich Brotz and Michael Schumpf, BEHR GmbH & Co. KG; Fabio Barone, Paolo Durello and Franca Carena, Denso Thermal Systems; Moni Islam, Audi AG.

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