Acoustics Brochure

Modeling Acoustics in FLUENT

In recent years, as engineering design of components and systems has become increasingly sophisticated, a significant amount of effort has been directed toward the reduction of aerodynamically generated noise. With the ongoing advances in computational resources and algorithms, CFD is being used more and more to study acoustic phenomena. Through detailed simulations of fluid flow, CFD has become a viable means of gaining insight into noise sources and basic sound production mechanisms.

includes cases where the frequency range of interest is fairly narrow, the sources and receivers are located close to each other, and the sound to be captured is fairly loud. The sound generated by an open car window (see page 5) is one example, and the sound produced by a side view mirror is another [1, 2]. For both of these cases, the CAA results are in good agreement with experimental measurements.

CAA has also been used successfully to predict whistles (loud tones) produced by automotive air intake systems. The whistling FLUENT offers four sound is caused by an air approaches for simulating jet passing underneath aeroacoustics. In order of the throttle plate (Figure decreasing computational 1). As it passes over a effort, these are computasump cavity, a shear tional aeroacoustics layer is established. If (CAA, or the direct resonance occurs method), the coupling of between the flapping CFD and a wave-equashear layer and sound tion-solver, integral acoustic models, and Figure 1: Instantaneous velocity magnitude contours in a CAA simulation waves bouncing off the whistles generated in an automotive air intake system; the shear sump bottom, a loud broadband noise source of layer flapping in the mouth of the sump cavity causes a loud whistle whistle develops. The models. sound spectrum predicted by a CAA simulation (Figure 2) is in excellent agreeComputational Aeroacoustics Computational aeroacoustics is the most comprehensive ment with the corresponding experimental measurement way to simulate aeroacoustics. It does not rely on any [3, 4]. The CAA simulation predicts almost the exact model, so is analogous to direct numerical simulation same whistle frequency and sound pressure level (SPL) (DNS) for turbulent flow. CAA is a transient simulation as measured in the experiments. of the entire fluid region, encompassing the sources, receivers, and entire sound transmission path in between. By rigorously calculating time-varying flow structures, pressure disturbances in the source regions can be followed. Sound transmission is simulated by resolving the pressure waves traveling through the fluid. While CAA is the most general and accurate theoretical approach for simulating aeroacoustics, it is unrealistic for most engineering problems because of a number of practical limitations, including widely varying length and time scales characteristic of the sound generation and transmission phenomena, and widely varying flow and acoustic pressures. While these constraints render CAA unsuitable for most practical situations, there is a small class of engineering problems to which it can be successfully applied. This

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Figure 2: Computationally predicted (using CAA) and experimentally measured sound spectrum showing a loud whistle generated by an automotive air intake system [3, 4]

CFD-Sound Propagation Solver Coupling The computational aeroacoustics approach is prohibitively expensive for most practical problems due to the large difference in time, length, and pressure scales involved in sound generation and transmission. Computational expense can be greatly reduced by splitting the problem into two parts: (1) sound generation and (2) sound transmission. With this approach, sound generation is modeled by a comprehensive transient CFD analysis, while a wave equation solver, such as SYSNOISE from LMS International, or ACTRAN from FFT, is used for analyzing sound transmission. These software products solve the wave equation using the boundary element method (BEM). In one recent example, FLUENT was used to simulate the transient flow field around a generic sideview mirror. Time-varying static pressure was recorded on the mirror surfaces and base plate and exported to SYSNOISE. The output includes a spatial distribution of the sound level as a function of sound frequency (Figure 3).

The FW-H approach has been used to study the sound generated by flow over a cylinder of diameter D. Using the LES model, the 2D unsteady flow solution (Figure 4) is characterized by a predominant frequency Strouhal number of 0.19, compared to a measured value of 0.187. At an observer distance of 35D, the predicted sound pressure level is 114 dB, compared to an experimental value of 117 dB [7]. At a distance of 128D, the predicted and experimental SPL values are 102 and 100 dB, respectively.

Figure 4: Line contours of vorticity magnitude at one instant during the unsteady flow past a 2D cylinder, modeled using LES Figure 3: SYSNOISE prediction of sound pressure level for an automotive side-view mirror, based on flow-induced sources predicted in FLUENT

Integral Acoustics Methods The approach of splitting the flow and sound fields from each other and solving for them separately can be simplified further if the receiver has a straight, unobstructed view of each individual point that is a source of noise. Sound transmission from a point source to a receiver can be computed by a simple analytical formulation. The Lighthill acoustic analogy [5] provides the mathematical foundation for such an integral approach. The FfowcsWilliams and Hawkings (FW-H) method [6] extends the analogy to cases where solid, permeable, or rotating surfaces are sound sources, and is the most complete formulation of the acoustic analogy to date. The FW-H method is implemented in FLUENT.

The FW-H approach has also been applied to the generic side view mirror mentioned earlier. The LES model was used for the 3D flow calculation in the region surrounding the mirror. An iso-surface of vorticity, colored by velocity (Figure 5) illustrates the complex, transient nature of the flow. Using this solution, the sound pressure levels were computed at several microphone locations. Figure 6 shows the spectrum at one receiver for the FWH calculation and the corresponding CAA calculation. Both are in good agreement with data [1].

Figure 5: Contours of velocity are plotted on an iso-surface of vorticity magnitude, while line contours of pressure are shown on the plate at left

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In summary, FLUENT offers four ways for simulating aeroacoustics. These range from highly accurate, but expensive methods to quick and approximate approaches. All of these methods are included in the standard FLUENT software; no add-on modules are necessary. !

Figure 6: The Direct (CAA) and FW-H approaches are both in good agreement with experiment for a receiving point not far from the mirror Data courtesy of DaimlerChrysler

Broadband Noise Source Models The three methods described so far require well-resolved transient CFD simulations, since they aim to determine the actual time-varying sound-pressure signal at the receiver, and from that, the sound spectrum. In several practical engineering situations, only the locations and relative strengths of sound sources, rather than the sound spectra at the receivers, need to be determined. If the sound is broadband (without any prominent tones characterized by sharp peaks in the spectrum), the source strengths can be evaluated with reasonable accuracy from the time-averaged structure of the turbulent flow in the source regions. Turbulence is the primary cause of sound in aeroacoustics, so in a broad sense, regions of the flow field where turbulence is strong produce louder sources of sound. FLUENT 6.2 includes a number of analytical models referred to as broadband noise source models which synthesize sound at points in the flow field from local flow and turbulence quantities to estimate local sound source strengths. The key advantage of these models is that they require very modest computational resources compared to the methods described in the previous sections. Broadband noise models only need a steady state flow solution, whereas the other methods require wellresolved transient flow solutions. One example recently studied involves the prediction of prominent sound sources around a simplified sedan (Figure 7), using Lilley’s acoustic source strength broadband noise model [8].

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References: 1. R. Siegert, V. Schwarz and J. Reichenberger, AIAA Paper No. 99-1895. 2. B.S. Lokhande, S.D. Sovani and J. Xu, SAE Paper No. 2003-01-1698. 3. V. Kannan, J. Seifert, T. Golletti and D. Hanner, SAE Paper No. 2004-01-0395. 4. V. Kannan, S.D. Sovani, D. Greeley and A.D. Khondge, Submitted to SAE NVH Conference, May 2005. 5. M.J. Lighthill, Proc. Royal Society A 211, p. 564 (1952). 6. J.E. Ffowcs-Williams and D.L. Hawkings, Proc. Royal Society of London A 264, pp. 321-342 (1969). 7. Revel, Lockheed Report 28074. 8. G.M. Lilley, The Radiated Noise from Isotropic Turbulence Revisited, NASA Langley Research Center ICASE Report 93-75; NASA CR-191547.

Figure 7: An iso-surface of Lilley’s acoustic source strength shows prominent wind noise sources on a generic sedan

The Sound of Side Window Buffeting

Wind buffeting, the noise and pulsating forces that are experienced when driving a car with the side windows open, has become a significant factor in the overall passenger experience in recent years. It is caused by an unstable shear layer that is established at the upstream edge of the window opening. Disturbances are shed from this location and travel along the side of the vehicle. When they reach the rear edge of the window opening, a pressure wave is generated that propagates both inside and outside the passenger compartment. Outside the vehicle, this wave propagates both forward and backward along the side of the car. When the forward traveling wave reaches the front edge of the opening, it triggers another disturbance that moves back to the rear edge. This process is repeated many times every second and causes the shear layer to develop a characteristic buffeting frequency, which depends on the speed of the auto-

Figure 1: The model geometry of the car exterior and interior with the front left window open

mobile and the geometry of the opening. Often the frequency is below the range that can be heard by human ears but it still can be felt by passengers as a pulsating wind force. Wind buffeting can be detected using microphones, but the complicated pressure waves that are its cause are very difficult to measure. As a result, engineers in the past have had to wait until relatively late in the design process when prototypes become available to measure this phenomenon. These measurements typically give them little or no information about what areas of the design are affecting wind buffeting and what could be done to reduce it. The only option is to modify and test the prototypes to see whether individual changes have any effect. This process is so costly and time-consuming that it is difficult to identify changes that will improve the design.

At DaimlerChrysler, engineers have been using the computational aeroacoustics (CAA) approach in FLUENT to simulate wind buffeting. In two recent studies [1, 2], the modeling process began by importing a surface model of the outer shape of the vehicle and a CAD model of the vehicle interior (Figure 1) into a CFD preprocessor. The simulation domain was defined to include the entire passenger cabin, which is connected to the external flow domain through an open window. Dummies representing the passengers were included in the model to correctly represent the volume of the passenger compartment. The vehicle surface was modeled to a significant degree of detail to capture flow development from the vehicle front end to the window opening. Several levels of local mesh refinement were used. Two refinement levels were applied outside the vehicle to capture the wake behind the vehicle. One refinement level was applied inside the passenger compartment to capture wave propagation inside the cabin. The finest refinement level was applied at the area of the opening to capture the shear layer. A close-up view of the surface mesh near the open window is shown in Figure 2. The turbulent flow was captured using the RNG k-ε and LES turbulence models, both of which have been shown in the past to provide good results for a range of turbulent conditions. Interior surfaces of the vehicle were assumed to be solid walls instead of soft surfaces such as carpeting or fabric. Actual car surfaces are less reflective and more absorbent than solid walls, giving the model a

Figure 2: The surface mesh detail for the car exterior and interior in the vicinity of the open front window

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tendency to overpredict the wind buffeting phenomena.

and propagation of these waves can help engineers understand exactly how wind buffeting occurs in a particular design, and can help them iterate quickly to an improved design.

A steady-state solution was first obtained for each case, and these results were used as initial conditions for a subsequent series of The frequency spectra and sound transient simulations that were pressure level were in good agreeused to capture the pressure flucment for all locations studied tuations in the vicinity of the Figure 3: Pressure field at a speed of 60 mph and yaw angle of 5 degrees, showing the wakes within the vehicle (Figure 4). open window. Monitors were set behind the A-pillar and mirror Additional simulations correlated up at the driver’s and front seat well with experimental measurepassenger's ear locations and ments in predicting reductions in static pressure was recorded at the SPL and frequency of the these locations at every time sound from an open front window step. The initial transients died as the vehicle speed is reduced down and the pressure traces from 60 mph to 50 mph. reached a dynamically steady Simulations with the left front winsolution in roughly 300 to 500 dow wide open and the right rear time steps. Subsequently, pressure window open 1 inch were also pertraces were recorded for time formed. These showed that buffetperiods between 1.0 and 2.0 secing was substantially reduced. onds - long enough to obtain a Simulations with a modified side sound pressure spectrum. The mirror design reduced buffeting by pressure signals were converted Figure 4: Spectrum of the side window buffeting sound heard by a car driver 13 dB, which also correlated well to the sound frequency spectrum with experimental measurements. by taking a discrete Fourier transform using a Hanning window filter. The sound DaimlerChrysler engineers are making use of these results by simulating other vehicles, evaluating the influence of pressure level (SPL) was finally converted to dB units. more parameters, and evaluating different modeling techThe CFD predictions were validated by comparing them niques. As simulation is more fully integrated into the to experimental measurements conducted in a wind tun- design process, this approach should make it possible to nel. The CFD simulations accurately predicted buffeting substantially reduce wind buffeting in the future. ! frequency and sound pressure level, and matched SPL Courtesy of DaimlerChrysler and frequency variation trends observed in the experiments. Contours of pressure were examined on two hori- Reference: zontal planes in the critical front window area (Figure 3). 1. D. Hendriana, S.D. Sovani, M.K. Scheimann, “On The results indicated that a vertical vortex occurs behind Simulating Passenger Car Window Buffeting,” SAE the A-pillar (the structural member between the windPaper No. 2003-01-1316 (2003). screen and the front window). An animation of the tran- 2. C.-F. An, S.M. Alaie, S.D. Sovani, M.S. Scislowicz, sient solution showed vortex movement with the local K. Singh, “Side Window Buffeting Characteristics of flow, with impingement on the B-pillar (the structural a SUV,” SAE Paper 2004-01-0230 (2004). member between the front and rear windows). The wave generated at the B-pillar was shown to propagate into the passenger compartment. Visualization of the formation

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Cavity Noise Generation

Cavity flows have been the subject of research since the 1950’s. Although geometrically simple, the fluid dynamics in such flows is complicated, involving shear layer instability, flow induced resonance, and turbulence. Flow over a cavity causes large pressure oscillations to develop, and these can lead to structural damage. Suppression techniques have been applied with varying degrees of success. The flow generated by an open cavity with transonic flow across the top combines several clearly identifiable flow phenomena, such as a mixing layer with its train of large structures, recirculation flow in the cavity, pressure waves generated by the crossing of the structures, and strong acoustic coupling between all these phenomena. Such flows occur in landing gear wells and bomb bays on aircraft, where sonic fatigue and the reduction in pressure fluctuations and noise are of prime concern. The pressure vents on the space shuttle cargo bay have also been observed to cause high internal noise levels during ascent.

Figure 1: The complex flow inside the cavity

In this example, a shallow rectangular cavity, 20 inches in length and 4 inches square in cross-section, is studied. A bulk Mach number of 0.85 outside the cavity is specified. The turbulent flow is modeled using the detached eddy simulation (DES) approach, whereby a RANS calculation (in this case, using the Spalart-Allmaras model) is performed in the near-wall region, and an LES calculation is performed in the free stream. A hexahedral mesh of 1.4 million cells is used. Using the non-iterative time advancement (NITA) solver in FLUENT, a period of 0.6 seconds is simulated, and noise calculations are performed using the computational aeroacoustics (CAA) approach. Time averaged results from the last 0.2 seconds of the simulation are used to compute the frequency spectrum of the noise produced for comparison to experiment. In Figure 1, an iso-surface of vorticity, colored by velocity, illustrates the complex, transient nature of the cavity flow field. The vorticity is being generated principally in the free shear layer, although the figure only shows the cavity gen-

Figure 2: PRMS along the cavity ceiling for the first mode; FLUENT DES predictions are compared to experiment [1]

Figure 3: Sound pressure level (SPL) 7 inches downstream of the cavity, computed using DES, URANS, and measured by experiment [1]

eration. The growth of the boundary layer along the entry plate is visible in the form of spreading oil film lines. Following the separation of the boundary layer from the leading edge of the cavity, Kelvin-Helmholtz instabilities develop and pulsate during the transient flow, causing regions of localized shocking to appear and disappear within the unstable shear layer. Rossiter first developed an empirical formula for predicting cavity-flow resonant frequencies, today referred to as Rossiter modes. For this configuration the first three Rossiter modes (peaking at 145Hz, 350Hz and 590Hz respectively) are of a similar strength across the ceiling of the cavity. Each modal band is calculated by processing the power spectral density, using frequencies that bracket the peak. The RMS pressure along the cavity ceiling, PRMS, for the first mode is in very good agreement with experimental data [1], as shown in Figure 2. The sound pressure level at one of ten microphone locations is compared to experimental data [1] in Figure 3. The DES approach provides a frequency spectrum that is in very good agreement with experiment, while an unsteady RANS calculation does not. This result is consistent at every monitor point considered in the study. ! Reference: 1. Experimental data provided by QinetiQ, funded by UK MOD Applied Research Program.

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Automotive Rain Gutter Noise

A recent survey by J.D. Power and Associates [1] indicates that excessive wind noise is a major concern for automobile passengers. Wind noise is generated by features on the outer body of a car, such as roof-racks, door-gaps, and side-view mirrors. One prominent feature contributing to the overall wind-noise level is the A-pillar rain gutter. This rain gutter serves the purpose of collecting and draining rain water that would otherwise be swept from the windshield, past the A-pillar, and onto the side windows, thereby reducing the visibility through them. It has the shape of a long narrow channel spanning the length of the A-pillar and facing the wind. From an aerodynamics perspective, the rain-gutter acts as a turbulator, creating a highly turbulent flow in its wake. Pressure fluctuations created by such turbulence contribute to wind noise.

cost. A more economical approach makes use of LES to compute the time-varying pressure field (the noise sources), and a simple acoustic analogy to compute the sound transmission. This second approach has been applied to the rain gutter, using the Ffowcs-Williams and Hawkings [2] acoustics model to compute the sound transmission. The goals of the simulation are to determine: 1. the transient flow structure around the rain-gutter 2. the distribution of the pressure coefficient, Cp, along the base plate 3. the frequency spectrum of the static pressure at a point on the base plate downstream of the rain gutter, and 4. the frequency spectrum at a point above the rain gutter. The results from the CFD simulations are compared to experimental and computational data reported by Kumarasamy and Karbon [3]. A diagram of the solution domain is shown in Figure 1. The rain gutter is located a distance a from the inlet surface AEHD. The distance a is approximately 7.4b, where b is the height of the rain gutter (0.0127 m). The length of the rain gutter, c, is about 0.64b. Surface ABCD is a symmetry plane.

Figure 1: The outline of the domain, showing the rain gutter, inlet, and microphone

With recent advances in CFD models and algorithms and with increases in computational power, it is now possible to study wind noise generation and transmission using CFD simulations. The present work focuses on the wind noise produced by an idealized rain gutter. The results are compared with experimental data and other CFD simulations reported in the literature. The idealized rain gutter is a backward facing elbow mounted on a flat plate. The flat plate and gutter are placed in a virtual wind tunnel with a rectangular crosssection. The free stream air speed is 22.35 m/s, corresponding to a Reynolds number of 40,000, based on the height of the rain gutter. The width and height of the raingutter are both 0.0127 m. The large eddy simulation (LES) model is used for the simulation. This transient turbulence model can be used to predict both the sources and transmission of sound, but at a high computational

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Figure 2 illustrates the turbulent flow structures generated by the rain gutter. Iso-surfaces of vorticity are colored by velocity magnitude to illustrate the flow. Contours of pressure are shown on the base plate and perpendicular wall.

Figure 2: Iso-surfaces of vorticity behind the rain gutter, colored by velocity magnitude, and pressure contours on the base plate and symmetry plane

Instantaneous velocity vectors on the symmetry plane are shown in Figure 3. The flow separates upstream of the rain gutter, and two very distinct flow regions develop. The free stream flow outside the separated region is steady, while that inside is complex and unsteady.

Reference: 1. 2000 Vehicle Acoustic Study, J.D. Power and Associates, Westlake Village, CA 91361. 2. J.E. Ffowcs-Williams and D.L. Hawkings, Proceedings of the Royal Society of London A264, p. 321-342 (1969). 3. A. Kumarasamy and K. Karbon , Aeroacoustics of an Automobile A-Pillar Rain Gutter: Computational and Experimental Study, SAE Paper 1999-01-1128 (1999). Figure 3: Velocity field on the symmetry plane

The distribution of the pressure coefficient, Cp, along the flat plate at the symmetry line is shown in Figure 4. The position of the rain gutter is shown. The experimental data shown is from Kumarasamy and Karbon [3]. The FLUENT results are in very good agreement with the data. The spectrum of time-varying pressure recorded at the intersection of the base plate and symmetry plane, 0.0254 m downstream of the rain gutter’s vertical surface, is shown in Figure 5. The FLUENT results are in good agreement with the experimental measurements [3]. The sound spectrum at the far-field microphone, located 0.10795 m above the rain gutter on the symmetry plane, is shown in Figure 6. The results are again in good agreement with published values. In summary, FLUENT has been used to simulate the flow field around an idealized automotive A-pillar rain gutter and the sound radiated from it. The LES turbulence model was used to compute the transient flow field, and sound radiation was calculated with the Ffowcs-Williams and Hawkings integral method. Numerical results were compared with corresponding experimental measurements reported in the literature. The time-averaged pressure coefficient values predicted by the FLUENT simulations were found to match (within the bounds of experimental uncertainty) the measured steady-state values. The spectra of pressure at a point on the base plate and at a far-field microphone were within a few dB of the corresponding experimental measurements over a wide frequency range. Overall, the results demonstrate that the LES turbulence model coupled with the FfowcsWilliams and Hawkings method are well suited to acoustics simulations of this type. !

Figure 4: Pressure coefficient as a function of position along the base plate, with comparisons to experiment [3]

Figure 5: Sound spectrum at a microphone located behind the rain gutter on the base plate

Figure 6: Sound spectrum at the microphone located above the rain gutter

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Low Noise Landing

Landing gear noise is not the first thing that comes to mind when thinking about noise pollution at a busy airport. The once dominant jet engine noise has been reduced significantly over the past thirty years, primarily through the introduction of high bypass turbofan engines. As a result, airframe noise has emerged as a leading component of aircraft noise during the final approach phase of a landing. Environmental concerns and noise certification regulations are therefore causing aircraft manufacturers to take a closer look at this phenomenon. The main contributors to airframe noise in a landing configuration are high-lift devices, such as slats and deployed flaps, and surprisingly, the landing gear. Measurements have shown that these components are not equally important on all aircraft. While the high-lift devices are noisier on medium size aircraft, the landing gear is becoming the dominant source on large airplanes, such Figure 1: Vortical structures visualized as the Boeing 777. using iso-surfaces of the second invariant of the deformation tensor, colored by velocity magnitude

Landing gear systems have complex, non-streamlined geometries, and generate highly turbulent wakes. Vortices shed from one component impinge on other elements, generating noise with a broad spectrum, from a few hundred Hz to several kHz. If noise can be predicted using engineering software, modifications such as fairings and streamlining can be introduced during the design phase.

At Fluent, engineers have recently analyzed a 1/10th scale landing gear model, representative of the gear used on a Boeing 757 aircraft. The same configuration has been studied using CFD by researchers at Penn State [1] and NASA Langley [2, 3], and will also be tested in a wind tunnel at the Quiet Flow Facility at the Langley Research Center. The four-wheel landing gear assembly contains all of the major components, including the oleostrut, axles, connecting blocks, diagonal struts, a door, and additional parts that hold the configuration together. A flat plate simulates the wing surface.

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Predicting aeroacoustic noise is not a trivial matter. Only a minute fraction of the kinetic energy present in the primary flow is converted into acoustic energy and radiated. To correctly capture the acoustics, the turbulent flow must be calculated with high fidelity. Since turbulence is an inherently unsteady phenomenon, a time-consuming transient simulation is required. The large eddy simulation (LES) turbulence model with the Smagorinsky subgrid scale model was used for the landing gear calculation. Integral techniques that predict the far-field acoustic signal using source data input from a near-field CFD simulation have emerged as a promising and economic way to compute sound levels. The Ffowcs-Williams and Hawkings (FW-H) approach [4], the most universal and complete integral method available today, was used for the simulation. The CAD (STEP) model provided by NASA Langley was cleaned up in GAMBIT, and a computational grid was built using GAMBIT and TGrid. Boundary layer prisms were grown in TGrid, so that the prism cap surface mesh could be used to control the growth and continuity of the tetrahedral elements away from the boundary layer. Size functions were used to cluster elements in the vicinity and wake of the landing gear, resulting in a 5.3 million cell mesh, suitable for an LES simulation. The landing gear case was run incompressibly (a valid approximation for compact sound sources) for nearly 10,000 time steps, or one flow-pass through the domain, before the turbulence statistics were sufficiently stabilized and the acoustic source data sampling could be started. The acoustic source data was extracted directly on the landing gear surface over approximately one additional flow-pass, and then processed with the FW-H solver. The FW-H tool is ideal for predicting far-field radiation in the absence of external scattering surfaces. The necessary source data can be extracted from permeable (interior) or

Figure 2: Dipole source strength, using contours of dp/dtRMS, shows a high source intensity at the rear diagonal strut and behind the oleo-strut

solid (wall) surfaces, and the method is not very sensitive to the actual source surface placement. The direct output includes the far-field pressure signals at user-specified receiver locations. Postprocessing tools are available to perform spectral analyses of these signals, including overall sound pressure level (OASPL) outputs. Also available is the local dipole source strength, which can be used to assess contributions from different source locations. Surprisingly, but in good agreement with other studies performed on the same configuration [2, 3], flow visualization revealed that the two diagonal struts shed nearly as much vorticity as the big wheels (Figures 1 and 2). A very short distance downstream of the landing gear, it is difficult to differentiate the flow structures originating from different components. Persistent flow separation due to an asymmetric flow was observed at the gear door leading edge. Animations of unsteady surface pressure showed more complex patterns on the rear wheels and rear strut, as expected. The acoustic analysis indicated that the overall sound pressure levels at a distance of 10 wheel diameters upstream and downstream of the landing gear are about 4dB lower than those measured in the two lateral directions (Figure 3). Differences were also noticed in the sound pressure spectra (Figure 4). The lateral directions peak at around 700 Hz, and the same frequency was observed to be dominant in the crossflow force response. The streamwise spectra peak at considerably higher frequencies. A total of 18 surface pressure probes were strategically placed in the rear of the wheels, struts, and along the wheel door. The recorded pressure traces confirmed that the rear diagonal strut is one of the dominant noise sources. Fluent engineers are anxiously awaiting the experimental data expected from the wind tunnel to confirm these findings. !

Figure 3: Overall sound pressure levels (OASPL) for five receivers located 1 m from the landing gear

Figure 4: Sound pressure level spectra (dB) for four of the receivers shown in Figure 3

References: 1. F.J. Souliez, L.N. Long, P.J. Morris and A. Sharma, International Journal of Aeroacoustics 1, No. 2, p. 115-135, 2002. 2. F. Li, M.R. Khorrami and M.R. Malik, AIAA Paper 2002-2411, 8th AIAA/CEAS Aeroacoustics Conference, Breckenridge, CO, June 17-19, 2002. 3. D.P. Lockard, M.R. Khorrami and F. Li, AIAA Paper 2004-2887, 10th AIAA/CEAS Aeroacoustics Conference, Manchester, UK, May 10-13, 2004. 4. J.E. Ffowcs-Williams and D.L. Hawkings, Proceedings of the Royal Society of London A264, p. 321-342 (1969).

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