This paper presents beamforming techniques for source localization on aicraft in flight with a focus on the development at DLR in Germany. Fly-over tests with phased arrays are the only way to localize and analyze the different aerodynamic and engine sources of aircraft in flight. Many of these sources cannot be simulated numerically or in wind-tunnel tests because they they are either unknown or they cannot be resolved properly in model scale. The localization of sound sources on aircraft in flight is performed using large microphone arrays. For the data analysis, the source signals at emission time are reconstructed from the Doppler-shifted microphone data using the measured flight trajectory. Standard beamforming techniques in the frequency domain cannot be applied due transitory nature of the signals, so the data is usually analyzed using a classical beamforming algorithm in the time domain. The spatial resolution and the dynamic range of the source maps can be improved by calculating a deconvolution of the sound source maps with the point spread function of the microphone array. This compensates the imaging properties of the microphone array by eliminating side lobes and aliases. While classical beamfoming yields results that are more qualitative by nature, the deconvolution results can be used to integrate the acoustic power over the different source regions in order to obtain the powers of each source. ranking of the sources. These results can be used to rank the sources, for acoustic trouble shooting, and to assess the potential of noise abatement methods.

Flow-induced vibration tests were performed on a beam with a square cross section. The test article was induced into a plunge mode self-excited vibration by forcing air across the beam while it was suspended by leaf springs. The natural frequency and the damping were varied during these tests to alter the variables that contribute the onset of flow-induced vibration. Additionally, cantilevered beams were tested in the same wind tunnel and results proved to be consistent with results from the leaf spring supported beam. The slope of the vertical force coefficient was determined from this testing; this coefficient was determined to be approximately unity, which was roughly 1/3 of the value found in other published data.

The consumer today places greater demands upon the vehicle acoustical engineer than in the past. Product quality has always been associated with a quiet ride. Automotive engineers recognize that the predominant sources of vehicle interior noise are wind, tire-road or rolling noise, and the powertrain. This paper suggests a test protocol for measuring wind and rolling noise using a chassis rolls dynamometer and road tests. Automotive engineers are frequently confronted by customer complaints concerning wind noise. Usually, engineers resort to using wind tunnels to address these concerns and to conduct diagnostic studies to remedy wind noise problems. Unfortunately, wind tunnels are expensive to rent and difficult to schedule. As an alternative, the engineer can learn a great deal about the wind noise of a vehicle by using a chassis rolls dynamometer along with road tests [1,2]. If the chassis rolls surface texture closely matches that of the road surface, the tire-road or rolling noise signal in both situations can be assumed to be equivalent. The powertrain noise source can be minimized by shifting the vehicle into neutral and coasting. Wind noise is a source for the road measurements, but not for the chassis rolls. Hence, the wind noise can be calculated by measuring the cab interior noise for both operating conditions, and subtracting the rolling noise measured on the chassis rolls. The two vehicles tested in this study included a pickup truck and a sport utility vehicle. The acoustical data revealed significantly different rolling and wind noise characteristics. The pickup truck had significantly louder rolling noise, and the wind noise was dominated by low frequency sound. The sport utility vehicle was much quieter overall and was significantly quieter for rolling noise than the pickup. The wind noise of the sport utility vehicle also was dominated by high frequency components. Both vehicles showed that rolling and wind noise trends increase linearly with speed. However, the slope of wind noise data for the sport utility vehicle was much steeper than the pickup, which suggested that it was more sensitive to wind noise as speed increased. Exterior noise data from both vehicles showed that the tire-road signal from the road differed significantly from that of the chassis rolls dynamometer. Rolling & wind noises will become even more critical as the motor vehicle industry adopts hybrid electric and, in the future electric fuel cell vehicles, because powertrain noise sources in the vehicle will likely be reduced. The procedure suggested here provides an inexpensive simple approach to assessing rolling and wind noise in the vehicle.

This Part-2 paper applies Part 1’s theory of sheared rapid distortions to compute broadband noise from flow over large roughness elements, and compares those calculations to recent wind-tunnel measurements. The calculations suggest that shear effects are subdominant in the sound-production process. Post-processing of computed results brings out key features of the theory’s non-equilibrium distorting turbulence. A follow-up analysis makes possible the physical interpretation of the measured acoustic spectral densities in terms of the kinematics of the spatially non-uniform carrier flow.

This paper describes the development of a new method for measuring pass-by sound from trucks and other vehicles using 2-dimensional arrays. The approach provides 2-dimensional quantitative maps “images” of the cross-range and elevation distribution in the vehicle side view. The method is an application and extension of an array technology that was originally used for the characterization of static aeroacoustic sources in wind tunnels. The focus of this work is on identifying and rank-ordering the important contributing sources of passby noise. This development includes two phases: developmental testing at a test track site, and road-side testing at two California State highway sites. The acquisition post-processing allows the “observer” to track the vehicle cross-range in order to create a time sequence of source maps that may be interpreted as both level relationships and directivity patterns. The processing applies both range and approximate Doppler adjustments to spectra as a function of time during pass-by or, equivalently, to vehicle position relative to the array’s center. An image demodulation scheme is shown to clarify the images. The initial phase of this work occurred at a test track using known “cooperative” truck sources. This experience permitted the verification of the method and the definition of a final measurement approach that was viable at a highway site. Subjects were all trucks that varied in model, vehicle speed, tread, and the presence of a trailer. The array beamformer’s ability to localize and the measurement system’s ability to track were validated using both stationary and moving sources. Following validation at the test track site, the instrumentation was transferred to two California highway sites. There, acoustic calibration was used to align the array with the road track and to provide a spatial reference for mapping the “images”. Both light and heavy vehicles at these sites were “uncooperative” with arrivals and speeds randomly determined by traffic flow. This work was funded by the California Department of Transportation.