The normalized fan rotational speed per aircraft engine (N1%) is an essential input parameter to noise prediction models, but is often confidential and not directly accessible to researchers. The aircraft acoustic signal characteristics, and specifically the tonal component, can be used to extract this parameter. However, existing methodologies estimate N1% parameters from whole-aircraft spectra, which can lead to inaccurate estimations. This research aims at investigating the various tonal contributions by isolating and reconstructing spectrograms of individual noise sources using acoustic arrays. Using such arrays, it is possible to discriminate between the various components that contribute to the noise emitted by the aircraft, especially between the engines, but also the nose landing gear. From the resulting engine-specific spectrograms the N1% of individual engines for 24 aircraft were obtained. For the A321neo and the B737NG, it is found that, for 80% of the analyzed aircraft, additional engine tones accompany the higher harmonics of the engine blade passage frequency, with these additional tones corresponding to twice the shaft frequency. In addition, it was found that N1% differences between the two engines are reflected in the spectrograms and that a tone stemming from the nose landing gear can be present, resulting in a complex pattern of tones in the whole-aircraft spectrogram. The insights on the various tonal contributions to the received signal are of importance regarding the further development of methods that aim to extract the engine setting from aircraft noise measurements and as such for enabling more accurate noise calculations.

Sound propagation in closed test section wind tunnels suffers from reflections and diffraction, which compromise acoustic measurements. In this article, it is proved possible to improve the post-processing of phased-array microphone measurements by using an approach based on the combination of numerical acoustic simulations and beamforming. A Finite Element Method solver for the Helmholtz equation is used to model the acoustic response of the experimental facility. The simulations are compared with acoustic experiments performed at TU Delft's Low Turbulence Tunnel, using both fully reflective (baseline) and lined test sections. The solver accurately predicts the acoustic propagation from a monopole sound source at the centre of the test section to the microphones in the phased-array, for frequencies in the range 500Hz<f<2000Hz. It is shown that a (lower fidelity) geometric modelling method is unable to precisely predict the acoustic response of the Low Turbulence Tunnel at these frequencies, due to strong acoustic diffraction. The numerical results are used to implement corrections to the post-processing of experimental data. A corrected version of the Source Power Integration method is able to increase the accuracy of the source's noise levels calculation, based on a single numerical simulation with the source at the same location as in the experiment. A Green's function correction increases the beamforming resolution and the source's noise levels estimation accuracy from the beamforming maps, without a priori knowledge of the source's location. Both corrections perform well at processing flow-on acoustic measurements, and the Green's function correction shows an additional benefit. The improvement in beamforming spatial resolution leads to an increase of the signal to noise ratio.

The deconvolution method CLEAN-SC is proposed as a tool for analysing engine static tests with far-field microphone arrays. Using measurements on a DGEN380 engine, it was demonstrated that CLEAN-SC is able to make a breakdown of the different engine noise sources (jet, core, bypass, intake) and to determine the far-field directivity of each source. Even the challenging task of separating core noise from jet noise was performed successfully, including the directivity.

Aeroacoustic measurements performed by flush-mounted microphone arrays on the walls of closed-section wind tunnels are contaminated by the hydrodynamic pressure fluctuations of the wall's boundary layer. This study evaluates three different microphone cavity geometries for mitigating this issue. Their improvement to the signal-to-noise ratio (SNR) and the accuracy of their acoustic imaging results are compared to a flush-mounted microphone array. The four geometries include: (1) an array of flush-mounted microphones as the baseline, (2) a cylindrical hard-plastic cavity with a countersink, (3) a conical cavity made of melamine acoustic absorbing foam, and (4) a conical cavity with star-shaped protrusions, also made of melamine. The three arrays with cavities were covered with a steel-wire cloth to reduce the boundary layer fluctuations at the microphone while the baseline array was uncovered. Two sound sources were tested in an aeroacoustic wind tunnel for assessing the performance of the different cavities: a speaker placed outside the flow and a distributed sound source generated by a flat plate inside of the flow. When using conventional frequency domain beamforming, both cavities made of melamine offer up to a 30 dB increase in SNR with respect to the flush-mounted case, followed by the hard-walled cavity with up to a 20 dB increase. This is a 20 dB improvement when compared to the single microphone cases. The melamine cavities also provide cleaner acoustic source maps and accurate spectral estimations for a wider frequency range. The effect of cavity placement and geometry on the coherence, which affects the beamforming analysis of the acoustic signal was negligible for all cases. Distributed sound source measurements using the three arrays agreed with predictions using the Brooks, Pope, and Marcolini (BPM) model, showing that the cavities could detect vortex shedding that was undetectable by the flush array.

In this paper, the performance of four acoustic imaging methods: conventional frequency domain beamforming (CFDBF), functional beamforming (FUNBF), enhanced high resolution CLEAN–SC (EHR–CLEAN–SC) and generalized inverse beamforming (GIBF), is investigated in terms of accuracy and variability. Three experimental test cases are considered: 1) a single speaker emitting synthetic broadband noise, 2) two speakers emitting incoherent synthetic broadband noise, and 3) trailing–edge noise generated by a tripped NACA 0018 airfoil. All the measurements were performed in the anechoic wind tunnel of Delft University of Technology. Overall, GIBF and EHR–CLEAN–SC offer the most accurate results when point sources (speakers) are present. They even achieve super–resolution by separating sound sources beyond the Rayleigh resolution limit. Repeating the measurements indicates a standard deviation in the results of less than 1 dB. When analyzing distributed sound sources, such as trailing–edge noise, CFDBF and FUNBF provide the best performance. This indicates that the acoustic imaging method needs to be selected based on the expected sound source configuration.

The recently introduced high-resolution (HR)-CLEAN-SC algorithm for acoustic imaging provides ‘super-resolution’, i.e. the ability to discern sound sources located closer than the Rayleigh resolution limit. This is achieved by allowing the source markers to be relocated from the actual source locations within a certain constraint to avoid the combined influence of the other sound sources. The freedom to relocate the source markers to increase the performance of the algorithm depends on the maximum sidelobe level of the acoustic array used. This paper presents an ‘enhanced’ version of the HR-CLEAN-SC algorithm which benefits from low maximum sidelobe level array design. The source marker constraint μ is adapted to the maximum sidelobe level at each frequency. Application to up to four synthetic sound sources shows that the sources can be resolved at half the frequency associated with the Rayleigh resolution limit, when an acoustic array optimized for low maximum sidelobe level is used in combination with Enhanced HR-CLEAN-SC. This improves source discrimination compared to when the HR-CLEAN-SC algorithm is used with a benchmark acoustic array design. The results are confirmed by experimental validation in which up to four loudspeakers and the same array configurations as in the synthesized data case are used.

Phased microphone arrays have become a well-established tool for performing aeroacoustic measurements in wind tunnels (both open-jet and closed-section), flying aircraft, and engine test beds. This paper provides a review of the most well-known and state-of-the-art acoustic imaging methods and recommendations on when to use them. Several exemplary results showing the performance of most methods in aeroacoustic applications are included. This manuscript provides a general introduction to aeroacoustic measurements for non-experienced microphone-array users as well as a broad overview for general aeroacoustic experts.

Most acoustic imaging methods assume the presence of point sound sources and, hence, may fail to correctly estimate the sound emissions of distributed sound sources, such as trailing-edge noise. In this contribution, three integration techniques are suggested to overcome this issue based on models considering a single point source, a line source, and several line sources, respectively. Two simulated benchmark cases featuring distributed sound sources are employed to compare the performance of these integration techniques with respect to other well-known acoustic imaging methods. The considered integration methods provide the best performance in retrieving the source levels and require short computation times. In addition, the negative effects of the presence of unwanted noise sources, such as corner sources in wind-tunnel measurements, can be eliminated. A sensitivity analysis shows that the integration technique based on a line source is robust with respect to the choice of the integration area (shape, position, and mesh fineness). This technique is applied to a trailing-edge-noise experiment in an open-jet wind tunnel featuring a NACA 0018 airfoil. The location and far-field noise emissions of the trailing-edge line source were calculated.

This paper proposes the enhancement of a previously developed method for beamforming in engine ducts, which enables phased array beamforming in the interstage section of a turbofan engine. The enhanced method features the use of tailored Green’s functions, to deal with the annular geometry and the non-uniform (swirling) flow in this part of the engine duct. A complicating factor is the occurrence of resonant acoustic modes, when there is no acoustic lining. The paper describes the computation of the tailored Green’s functions and how resonant modes can be subtracted. The use of the method is firstly demonstrated by application to simulated sources, and is subsequently applied to test data. At the moment there is no other information on the sources that could be used to validate the method. The only indication that the method is working well is that it should find the correct number of rotating fan blades in the beamforming plots. This is indeed the case, but only in a small fraction of the results.

Unequally spaced transducer rings make it possible to extend the range of detectable azimuthal modes. The disadvantage is that the response of the mode detection algorithm to a single mode is distributed over all detectable modes, similarly to the Point Spread Function of Conventional Beamforming with microphone arrays. With multiple modes the response patterns interfere, leading to a relatively high “noise floor” of spurious modes in the detected mode spectrum, in other words, to a low dynamic range. In this paper a deconvolution strategy is proposed for increasing this dynamic range. It starts with separating the measured sound into shaft tones and broadband noise. For broadband noise modes, a standard Non-Negative Least Squares solver appeared to be a perfect deconvolution tool. For shaft tones a Matching Pursuit approach is proposed, taking advantage of the sparsity of dominant modes. The deconvolution methods were applied to mode detection measurements in a fan rig. An increase in dynamic range of typically 10–15 dB was found.

In this article, a high-resolution extension of CLEAN-SC is proposed: high-resolution-CLEAN-SC. Where CLEAN-SC uses peak sources in ‘dirty maps’ to define so-called source components, high-resolution-CLEAN-SC takes advantage of the fact that source components can likewise be derived from points at some distance from the peak, as long as these ‘source markers’ are on the main lobe of the point spread function. This is very useful when sources are closely spaced together, such that their point spread functions interfere. Then, alternative markers can be sought in which the relative influence by point spread functions of other source locations is minimised. For those markers, the source components agree better with the actual sources, which allows for better estimation of their locations and strengths. This article outlines the theory needed to understand this approach and discusses applications to 2D and 3D microphone array simulations with closely spaced sources. An experimental validation was performed with two closely spaced loudspeakers in an anechoic chamber.