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.

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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.