Assessment of the accuracy of microphone array methods for aeroacoustic measurements

Abstract

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.

Implementation of tonal cavity noise estimations in landing gear noise prediction models

“…This technique raises the acoustic source map obtained with conventional beamforming to the power of an exponent parameter ν and the CSM to the inverse of this power 1/ν. The value of ν for this study was selected to be 8 after performing a sensitivity analysis [66].…”

“…In order to do so, experimental measurements of commercial aircraft flying under operational conditions were performed. A microphone array was employed, which, combined with acoustic imaging algorithms [61][62][63][64][65][66], allows for the visualization of sound and the separation of the noise emissions generated by different elements on board [61,67]. This case study focuses on the Airbus A320 aircraft, which is a relevant example, given its popularity (at the moment of writing this manuscript, it is the world's best-selling airplane) and the aforementioned presence of a strong tonal component in the noise signature of its NLG system [21,45].…”

Multi-Approach Study of Nose Landing Gear Noise

“…Adapted from [33]. In addition, the high-resolution deconvolution method HR-CLEAN-SC [47,[65][66][67][68] was also applied to the data from both arrays in order to obtain a better dynamic range (fewer and lower sidelobes) and to investigate whether one or more sound sources were present (even beyond the Rayleigh resolution limit [65][66][67]). For the frequency range considered in the wind-tunnel measurements (200 Hz to 4 kHz), the differences between the obtained spectra by the SPI technique and HR-CLEAN-SC were small.…”

Real-time identification of aircraft sound events

“…In other words, this methodology applies frequency domain beamforming to the spatial cross-correlated microphones. Examples of this methodology include identifying taxiing aircraft ( Asensio et al, 2007 ), thrust reversing ( Asensio et al, 2015 ), and aircraft takeoffs, landings, or fly–over ( Genescà et al, 2009 , Genescà et al, 2010 , Merino-Martínez et al, 2020 , Merino-Martinez et al, 2016b , Sánchez-Pérez et al, 2014 , Snellen et al, 2017 ). This technique has the advantage of isolating aircraft noise from ground reflections, or ground-borne noise sources ( Genescà, 2016 ), which represent most of the unwanted background noise source for aircraft noise measurements, and therefore microphone arrays are useful tools for that purpose.…”