Assessment of the use of global optimisation techniques for aircraft noise source identification

Over the past years, noise nuisance around airports strongly increased resulting in the need for aircraft noise reduction. Therefore, the noise sources should be known. This thesis assesses a new acoustic imaging method to identify sources on aircraft fly-over measurements. The proposed Global Optimisation (GO) algorithm is based on the theory behind Conventional Beamforming, but can identify sources in 3-dimensions and potentially overcomes the spatial resolution limit. This imaging method was applied to simulations, an anechoic room set-up, and two fly-over measurements (landing and take-off) performed with a 64-microphone array. The simulations and anechoic room test showed that GO can overcome the spatial resolution limit, but has difficulty finding the correct distance between the array and the source. The fly-over cases showed that the aircraft height was not found by GO. For the landing case, the engines and landing gear were found as sources. For the take-off case, GO did not find sources at low frequencies. The method is promising, but to increase the performance on fly-over measurements, it is advised to use the knowledge of the aircraft geometry when applying the GO algorithm.Aerospace Engineerin