Turbofan engines are still one of the loudest noise sources of modern commercial aircraft. Noise radiation from the engine is highly directional and it is, therefore, important to obtain this directivity pattern from microphone-array data from static engine noise tests. Recently, Sijtsma has shown that the deconvolution method CLEAN-SC is able to break down engine noise sources and that it is capable of determining the directivity of these sources in the far field using measurements of a DGEN380 static engine test. This study further investigates this capability of CLEAN-SC by performing acoustic experiments under controlled conditions in the anechoic chamber at the faculty of applied sciences at Delft University of Technology. The experiments consist of a variety of tests with 2 different speakers placed inside an aluminum cylindrical pipe simulating an engine. In these experiments, the location and directivity of the individual noise sources can be measured separately, so that the CLEAN-SC results can be compared to the ground-truth reference. Moreover, the results are also compared with other acoustic imaging methods, such as DAMAS and conventional beamforming.

These are exciting times to work in the field of acoustics: researchers have a more granular understanding of sound than ever before due to the ability to acquire, store and analyze unprecedented amounts of acoustical data. The purpose of acoustic imaging is noise reduction. If noise can be measured, it can be reduced. Acoustic imaging is generally performed with a technique called conventional beamforming, which makes use of an array of microphones and of the phase of sound at the different microphones. Two new acoustic imaging techniques are Global Optimization and Source Power Integration, improving on the shortcomings of conventional beamforming. This thesis investigated the performance of Global Optimization and Source Power Integration in comparison to conventional beamforming. Two of the main findings are that the Global Optimization technique is good at precisely identifying closely spaced sound sources and that the Source Power Integration technique can accurately determine the sound power levels of distributed sources.

This work aims to determine the optimal microphone placement on an acoustic array of TU Delft’s ‘V-tunnel’ which is used for beamforming in aero-acoustic studies. The beamforming performance is driven by two parameters; the Maximum Side lobe Level (MSL) and the Main Lobe Width (MLW). The array design should give a good trade-off between these parameters. The proposed optimization method has two optimization loops. First, the main loop consists of design variables used to collectively describe the distribution of microphones. Then the nested loop generates arrays which satisfy the geometry descriptions from the main loop. Finally, the main loop searches for the optimal design variables. The optimized array is able to achieve the MSL below -15 dB up to the distance approximately four times the MLW around the main lobe. Experimental validation was also carried out to compare the optimized array’s performance with a benchmark array and an array from the beginning of the optimization.