Aeroacoustic testing in wind tunnels is crucial for understanding and mitigating the noise generation mechanisms in several devices while maintaining satisfactory aerodynamic performance in the conceptual design stage. However, current measurements in closed-section wind tunnels face challenges in terms of installation, due to the effect of the boundary layer of the wind tunnel walls, and accuracy. To address these issues, the proposed methodology integrates advanced signal processing techniques and cost-effective and limited alterations in a closed-section wind tunnel. Different configurations, such as a perforated panel, a perforated panel with melamine foam rings, and the addition of melamine foam panels behind the array and inside the wind tunnel combined with the use of a microphone array consisting of 88 microphones, recessed behind an acoustically transparent stainless steel mesh, has led to significant improvements in signal-to-noise ratio and measurement accuracy compared to the baseline aeroacoustic testing. In general, this setup enables the identification of noise sources with a signal-to-noise ratio of at least -10 dB. Additionally, the utilisation of advanced beamforming techniques (CLEAN-SC and DAMAS) in post-processing yields clearer outcomes. Finally, the effectiveness of the set-up was evaluated, resulting in an approximate 15 dB improvement in peak prominence of the flow-induced noise source due to the higher number of microphones and beamforming.

Military fighter pilots spend most of their working hours performing training missions, simultaneously carried out by multiple pilots with different qualifications. Scheduling these missions is typically done manually, which is a time-consuming task resulting in far from optimal schedules. This study proposes a model that schedules three types of training missions and assigns pilots to these missions for a full-year training program. The proposed methodology consists of a two-stage Mixed Integer Program. Both stages are solved with a Brand and Bound algorithm. Additionally, the second stage implements a Rolling Horizon approach. A case study considering two Royal Netherlands Air Force fighter squadrons is used to test the model. The results show an increase of more than 15% for the average amount of training completed by all pilots, compared to the current scheduling process. The solutions are obtained in a few minutes, demonstrating the suitability to use the model in practice and reduce the time spent on constructing schedules.

The need to contribute to a sustainable future of the aviation industry has spiked an interest in turboprop engines. This is due to the higher propulsive efficiency, the better take-off and landing performance on short runways, and the new developments in propeller-driven hybrid and electric aircraft. The most significant drawback is the noise they generate, which is undesirable. To reduce the propeller noise, new advanced propeller designs are needed. Propeller noise models can be used as a valuable tool during the design phase. The models can only be confidently used when they have been properly validated. The more data is used to validate, the higher the confidence will be.

A master student that graduated from the Aircraft Noise and Climate Effects section at the TU Delft created a propeller noise model called HeliX-tool. This model is validated by another model and wind tunnel measurements from NASA but not yet by full-scale outdoor experimental data. To increase the confidence of the HeliX-tool, this model is compared to full-scale outdoor experimental data and the quality is assessed in this thesis.

The HeliX-tool models the steady loading and thickness noise and is based upon the helicoidal surface theory. This theory needs the aerodynamic and performance characteristics of the propeller blade which are obtained by implementing the two external analysis tools, XFOIL and XRotor.

The experiment is performed with the Pipistrel Velix Electro at Teuge airport to obtain the full-scale outdoor data to validate the model. The Pipistrel Velis Electro is a fully electric-powered two-seater owned by E-flight academy. The microphone array that is used to obtain the full-scale outdoor data has 64 microphones placed in an Underbrink configuration.
The propeller geometry is an important input for the HeliX-tool and is obtained by 3D scanning the propeller blade of the Pipistrel Velis Electro with the FaroBlu HD laser.

The validation metrics are the SPL and OSPL values. The frequency spectra are generated for both the full-scale outdoor data and the HeliX-tool when the aircraft is overhead the microphone array and are compared to each other. In general, the HeliX-tool gives higher SPL values compared to the full-scale outdoor data, which also results in higher OSPL values from the HeliX-tool.
The location of the most dominant noise source can be found, with beamforming, although the resolution is limited by the Rayleigh limit. Functional beamforming is used to reduce the side lobes and it can be seen that the dominant noise source shifts in location. It can be concluded that there might be a noise source present within the Rayleigh limit but this cannot be confirmed.

The values of the validation metrics are within 4 dB and from this can be concluded that the confidence in the HeliX-tool is increased. It can be used as a tool in the design phase of new advanced propellers but is limited to Mach numbers below 0.7 due to the Prandtl­Glauert compressibility correction used in XRotor.

The past years there has been a significant increase in the popularity and commercial availability of consumer drones.

With increasing UAV operations, tailored measures need to be implemented to integrate the massive employment of these vehicles in day-to-day operations. One of the current issues of UAVs is the noise emitted by these vehicles. The purpose of this research is to offer a doorway towards understanding the behavior of drone noise with changing operational and design parameters.
The radiated noise from four selected drones was experimentally measured under realistic environmental settings in an open field. The study dives through spectral analysis of a series of maneuvers, assesses the suitability of acoustic imaging algorithms for drone noise sources, explores adequate noise metrics, and lastly, identifies influential drone parameters essential for the modeling of drone noise in the future. Spectrogram and power spectral density analyses unravel the frequency range of interest between 200 Hz to 3000 Hz with dominant harmonics of the rotors’ blade passage frequencies. Above this range, the ground reflection hinders the proper identification of noise sources. Further investigation is done with 4 beamforming algorithms. As drone noise may exhibit complex radiation patterns due to interference of multiple rotors, sophisticated beamforming techniques (i.e. Functional (FB), Dipole (DB), and Quadrupole Beamforming (QB)) were implemented which are potentially superior to Conventional Beamforming (CB) in the setting of drone noise. The results of the beamforming algorithms have been verified with synthetic data. Notably, FB was found to perform best on a source if a correction for the expected source type is applied first, however, it is outperformed by other methods if the source type is not known a priori. While all implemented algorithms yielded excellent results on synthetic data, their accuracy greatly decreases when applied to real-life data. This can be attributed to the multitude of external factors and imperfection in vehicle design, as well as the dependency of DB and QB on prior information regarding the pole orientation on the rotors. In conclusion, only CB could be applied reliably. Analysis of CB over snapshots of drones during different maneuvers shows that the rotors prove to be the main sources of noise in hovering and increasing height from Hover. The source identification, however, becomes unreliable with the change in height. To yield a complementary view on the sound characteristics of drones in specific maneuvers, the sound pressure levels and various sound quality metrics have been computed. The metrics reveal the effects of drone parameters (i.e. rotor size, distance to the observer, thrust settings, etc.), thus leading to a selection of possible candidates for a future noise model. This may be useful to assess the human psychoacoustic annoyance of drone noise in an environment where drones become a part of our everyday life.

With the increasing human activities in the marine environment, such as fisheries, dredging, coastal protection or construction of marine infrastructure, seabed sediment and habitat mapping have become highly relevant for the development of sustainable marine management strategies. Compared to traditional mapping methods, primarily based on bed sampling, multibeam echosounding belongs to the cutting-edge technology to time-efficiently acquire high-resolution bathymetric and backscatter (BS) data over large areas. Using classification methods to combine the acoustic data with ground-truthing, large-scale maps can be automatically and objectively produced, that enables to describe the distribution of benthic habitats or quantify marine resources. However, acoustic sediment classification still does not allow to discriminate between the entire heterogeneity of the seabed and is generally applied to a single multibeam echosounder dataset by means of revealing the seabed state only at a given time instant. Two challenging issues addressed within the scope of this thesis are summarized as: (1) Investigation on the applicability of repetitive multibeam (single-frequency) BS measurements for monitoring the seabed; and (2) Evaluation of the potential of multispectral BS to increase the acoustic discrimination between different seabed environments.@en

The increase of energy consumption as well as the need to reduce green-house emissions trigger the shift from a fossil-fuel based society towards a sustainable society. With a constant increase in the installed power capacity, wind plays a key role in the global energy framework. The improvement of the technology opens up new targets for the wind development [28]. In particular, the installation of small wind turbines in the urban environment represents a promising solution to supply residential power. Whilst, some of the problems faced by large-scale wind farms, like losses in the electrical distribution and transportation system, can be reduced. However, the immaturity of the technology leads to elevated capital costs and technical challenges [41]. Among these disadvantages, the siting near human activities is limited by the sound emitted by small wind turbines [65].
The current work focuses on the aerodynamic noise produced by horizontal-axis small wind turbines. For this purpose, the rotational beamforming algorithm, ROSI (i.e. ROtating Source Identifier), together with microphone array measurements, have been used. The ability of the method to follow the movement of the sound source is essential to correctly localize and quantify the rotating sound source. ROSI is applied in the time domain: first, for every source position a time history reconstruction of the signal recorded at each microphone is performed. Then, the microphone signals are sampled for every emission time, leading to the de-Dopplerisation of the signals. Last step is to sum all de-Dopplerised signals and to repeat this procedure for each scan point [33].
In order to validate the technique, the ROSI performance has been investigated with an increasing degree of uncertainty: initially, under ideal and know conditions with simulated datasets and, then, applying ROSI to experimental data. The results of the simulations show that ROSI is not influenced neither by the rotational speed of the source or by the selected time snapshot. Furthermore, the differences in the beamformed results always agree with the differences in the source level, proving that ROSI can be used to compare different blade or turbine geometries. Additionally, the ROSI performance is in agreement with the Rayleigh limit. In fact, if the spacing between the sound source is below the Rayleigh limit, the sources are not distinguished as separated and their SPLs are not correctly estimated. As regards the experimental approach, a small-scale prototype simulating the acoustic of a small wind turbine was tested in the Anechoic Tunnel facility while an upwind turbine was measured in the Open Jet Facility, varying both the wind speeds of the tunnel and the rotational speed of the blades. The two facilities are located at the TU Delft University. During the first experiment, the measurement geometry is verified and the resulting outcomes are compared with the simulations. It is confirmed both that the rotational speed does not limit the ROSI performance and that the source localization and quantification are affected by the Rayleigh limit. As regards the last experiment, the ROSI uncertainty is investigated in a non-anechoic environment. In the range of frequencies where the wind turbine noise is expected (i.e. 500 Hz - 2000 Hz [60]), the high levels of background noise do not allow to localize and to quantify the main noise sources in the small wind turbine. Thus, the wind turbine noise is not assessed and the experimental data are not compared with the analytical noise models, provided in literature.

One of the main limitations of microphone measurements in an open-jet wind tunnel is spectral broadening. The phenomenon of spectral broadening is observed when a single frequency sound wave passes through a free shear layer. Inside the turbulent shear layer the wave interacts with the turbulence, which leads to broadening of the tone in the frequency domain. The spectrum of a single frequency tone measured with a microphone will not only include the peak but also two shoulders containing the broadened frequencies, one on each side of the peak. Spectral broadening does not only a_ect single microphone measurements but also has a detrimental e_ect on acoustic array measurements. The distortion caused by the shear layer will reduce the coherence between the microphones in the array. This limits the size and thus resolution of the array. This spectral broadening is particularly problematic when examining objects which produce a tonal noise, e.g. open rotors, sharp edges.