This report investigates how accurately drones can be tracked by measuring the noise they emit
over time with a small microphone array; the ReSpeaker Mic Array v2.0. This acoustic localization
is achieved by applying beamforming to select frequency ranges. In literature this localization has
been achieved and accurately so, but the microphone arrays there are generally at least a meter in
diameter with many microphones. The array used in this project is about 5 cm in diameter and
contains only four microphones. It is therefore cheaper in cost and in terms of calculation time.
The first experiment takes place in an anechoic chamber to compare the beamform output with
reference values, specifying the expected elevation and azimuth angles. The output turns out
to be fairly accurate, with an error of a couple of degrees. A second experiment is with indoor
drone flights, where the reference location is only broadly known. The beamform output at high
frequencies appears to be very accurate and the output at low frequencies is less accurate. This
is expected due to the arrays small size which hinders beamforming at low frequencies but it is
unfortunate as drones emit most of their noise at lower frequencies.
The third experiment is with outdoor drone flights in the Unmanned Valley near Katwijk aan Zee. A
500 gram heavy Parrot Bebop 2 drone with three-bladed propellers can be localized up to a distance of 100m and a 907 gram heavy DJI Mavic 2 drone with two-bladed propellers can be localized up to a distance of 80m. Both with an error between GPS and beamform output of less than 10 degrees
if beamforming is applied to a higher frequency range of 2-5kHz. This only applies if the array
is placed at a 45-degree angle with the ground, which improves the beamforming accuracy. The
application of functional beamforming did not improve the results of this experiment.

River plumes form when freshwater from rivers enters the salty ocean, creating buoyant water masses that strongly influence coastal circulation. By transporting freshwater, heat, nutrients, sediments and pollutants, they impact the ocean dynamics and ecosystems on local (10-100 km) and even (beyond) regional scales (>1000 km), depending on the size and dynamics of the plume. This thesis aims to improve our understanding of the Rhine River plume and its interactions with sea-level variations. The Rhine plume, located along the Dutch coast in the Southern North Sea, is highly dynamic system, influenced by tides and winds.
Chapter 2 investigates the variability of the wind-driven response of the Rhine River plume using numerical model simulations of a spring-neap cycle forced by idealized wind conditions. The difference in wind-driven response between spring and neap tide shows how the competition between straining and mixing, both induced by tides and winds, determines the structure and evolution of the Rhine River plume.
Chapter 3 examines the plume’s effect on sea-level variability along the Dutch coast by comparing barotropic and baroclinic model simulations. The Rhine plume induces a positive steric height anomaly, elevating the mean sea level along the coast and modulating the tidal signal near the river mouth. This highlights the need to include river plumes in sea-level studies.
In Chapters 4 and 5, an innovative method is developed for estimating sound speed profiles from multibeam echosounder measurements. The inversion method is based on minimizing the discrepancies between overlapping swaths and exploits empirical orthogonal functions to describe sound speed profiles using a limited number of unknowns. Since sound speed is influenced by depth, temperature, and salinity, this proof-of-concept provides a way to offer valuable insights into the vertical structure of the water column using routinely collected data.
Overall, this thesis advances our understanding of the Rhine River plume and its contribution to sea-level variability. In addition, the development of a proof-of-concept for retrieving sound speed profiles from multibeam echosounder measurements offers a promising approach to provide valuable information on stratification in river plumes. Together, these contributions support improved modelling and understanding of coastal oceans, particularly river plumes, which will become more and more important, especially in the face of climate change and its impact on coastal regions.

Sustainable aviation is achieved when the negative impact of aviation on people, planet, and profit is minimised. Aviation impacts people through the noise it produces. Exposure to high noise levels for prolonged periods of time can lead to sleeping disorders, hypertension, and hearing issues. Therefore, the new generation of aircraft should be designed such that the noise emissions are reduced. Additionally, reducing noise annoyance is important as the human perception of aircraft noise is not only influenced by sound pressure levels.

Reducing the noise emissions and annoyance is only possible when the noise sources of an aircraft are known and can be predicted accurately during the design process. The objective of this dissertation is therefore to improve aircraft noise prediction models that can be used for reducing noise emissions and noise annoyance in the design process. This is specifically applied to currently operational sustainable aviation systems.

This report discusses urban air quality measurements carried out in a park in Aigaleo (Athens), located adjacent to a busy road. The measurements were taken using the Sniffer4D Mini 2 sensor box with a drone to investigate how Urban Air Mobility can aid in measuring urban air quality. CO, NO₂, O₃, SO2, PM_2.5 and PM₁₀ concentrations were recorded through point measurements at varying locations, altitudes and times. Several validation measurements were carried out, showing that temperature heavily influenced the outcome of the recordings. Due to the high number of variables, no conclusion has been found on the wind effect. During rush hour, NO₂ and CO concentrations were highest close to the traffic; however, NO₂ concentration showed non-linear behaviour with increase in altitude when further from the traffic or out of rush hour. SO₂ concentration always decreased with an increase in altitude; highest concentrations were observed further from the busy road. Highest O₃ concentrations were obtained in the afternoon at higher altitudes, due to its reaction under sunlight. PM_2.5 and PM₁₀ concentrations had a high variability due to environmental conditions. Additionally, continuous measurements were carried out, in which the drone flew a path through several locations and altitudes. The results from these were interpolated to enable the detection of areas with higher concentrations of air pollutants.

Addressing the increasingly urgent need for sustainable aviation solutions, this study explores operational innovations as a quicker and more scalable addition to novel zero-emission propulsion systems. Through the use of regression-based causal inference methods, this study aims to understand the relationship between flight fuelburn inefficiency and the factors causing these inefficiencies. Such an approach allows for the attribution of inefficiencies to factors on an overall scale, requiring less specific domain knowledge for initial results. A case study, involving a sample of 100,000 flights, representative of European operations, reveals that airspace structure (3.2% increase in inefficiency) and turbulence along the flight plan (2.5% increase) are the leading causes, while variations in average airspeed, congestion, and crosswind contribute the least to flight inefficiency. A compilation of the results shows that the performed analysis leaves 61% of the observed flight inefficiency unaccounted for. Future work would include the exploration of different metrics even closer to actual climate and air quality effects, as well as detailed uncertainty quantification. The developed flight inefficiency prediction model allows experimentation with counterfactual scenarios, contributing to the global transition towards more sustainable air transport networks.

Global passenger air traffic has doubled in the 13 years prior to 2019, and is expected to double again over the next 20 years or so. Growing demand for aviation is met by a corresponding increase in jet fuel being burned by aircraft, releasing multiple pollutants into the atmosphere. Besides disturbing the Earth’s radiative balance, these emissions also lead to excessive deposition of reactive nitrogen, and to a degradation of air quality. Anthropogenic nitrogen deposition damages vulnerable ecosystems, while degraded air quality is associated with increases in human mortality rates. These last two environmental impacts can be very localized, but, owing to the high altitude of emissions, they also occur over intercontinental distances. This thesis aims to evaluate the magnitude of air quality and nitrogen deposition due to emissions from civil fixed-wing aircraft at a global scale, and how these impacts might change in the coming decades.

Ammonia shows great potential for decarbonizing the road transportation sector because it does not produce CO₂ emissions during combustion. On the other hand, ammonia-fueled engines emit gases such as (unburned) ammonia (NH₃), and nitrogen oxides (NO)_x, which contribute to air pollution and adversely impact human health, among other environmental effects. The aim of this work is to investigate the air quality impacts and human health trade-offs associated with potential emissions from an ammonia-fueled road transportation fleet of different vehicle categories. Specifically, we create a simplified emissions tool to quantify the NH₃ and NO_x spatial emissions from state-of-the-art ammonia engine developments. Next, the emissions are combined with spatial sensitivities, obtained from the adjoint of GEOS-Chem, to estimate the corresponding air quality impacts in terms of PM_2.5 formation and human health impacts with respect to premature mortalities.
The research findings indicate that the ammonia-fueled sector generally exhibits higher air quality and human health impacts compared to conventional transport. We estimate that the implementation of various ammonia engine configurations into the road transportation fleet in the USA in 2011 could lead to approximately 23 000 to 250 000 premature mortalities.
However, there is potential for mitigating air quality impacts of ammonia emissions through the use of post-combustion treatment methods or the adoption of ammonia-fueled heavy-duty vehicles. These approaches could help achieve air pollution levels comparable to those of fossil fuel vehicles while reducing the carbon footprint, offering future possibilities for an ammonia-fueled road transportation sector.

Aircraft noise is a significant problem for communities surrounding airports. Accurate prediction models are needed to estimate noise levels from aircraft operations. In this research, the accuracy of the sonAIR aircraft noise model is evaluated in predicting noise levels around Schiphol airport by comparison to measurement data from NOMOS and the current best-practice modelling approach Doc29. Results show a significant but consistent underestimation of noise levels by sonAIR, mainly due to a generalisation of emission models. The standard deviation of differences between model results and measurements is lower for sonAIR than for Doc29 by up to 1 dB. Differences between measurement and model results were found in the relation between N1 and noise levels, maximum noise levels and frequency spectra. These results demonstrate that sonAIR provides more reliable predictions of noise levels on the single flight event level than Doc29. Additionally, this study shows agreement with results from a previous validation study in Zürich, thereby confirming the applicability of sonAIR to another airport. This research contributes to better aircraft noise predictions, which will have implications ultimately leading to a better quality of life for communities affected by aircraft noise.

Noise quantification methods for helicopters are limited, this especially holds for military helicopters. This research assesses the suitability of an existing civil helicopter noise model for the military helicopters of the RNLAF. In order to achieve this noise modeling platform HELENA, which uses hemispheres created for the Apache in a previous project, has been integrated as input data in this research. HELENA has previously been used for civil helicopters. Surrounding the Gilze Rijen Airport, the noise measuring network Casper is installed. From Caspers Noise Measuring Terminals (NMT), noise levels 𝐿𝐴,𝑚𝑎𝑥 and 𝑆𝐸𝐿 are extracted for every helicopter that passes by. These values are compared with the calculations from HELENA.
For this research a set of flights has been selected, carefully keeping the limitations of both HELENA as well as Casper in mind. The most ideal flights would be steady, straight, symmetric and repeatable flights. Transit flights departing to or returning from a northeast positioned low-level flying area, encompass most of these requirements. These flights pass three NMTs. For a full year, all Apache flights passing these three NMTs were collected. The formation flights and flights with a maximum 𝐿𝐴,𝑚𝑎𝑥𝑑𝐵(𝐴) relatively close to the background 𝐿𝐴,𝑚𝑎𝑥 ≤ 70𝑑𝐵(𝐴) were removed. In total 108 noise events were selected for the model-calculation comparison. In general, the noise events were underestimated by HELENA. The error, measurement minus calculation, showed a large difference with a mean of𝜇 = 4.9𝑑𝐵(𝐴) and variance of 𝜎 = 2.9𝑑𝐵(𝐴). Upon closer inspection, the performance varied amongst the NMTs. One NMT (58) had the least noise events after selection but produced the largest difference between measurement and calculation (𝜇 = 8.8𝑑𝐵(𝐴) and variance 𝜎 = 3.3𝑑𝐵(𝐴)). The NMT with the most (70) noise events produced the smallest error with a mean (𝜇 = 4.2𝑑𝐵(𝐴)) and variance (𝜎 = 2.4𝑑𝐵(𝐴)). The underestimation could be due to certain circumstances that influence the measurement but are not included in the calculation. No correlation could be found for the presence of wind, deviation in speed relative to the speed of the corresponding hemisphere, distance or addition of ground effect.

Aircraft noise and its impact is one of the biggest challenges the aviation industry faces today. At airports, the number of operations and the flight routes are driven by the noise impact on the surrounding communities. To predict the expected aircraft noise levels around airports, noise models are used. The accuracy of these models is thus of great importance. This research aims to improve aircraft noise modelling by validation and calibration of the main input parameters using acoustic measurements. This research is focused on the Doc.29 aircraft noise model. This is an empirical model, based on Noise-Power-Distance (NPD) tables, which is convenient for fast computations of a large number of flight operations. The data set used consists of take-off measurements of the B737-800 gathered around Schiphol airport. To validate and calibrate the model with measurements, all input parameters should be correct to be able to identify where deviations come from. The power parameter thrust is an important input, but often not exactly known. The N1 rotational speed of the engines can be used to estimate the used thrust. An estimation of the N1% is performed by finding the fan tones from acoustic measurements. The found N1% is validated with Aircraft Condition and Monitoring System data (ACMS) from the measured flights. The estimation resulted in an average 4% root mean square error with the ACMS data. When using the estimated N1% as input for thrust, differences between model results and measurement are still found. These differences are assumed to be caused by errors in the NPD table. This allows altering the entries in the NPD table by adding the found differences from the measurements reduces the mean error to less than ±0.2 dBA and providing a 20-30% reduction in standard deviation. Another method of model improvement is done by looking at the measurements which have been standardised to reference conditions. The relation between thrust setting and sound level showed to be less dominant than originally expected from the NPD table. From these newfound thrust-noise relations, new NPD tables are created. This caused a 30-40% reduction in standard deviation, reducing the variation found between noise measurements and model results significantly. Next to an improvement in best-practice noise modelling, the methods described in this research give insight into the creation and validation of NPD values.

Aerodynamic noise produced by aircraft, wind turbines, and other objects subjected to airflow contribute to environmental noise pollution, which adversely affects human and animal health. Consequently, governments impose restrictions on aircraft and wind turbine noise levels. These restrictions can have an economic impact by limiting aircraft traffic and reducing wind turbine energy production. Accordingly, improving the design of aerodynamic surfaces to reduce their noise levels benefits health while enabling improved operational efficiency. Therefore, aeroacoustic research focuses on identifying and understanding the physical mechanisms behind aerodynamic noise to improve noise mitigation technologies. This research relies on acoustic wind tunnel measurements to validate simulations, theories, and design improvements.

Closed test section wind tunnels are widely used for aerodynamic testing but are less suitable for acoustic measurements because microphones must be installed in the wall. This location subjects the microphones to pressure fluctuations from the turbulent boundary layer (TBL), which contaminates acoustic measurements and reduces the signal-to-noise ratio (SNR). The impact of the TBL can be mitigated by recessing microphones within cavities and covering them with an acoustically transparent material. Modifying existing wind tunnel walls by installing cavity--mounted microphones is a straightforward and cost-effective improvement that enables combined aerodynamic and acoustic measurement campaigns.

The cavity geometry, i.e., depth, aperture size, wall angle, and presence of a covering determines the amount of TBL attenuation and consequently the improvement to SNR. While several studies have shown empirically that these parameters have an effect, few studies focus on identifying the physical mechanisms that explain the relationship between geometry and the reduction in TBL pressure fluctuations at the microphone. Thus, this thesis aims to identify these physical mechanisms through experiments and different modeling approaches to better explain the relationship between cavity geometry, the amount of TBL attenuation, and the subsequent impact on the measured acoustic signal.

Experimental data were collected to develop an empirical model to quantify how varying cavity geometry affects the measured pressure spectra. Moreover, experiments were also performed to validate simulation results and to quantify the SNR improvement when applying a beamforming algorithm to microphone array data. The modeling and simulation efforts focus on explaining the trends and phenomena identified in the experimental data. Initially, a physical model was developed that assumed acoustic propagation into an axisymmetric cavity with a constant cross-section. This model decomposes a pressure field, resulting from a TBL, into circular duct modes and was used to evaluate the relationship between cavity geometry and the propagation of these acoustic modes into the cavity. This model was followed up with a finite element method (FEM) simulation to study the influence of different cavity geometric parameters and wall materials on the acoustic response of the cavity when subjected to an acoustic wave.

The FEM simulation showed that the cavity's acoustic response is determined by the presence of standing waves in the form of acoustic depth modes. This simulation showed that cavities with angled walls have depth modes with lower amplitude waves and thus distort the acoustic signal less. Furthermore, it is shown that the acoustic responses of cavities formed out of sound-absorbing foam are driven by the shape of the foam holder and not the cavity shapes within the foam. Thus, the holder can be optimized to minimize the acoustic response, while the cavity itself can be optimized to reduce the influence of the TBL. Building upon these simulations, a Lattice Boltzmann based computational fluid dynamics (CFD) method was used to simulate the pressure and flow fields within three uncovered cavities and covered cavities resulting from the presence of a turbulent boundary layer.

The CFD simulations confirmed a significant finding of the physical model, that the amount of TBL attenuation increases as the cavity aperture size increases relative to the TBL streamwise coherence length. This is due to the resulting modal decomposition of the pressure field above larger cavities having more energy distributed across higher-order modes than for smaller cavities. These higher-order modes decay exponentially into the cavity, resulting in increased attenuation of the TBL. Smaller cavities have most of their energy in their first mode, which does not decay with increasing cavity depth. Furthermore, these simulations showed that the pressure field within covered cavities is primarily acoustic and can be decomposed into acoustic circular duct modes. Since the propagation of TBL pressure fluctuations into covered cavities is primarily acoustic, the shape of future cavities can be efficiently optimized using FEM simulations.

Finally, beamforming used with cavities improved the acoustic measurement SNR. Analysis shows that the improvements due to beamforming are independent of those attributed to the cavity geometry. Thus, combining the two approaches improves the SNR of acoustic measurements in closed test section wind tunnels.

In recent years, there has been a rise of electric propeller aircraft in the field of general aviation. These propeller aircraft greatly reduce the emissions and engine noise compared to conventional aircraft, but noise coming from the propeller stays unchanged. Acoustic imaging, using beamforming, could help in the characterization of associated noise sources. In this research, the noise of a Cessna Skymaster 337F is recorded using a microphone array, and two beamforming algorithms are applied in the data analysis: conventional beamforming (CTDBF) and rotating beamforming (ROSI). The combination of these two algorithms adds a new dimension to acoustic analysis of propeller noise, namely a choice of reference frame, which allows the researcher to separate rotating noise sources from stationary noise sources. It has been shown that this is a very valuable tool for localizing the noise, making it easier to reduce the propeller noise in future research.

For a hub airport, capturing transfer passengers and offering better connections are key elements that largely influence its future growth and profitability. This research aims to develop a strategic stand planning model capable of selecting profitable turnarounds that will expand the network of a hub, integrating those turnarounds into the pre-existing schedule and efficiently capturing transfer passengers. The model includes 2 main goals, namely i) the maximization of the number of captured transfer passengers and the ii) minimization of connection times, which are integrated with 2 other objectives, iii) the minimization of unassigned turnarounds and iv) the minimization of tow moves. The 4 objectives are solved hierarchically in the order: iii)-i)-ii)-iv). In an attempt to tackle the inherent complexity of models that include transfer passengers, the problem is split into two less complex levels. One solves the multi-objective function and the other delivers a specific stand assignment. This approach significantly reduced the computational load for large input sets. The results show that an efficient stand planning can maximize the transfer passenger throughput by up to 38.66\% and reduce the average connection by 17.86\%. It was also possible to conclude that objectives i) and ii) conflict with each other. Further results confirmed that the time slot to which each new turnaround is allocated is the best slot for the airport, but it is not necessarily the most profitable for the airlines. This research could lead to the development of tools used by rapidly expanding airports to assess the connection potential of new routes. It can also be utilized by busy airports to optimize the current schedule and to make important time slot decisions.

As poor air quality poses a risk to human health, this study aims to estimate the impact of Schiphol airport on local concentrations of various air pollutants from January 2014 to August 2020. The pollutants of interest are nitrogen monoxide, nitrogen dioxide, ozone and particulate matter. Three separate methods were applied: a comparison of air quality measurements from different locations, an analysis about the effect of wind on ambient concentrations and finally meteorology and road and air traffic activity-based regression modelling. Based on these methods it was estimated that the airport is responsible for between 24 to 31% of local concentrations of nitrogen dioxide. During the COVID-19 situation these effects decreased by ~ 4 - 5 µg/m3, which is 17 - 21% of the average concentrations measured in the years before. For the other pollutants the results were less robust and quantitative estimates include large uncertainties.

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

In order to reduce aircraft emissions during on-ground operations, electric taxiing systems (ETS) have been intensively researched to take over or assist in part of the taxiing phase of a flight. One of these ETS is the TaxiBot, deployed by Smart Airport Systems (SAS). While a number of papers have been researching this vehicle routing problem (VRP) in order to minimise costs, fuel consumption or another metric, most research uses deterministic input data. However, sudden changes are inevitable, disrupting the resulting schedule. In this paper, we propose both a strategic model and a tactical model for airport surface movement, taking into account stochastic delays to the flight schedule. The subsequent tactical schedule can be produced with only little deviations from the strategic schedule. The tactical model can be generated in the order of seconds, making it useful for real-life traffic management decision support systems. We found that the tactical schedule does not worsen remarkably. Still 47.9% of the flights can be towed by a TaxiBot, which was 48.5% in the strategic schedule. Different case studies have been performed in order to determine the effect of e.g. the size of the flight schedule and the number of TaxiBots used, on the aircraft taxiing coverage and TaxiBot efficiency. With this, both busy and calm days, now and in the near future are assessed and the optimum number of TaxiBots necessary can be determined. An upper limit is reached at an asymptote starting from 34 TaxiBots, while a lower limit is dependent on a trade-off between flight coverage and spare TaxiBot capacity.

Permeable trailing edges are experimentally investigated in this thesis as a mean tomitigate aeroacoustic noise. The first part of the study is devoted to deciphering themechanisms of noise generation involved in these devices. To this aim, trailing-edge inserts are manufactured employing commercially-produced open-cell metallic foams with different material properties, i.e., permeability and pore size.