This study aims to develop a regression model of the aerodynamic coefficients for leading edge inflatable (LEI) kite profiles. Aerodynamic data is obtained by 2D computational fluid dynamics (CFD) simulations for different Reynolds numbers, angle of attack and profile configuration, leading to the following: lift, drag and moment coefficients, as well as the surface distribution of pressure and skin friction coefficients. The machine learning regression model is trained on this multidimensional dataset to generate accurate 2D aerodynamic predictions, which serve as essential input for the vortex step method (VSM), a fast aerodynamic solver used in kite fluid-structure interaction (FSI) simulations.

The profile geometry parameterisation forms the backbone of the automated CFD toolchain. It provides an advanced and robust design framework for LEI kite profiles. The geometry is defined by its main components: a circular leading edge (LE) tube and a canopy, which is subdivided into two splines. The front spline connects the LE tube seam (i.e., the LE tube–canopy stitching connection) to the maximum camber point, while the rear spline extends from this point to the trailing edge (TE). Both splines are modelled as cubic Bézier curves with four control points, where the first and last points define the connection boundaries, enabling smooth and flexible surface shaping. The seam angle on the LE tube is dynamically calculated to ensure a smooth transition for any given configuration. The positions of the control points are governed by the following non-dimensionalised profile parameters, defined relative to the chord: LE diameter t, maximum camber chordwise position η, camber height κ, reflex angle δ, camber tension λ, and LE curvature φ. For meshing purposes, a finite thickness is assigned to the canopy to separate the upper and lower flow regions. Additionally, a LE fillet is added to the underside of the canopy to facilitate mesh smoothing at the sharp corner connection with the LE tube.

Aerodynamic data is collected from steady Reynolds-averaged Navier–Stokes (RANS) simulations, employing the k-omega shear stress transport (SST) turbulence model. The simulations are performed with the open-source CFD software OpenFOAM, using structured meshes generated in Pointwise. An extensive mesh sensitivity analysis was conducted, focusing on the effects of canopy thickness, the LE fillet, and the resolution of the fully structured grid in both normal and tangential directions. Transition modelling was omitted based on the assumption that the boundary layer undergoes forced transition at the LE tube seams. This includes the LE tube-canopy connection on the upper side and the LE closing seam on the lower side, where numerical results indicated that the flow transitions due to seam-induced roughness. Since the region upstream of the seams is small, its impact is considered negligible, justifying the simplification.

Due to the large number of simulations required, computational resources from the high-performance computing (HPC) cluster of the Faculty of Aerospace Engineering at TU Delft were utilised. To define the parameter configurations for data collection, a trade-off was made between parameter resolution and computational cost. Parameters were sampled across the following ranges for three Reynolds numbers (Re = 1 x 10^6, 5 x 10^6, and 2 x 10^7): α from -22° to -10° (13 values), t from 0.03 to 0.12 (5 values), η from 0.08 to 0.4 (8 values), κ from 0.04 to 0.16 (7 values), δ from -8° to 0° (4 values), and λ from 0.1 to 0.4 (4 values). The LE curvature φ was held constant at 0.65.

The flow fields were analysed for the effects on the newly introduced parameters in the updated profile geometry model: δ, λ, and φ. Downward deflections of the profile TE due to negative δ resulted in reduced lift and increased drag performance, while enhancing longitudinal pitching moment stability. In contrast, variations in λ showed the opposite effect; increased camber tension resulted in higher lift and drag values but diminished longitudinal stability. The parameter φ, having minimal geometric influence, caused negligible changes in aerodynamic performance, only slightly altering the pressure distribution. Consequently, φ was fixed in the regression model. Among all tested algorithms, the extra trees (ET) model achieved the highest predictive accuracy, with R2 scores of 0.987 for Re = 1 x 10^6, 0.988 for 5 x 10^6, and 0.989 for 2 x 10^7.

This thesis presents the development, implementation, and validation of a low-fidelity Aeroacoustic Prediction Framework designed for airborne wind energy systems (AWES), with the Kitepower system as a case study. As AWES technology moves toward commercial viability, understanding and predicting its acoustic emissions becomes critical for regulatory compliance, public acceptance, and design optimization.
The framework integrates established analytical and semi-empirical aeroacoustic models with aerodynamic data based on derived geometry and detailed flight information. It models all major noise sources from the airborne components, such as the Leading Edge Inflatable (LEI) kite, bridle lines, tether, and onboard ram-air turbine. The most significant contributions to the overall noise signature were found to be turbulent boundary layer trailing edge (TBL-TE) noise from airfoils, modeled using the Brooks–Pope–Marcolini (BPM) approach, vortex-shedding noise from cylindrical structures such as the tether and bridle lines, and tonal harmonics produced by the rotating turbine blades, captured through Hanson’s helicoidal surface theory.
To generate aerodynamic input, spanwise airfoil profiles were automatically extracted from 3D CAD models and analyzed through XFOIL. Real-time flight data was provided by an onboard sensor suite and processed through an Extended Kalman Filter (EKF), allowing dynamic simulation of flight conditions. Audio recordings were collected during test flights using GoPro cameras, enabling experimental validation of the acoustic predictions despite the absence of calibrated SPL measurements.
Validation showed strong agreement between predicted and measured spectra up to 5 kHz, particularly for turbine harmonics and general spectral shape. Deviations in the lower tonal harmonics were primarily attributed to acoustic shielding caused by the turbine’s duct structure. Additionally, the use of GoPro cameras introduced limitations due to their lack of calibration data and the presence of internal low-pass filtering above 5 kHz. Despite these constraints, the model successfully predicted tonal peaks, including the blade passing frequency and higher-order harmonics, aligning well with the experimental observations.
Additionally, the framework investigates the influence of the propagation effects, such as atmospheric absorption and geometric spreading, and integrates them to produce realistic observer-based predictions. Despite using non-professional audio hardware, the predictions captured key features including harmonic roll-off and broadband trends, affirming the framework's validity for early-stage design and evaluation.
This work demonstrates that low-order, physics-based models paired with aerodynamic inputs and synchronized flight data can yield meaningful acoustic predictions for AWES. The framework offers modularity, computational efficiency, and adaptability for future upgrades, such as the use of calibrated microphones or high-fidelity CFD data. It serves as a foundation for future extensions in auralization, psychoacoustic testing, and component-level noise reduction strategies.
Ultimately, the thesis bridges theoretical modeling with field-based validation, supporting the responsible integration of AWES technologies into noise-sensitive environments.

Airborne wind energy (AWE) is an emerging technology that differs in operating principles from horizontal axis wind turbines (HAWTs). It uses tethered flying devices to harness higher-altitude wind resources. The primary motivation for AWE development lies in its potential to deliver similar energy output at lower costs and reduced carbon emissions compared to wind turbines of equivalent power ratings. AWE is in its early development stage, with commercial prototypes reaching power outputs of up to several hundred kilowatts. At this early stage of technology development, the AWE industry can significantly benefit from a systems-level understanding of the technology. To this end, the work reported here developed a multi-disciplinary design, analysis and optimisation (MDAO) tool for the conceptual system design of an AWE device and applied it to identify key design drivers, trade-offs and the scaling potential of a chosen AWE concept.

The MDAO tool is a framework that integrates models, including wind resources, power production, energy production and costs. As part of this research, new models were developed to enable the framework’s functionality. This study focused on the fixed-wing ground-generation (GG) concept of AWE. Still, the proposed methodology can be applied to any AWE concept depending on the availability of individual models tailored to the particular concept. In most markets, performance is measured using a metric known as the levelised cost of energy (LCoE). This metric relates the system's total costs to the energy it can produce over its lifetime. This metric is used here as the objective for system design, evaluating trade-offs and scaling analysis.

Airborne wind energy (AWE) is an emerging renewable technology that generates electricity using tethered flying devices, such as kites. It harvests wind energy at higher altitudes than conventional wind turbines. As the technology nears commercialization, its successful deployment will depend not only on technical and economic feasibility but also on social acceptance. Understanding how communities perceive and are affected by AWE can help ensure smoother deployment, protect community well-being, and enhance contribution to renewable energy goals.

This dissertation is among the first research to systematically investigate the social dimensions of AWE, focusing on community acceptance – residents’ approval of local energy projects – and its influencing factors. The research is based on surveys conducted with residents near AWE test sites in Europe and a laboratory listening experiment to assess reactions to AWE-related sound emissions. The findings demonstrate that community acceptance of AWE projects relates to a combination of technical characteristics, subjective perceptions, and the fairness and transparency of project implementation. In line with the applied Integrated Acceptance Model (IAM), stronger perceived impacts – such as sound emissions, landscape impacts, and aviation lights – were associated with lower levels of acceptance. At the same time, fair and transparent project implementation was linked to higher acceptance. Noise annoyance emerged as a critical factor, shaped by both psychoacoustic properties (i.e., sharpness, tonality, and loudness) and individual characteristics (i.e., noise sensitivity, familiarity with AWE, and age).

While most of the results align with research on wind turbine acceptance, some key differences emerge. Unlike for wind turbines, the remaining three IAM factors – perceived local economic benefits, expected community support for the project, and general attitudes toward the energy transition – did not significantly predict acceptance in the case of AWE. This may be due to the fact that the technology is still undergoing development and is not yet commercially available or contributing to renewable energy targets. As a result, economic and social considerations that are typically relevant for commercial energy projects may not yet be salient for communities living near AWE test sites.

The findings highlight the need to incorporate social science insights into AWE development from the outset. By investing in interdisciplinary research, developing targeted mitigation strategies, engaging with local communities meaningfully, and establishing robust regulatory frameworks, the AWE sector can avoid common pitfalls faced by established renewable energy technologies. The early stage of AWE presents an opportunity to learn from these experiences and take proactive steps to ensure that the technology is developed and deployed in a way that is both technically and socially viable. By anticipating and addressing potential social challenges early on, the sector can help ensure that AWE gains public trust and contributes to a just energy transition.

Airborne wind energy (AWE) systems harness wind power using devices flying in controlled patterns, with two main concepts: onboard power generation in the 'drag mode' and ground-based generation in the 'pumping cycle'. Quasi-steady state models (QSM) efficiently predict parameters like tether force and kite velocity for smaller AWE systems but the neglect of inertial forces leads to inaccuracies for bigger systems. Various formulations of quasi-steadiness exist in AWE literature, but their validity limits remain poorly understood. This research presents a theoretical framework specifically tailored to crosswind tethered flight dynamics, which is used to obtain a consistent definition of quasi-steadiness and to quantify the validity of this assumption, through extensive solution space analysis. The study finds that QSM provides reasonable accuracy for time-averaged quantities but fails to predict other aspects such as amplitude or phase. Limitations arise from the use of a steady aerodynamic model and assumptions of a rigid tether.

The European Commission’s roadmap aims to install 60 GW of offshore wind energy by 2030 and 300 GW by 2050, requiring substantial raw materials. Airborne wind energy (AWE) offers a promising alternative with lower material demand and environmental impact. This thesis assesses the environmental impact of a 100 kW soft-kite AWE system using life cycle assessment (LCA). The target market for these AWE systems includes off-grid remote areas. Thus, a comparative LCA of hybrid power plant (HPP) configurations was conducted using site data from a military base in Marseille, France. Results show the Falcon AWE system has a GWP of 8.6 kg CO2 eq/MWh and CED of 144.1 MJ/MWh, with the ground station being the most impactful component. For the HPP, including diesel generators and batteries reduces the oversizing of renewable components, enhancing sustainability. Future recommendations include developing AWE-specific databases and evaluating more impact indicators.

The WaveWings project seeks to harness the synergetic potential of Airborne Wind Energy Systems (AWES) and Wave Energy Converters (WEC) to create a 1 GW deep-offshore renewable energy farm, contributing to the European Union’s net-zero 2050 goals. This report details the design process and outcomes of integrating a Leading-Edge Inflatable (LEI) kite system with a point absorber wave energy converter (WEC). The LEI kite and point absorber generate 2.3 MW and 150 kW of power, respectively. Key trade-offs and design choices were made in selecting the kite, launch system, WEC, and anchoring mechanisms to optimize performance and sustainability. Sustainable design strategies are implemented to reduce the Levelised Cost Of Electricity (LCOE) and Global Warming Potential (GWP). The integration of the airborne and wave energy systems was optimized through simulations to maximize energy absorption and respect synergy requirements. Market and financial analyses indicated that WaveWings project can be competitive on the west coast of Ireland.

This paper presents the design of a lab-scale silicon nitride photonic crystal lightsail, demonstrating the LightSailSim software package. LightSailSim, an open-source analysis pipeline, integrates a custom mechanical solver based on a particle system model with optical simulation results. The resulting design balances dynamic and structural stability with propulsive performance. It consists of an optimised photonic crystal that provides a suitable trade-off between stability and propulsive force. The sail is suspended at its edges by a circular ring made of silicon, providing sufficient boundary tension to prevent sail deformation-induced instability. The design is made such that it can be produced from a single silicon wafer and levitated using a single 400 W laser.

Airborne wind energy (AWE) is a wind energy technology in the development phase consisting of tethered kites that reach high altitudes consisting of relatively stable wind speeds. While no company has yet reached the point of commercial viability, a variety of AWE technology concepts and designs have been under development and are currently at low- to intermediate technology readiness levels. When AWE systems are grid-connected a power smoothing element is needed to smooth the oscillating power output of the system for the grid. In this thesis, a framework for modeling a grid-connected hybrid power system (HPS) consisting of AWE and batteries participating in the day-ahead market (DAM) has been developed in the MATLAB environment. The framework incorporates an existing AWE performance and cost model with power smoothing performance, battery degradation, and DAM storage arbitrage. Multiple use case scenarios are evaluated to test the economic performance of multiple configurations of the HPS. These scenarios are; an AWE system with an ultracapacitor (UC); an AWE system with batteries; a battery system operating in DAM arbitrage and an AWE system with batteries operating in DAM arbitrage. The configurations were evaluated using multiple performance metrics, primarily the internal rate of return (IRR) as a metric for economic performance. A simulation of the HPS participating in storage arbitrage was then used to determine the economic viability of using the battery system for both power smoothing and arbitrage. The arbitrage behavior of the storage system was determined by a heuristic selling logic model developed to simulate the combined use of a storage system for power smoothing and arbitrage. The arbitrage logic is based on price volatility and power smoothing constraints.

The battery power smoothing system resulted in significantly lower system cost overall and consequently an increase in profitability (IRR of 12.37%) compared to the more expensive UC power smoothing configuration (IRR of 10.20%). The HPS configuration with batteries used for power smoothing combined with arbitrage showed a marginal increase in economic performance with an IRR of 12.43%. This showed a potential value increase of the system when using excess capacity arbitrage but not at a significant rate.

Computational Fluid Dynamics (CFD) simulations of objects like the Kitepower V3A Leading Edge Inflatable (LEI) kite require accurate experimental data for validation. However, for these kites, particularly in steady-state simulations, such data is scarce. LEI kites are soft and deform continuously during flight, making validation complex. To address this, a unique experiment in the Open Jet Facility (OJF) at TU Delft, testing a rigidized subscale model of the V3A kite, marking the first wind tunnel test on a rigid kite.

The rigid model was made of carbon fiber reinforced polymer and mounted on a custom support structure that allowed adjustments to the kite’s angle of attack and sideslip. Aerodynamic forces and moments were measured at different wind speeds and orientations, with and without zigzag tape to study its effects on low Reynolds number flow.

The results were compared to existing CFD simulations, including Reynolds-Averaged Navier-Stokes (RANS) and vortex step methods. The experimental data aligned well with these simulations at low Reynolds numbers, validating the setup. However, some discrepancies highlighted areas for improvement, such as reducing interference from the support structure and better matching Reynolds numbers between simulations and experiments.

The potential of utility-scale airborne wind energy (AWE) systems to contribute significantly to the energy transition hinges on their large-scale deployment, which depends on the cost-competitiveness and complementarity with conventional wind turbines. Central to the assessment of these metrics is understanding long-term energy production, which is influenced by the variability of wind profiles. This thesis investigates the significance of wind profile variability on annual energy production estimation for AWE systems. The study establishes the climatology of vertical wind profiles and expands flight operation models of AWE systems. By synthesising these aspects, a new energy production estimation framework is developed to incorporate variations in the wind profile shape. This framework is utilised to assess the impact of different wind profile shapes on the energy production estimation. The research underlines the need to move away from conventional wind energy calculation methods and offers a more suitable alternative for AWE systems. The framework offers a valuable tool for increasing the understanding of the viability of large-scale deployment of AWE systems.

Airborne wind energy (AWE) is a novel concept aiming to substantially reduce the material demand and environmental impact of wind energy generation. AWE systems can also access steadier and stronger wind at higher altitudes, which is inaccessible to conventional wind turbines. The lower material effort, the increased capacity factor, and the access to so-far unused wind resources render AWE a potential cornerstone in a future low-carbon energy economy. The new conversion concept is currently being demonstrated by several start-up companies using different technical implementations at different maturity levels. The unifying challenge for commercialization is the operational robustness of the technology. For a successful market acceptance, the introduced aviation and ground-related safety risks need to be mitigated.

The present work aims to bring AWE closer to commercial success through two main contributions. As a first contribution, well-established practices of reliability engineering are used to measure and then systematically improve the safety and reliability of AWES systems. Experience from other safety-critical domains such as aviation, space, automotive, and medical are used to achieve this objective. A fault tree analysis (FTA) and failure mode and effects analysis (FMEA) are applied to an existing demonstrator system. A common practice in the safety-critical domain is automatically monitoring the system's health and taking action in case of faults. In this regard, a systematic fault detection isolation and recovery (FDIR) model is proposed for AWES. This architecture is generally applicable and flexible and can be applied to different AWE systems.

After reaching the required reliability and safety levels, formalization by the certification authorities is required. As a second contribution, the current regulatory framework is reviewed, the relevant authorities identified and a roadmap for aviation certification is presented. The ``Specific Operations Risk Assessment'' (SORA) by the Joint Authorities for Rulemaking on Unmanned Systems (JARUS) is a comprehensive and well-structured framework. Therefore, following the SORA is considered the best way forward to get the flying permit for AWES, claiming the ``specific'' category from the European Union Aviation Safety Agency (EASA) regulation. This permit is applicable for commercial operations in Europe. Other civil aviation authorities may also recognize the EASA's flying permit. In this respect, the SORA is applied to a hypothetical commercial operation scenario, and requirements for the flying permit are discussed.

Power output during flight operation of multi-megawatt airborne wind energy systems is substantially affected by the mass of the airborne subsystem, resulting in power fluctuations. In this paper, an approach to control the tether force using the airborne subsystem is presented that improves the quality of the power output. This kite tether force control concept is implemented on the 3DOF dynamic simulation of the MegAWES reference model. First, the winch of MegAWES is resized because an analysis of winch inertia and radius shows its effect on power output and tether force overshoot. Second, the power consuming sections during the traction phase are eliminated by using a feedforward winch controller. Finally, the peak power is substantially reduced by implementing the kite tether force controller which uses a measurement of the tether force, angle of attack, and airspeed to keep the tether force constant when the system is at its power limit. This reduces the range between minimum and maximum power output by 75%.

The aim of this executive overview is to summarise the content of this extensive report regarding the design of an Landing, Launching and Storage (LLS) system for a soft kite Airborne Wind Energy (AWE) system.
An innovative idea does not translate automatically to financial gain. With new technologies, such as AWEs it is crucial to assess the potential market for a product and the associated economic performance. Four market segments exist for energy generation: on-shore on-grid, on-shore off-grid, off-shore on-grid and off-shore off-grid. AWE performs best in on-shore offgrid applications due to its high mobility, higher capacity factor compared to wind and relatively lower land usage. AWE soft kites are currently targeting 100 kW to 500 kW range, which is currently dominated by medium-power diesel generators.

Airborne wind energy systems offer a promising approach for renewable energy generation. However, the noise emissions associated with these systems should be understood and minimized as well to promote their integration and (social) acceptance. This research aims to identify the noise sources of airborne wind energy systems, systems with leading-edge inflatable (LEI) kites and fixed-wing kites in particular, and establish a foundation for future research in this field. Analytical simulations using the Brooks, Pope, and Marcolini model and the Amiet model were combined with experimental measurements for this research.

The analysis of the fixed-wing kite revealed prominent peaks around 1500 Hz and 2000 Hz in the noise spectra, with the higher frequency peak observed at higher kite velocities. Analytical predictions indicated laminar boundary layer vortex shedding and tether vortex shedding as the main noise sources. The study also investigated the directivity of the turbulent boundary layer trailing edge noise, which revealed dipoles that exhibited slight deformations at higher frequencies.

For the LEI kite, noise analysis identified peaks in the sound pressure level around 300-400 Hz and 1000-2000 Hz. Analytical predictions highlighted turbulent boundary layer trailing edge noise and vortex shedding from the tether and bridle lines as the dominant noise sources.

By considering the implications of these findings, the noise impact of airborne wind energy systems can be minimized, fostering their sustainable deployment and acceptance.

A method to help renewables flourish is mitigating the variability that is inherent in natural resources. To do so, we explore the intricacies of the relationship between airborne wind (AWE) and solar energy to uncover the possibilities of future energy-generating hybrid power plants (HPP). Which leads to steering away from fossil fuel reliance, while increasing the dependability of renewable technologies.

The resources were investigated at one primary test site, where anomalies and trends were uncovered. By tracking the solar radiation and wind speed over time, the complementarity of the two is studied. When the Pearson correlation coefficients are negative, a non-variable energy generation capacity can be found leading to less intermittency in energy stock. These results are expanded to evaluate other locations in Europe, identifying the main contributing factors of a successful hybrid set-up. The case study location was Marseille based on pre-analysis of solar and wind availability.

Using resource correlation, energy output, and location data, the model developed to assess the location feasibility of HPPs found that most areas are not suited for annual generation situations, but are more successful on a quarterly basis. The HPP setup would allow the dependency on fossil fuels and storage options to decrease while having a flexible implementation option meaning it is a viable option for off-grid / remote locations and urban areas to help lighten the grid load.

The model created can be further developed into an HPP site map, to help further identify areas that would benefit from more renewable options without as many drawbacks. Overall, this research leads to a method for reaching 2050 climate goals by identifying HPP potential on a variable time basis.

The Airborne Wind Energy industry is evolving rapidly, aiming to establish its position among the major players in the conventional renewable energy sector. Since this industry is relatively young, it faces various challenges with scaling and integration into society. One aspect that can be improved is the design process of soft-wing kites. The aim is to shift from design iterations based on experience-based hardware modifications and experimental testing to design iterations based on optimization strategies using computational simulations. For optimization-based design to be a feasible option, it requires computationally efficient underlying models. This report presents the results of thesis research that aimed to develop a fast and reasonably accurate framework for structural modeling of soft-wing kites and connected tethers.

Deformation of leading-edge inflatable kites is essential for steering and controlling aerodynamic loads. Modeling of deformation is therefore indispensable, resulting in a highly non-linear fluid-structure interaction problem. Currently, a particle system model (PSM) is the preferred choice for modeling soft-wing kites because it can simulate deformations while converging faster than other methods. The existing PSM code in Java is outdated and the current academic preference has switched to developing in Python. This led to the central research question: “Can the PSM be implemented in Python, using a
mix of Object-Oriented (OO) and non-OO programming techniques, to efficiently predict deformation?”.

While a framework was implemented based on the original Java code, it was also investigated whether it is possible to modify the solution-finding method for the structural model to improve convergence times without loss of accuracy. The aeroelastic problem can be simplified into a series of form-finding problems by assuming a quasi-steady flow. Three main families of form-finding methodologies are presented that are capable of finding solutions. Based on their properties and computational efficiency, the kinetic damping algorithm was selected as having the most potential for improving the current particle system implementation.
The kinetic algorithm was originally developed for a dynamic relaxation method that used an explicit integration scheme. Kinetic damping has never been combined with an implicit integration scheme. Slight adjustments needed to be made to the algorithm’s implementation as the current particle system
employs an implicit scheme. Analysis of verification tests revealed characteristics of the utilized implicit Euler scheme. One notable effect was numerical damping, which hurt the performance of the kinetic damping algorithm. It was discovered that the algorithm’s performance was more consistent by disabling the quadratic correction.

Validation testing showed that each method is capable of accurately predicting the shape of tether and bridle line systems. To accurately simulate the deformation of membranes, more consideration must be given to the development of the PSM that represents the considered membrane continuum.
Next, the runtimes of the self-coded solver with explicit computation of Jacobian matrices and the framework with kinetic damping algorithm were compared to the performance of a black-box solver. It was found that the framework outperformed a black-box solver in runtime, demonstrating the advantage of
using the self-coded solver. Finally, benchmarking results indicated that the modified kinetic damping algorithm, despite some limitations, generally improved runtimes. Further investigation of the scaling of the runtime against increasing amounts of DOF indicated that the current framework could be fast enough for simulation in the range of 15 to 60 particles, despite the framework’s current non-optimized state.

Using renewable energy to power a Mars habitat is a technological challenge because resources such as solar and wind are significantly weaker than on Earth. This work investigates the feasibility of using airborne wind energy (AWE) systems in combination with solar photovoltaic (PV) modules to power a Mars habitat. The Luchsinger model and the higher fidelity QSM are used to simulate the performance of the AWE system and compared. This thesis builds upon two earlier design synthesis exercise (DSE) projects by implementing a version of the quasi-steady model (QSM) that accounts for the transition phase, models a realistic retraction trajectory, and accounts for the mass of the airborne components. Additionally, the results of the first DSE indicate that the Luchsinger model used did not appear to consume any energy during the reel-in phase, which is not realistic and led to an over-prediction of mean cycle power. However, this thesis aims to implement the Luchsinger model correctly.

Creating a road map for sizing AWE kite systems on Mars is the main objective of this thesis. Since the performance of the AWE system is varying in time and space, the Mars Climate Database (MCD) is used to retrieve atmospheric and surface solar flux data including wind Weibull probability distribution functions (PDF). The second DSE used the MCD and validated the results against wind data from various Mars landers. The MCD is based on numerical simulations of the Martian atmosphere using a general circulation model and validated with available observational data. Seasonal vertical wind profiles are generated from the meteorological data to characterise the boundary layer over time. A scaling study assesses how AWE on Mars differs from that on Earth, performing dimensional analysis. In the system characteristics chapter, the initial sizing of the kite area and mass is computed using the scaling study. The performance models create the power curves, which together with the wind PDFs and surface solar flux data are used in the habitat energy model to verify whether the power requirements are met. Due to an insufficient amount of quantitative information on the energy consumption of the robotic construction of the habitat, the design of the microgrid is covering only the use of the habitat, which is 10 kW of continuous power. This is similar to remote off-grid solutions on Earth, with the additional challenge of having lower resource availability, both for wind and solar. This thesis concludes that various configurations of a hybrid power plant can continuously provide 10 kW of power throughout the entire Martian year. Moreover, the results indicate that using kites alone could generate sufficient power for the habitat without using solar PV.

Rhizome project: http://www.roboticbuilding.eu/project/rhizome-development-of-an-autarkic-design-to-robotic-production-and-operation-system-for-building-off-earth-habitats/

Published poster on thesis for Airborne Wind Energy Conference in Milano, Italy,: https://repository.tudelft.nl/islandora/object/uuid%3A4013ca0b-5508-4413-b8cb-5251ca1e7781

Video made to illustrate how Mars flight would look like.: https://www.youtube.com/watch?v=YS20vHhqgKk&ab_channel=AirborneWindEnergyOnline

With the growing global utilisation of wind energy, lowering of its levelised cost of energy is pursued. This effort is hindered by wind turbine wakes and their detrimental effects on wind farm profitability. Most currently researched wake mitigation methods are based on wind farm system-level interventions, and there is a research gap on individual wind turbine design wake alleviation measures.

This thesis investigates the wake-diffusion rotor concept, a wind turbine design with an outboard shifted thrust distribution along the blade, and its effects on wake mitigation and power generation in wind farms.
Three wind turbine rotors with equivalent thrust coefficient are modelled as actuator disks in a RANS CFD software PyWakeEllipSys, corresponding to an inboard shifted thrust distribution, a conventional thrust distribution, and an outboard shifted thrust distribution representing the wake-diffusion rotor concept. Investigated scenarios include a single wind turbine and rows of three and ten wind turbines aligned to the inflow.
Compared to the baseline case, outboard thrust distribution produces lower wake velocity deficits, increased momentum transfer and increased turbulence generation. The wake-diffusion rotor concept increases the wind farm power generation by 3.8 % and 2.5 % in the three and ten wind turbine row scenarios respectively, with the largest relative gain in wind turbine power of 13.9 % for the second wind turbine in the row. Consecutive wind turbines in the row experience diminishing gains. The inboard shifted thrust distribution had opposite effects for all of these aspects.
The benefits in wake mitigation and power generation in wind farms from using the wake-diffusion rotor concept are concluded to come from higher turbulence mixing caused by the outboard shifted thrust distribution. Recommendations on its use for maximum effectiveness are given. Future research could focus on the design implementation of this concept, investigate combinations with other wake mitigation methods and perform parametric wind farm AEP studies.

When designing an airborne wind energy system, it is necessary to be able to estimate the traction force that the kite produces as a function of its flight trajectory. Being a flexible structure, the geometry of a soft kite depends on its aerodynamic loading and vice versa, which forms a complex fluid-structure interaction (FSI) problem.
Currently, kite design is usually done on an experimental basis since no model meets the requirements of being both accurate and fast.

In this project, an FSI methodology is developed to study the steady-state aerodynamic performance of leading-edge inflatable (LEI) kites by coupling two fast and simple models.

On the structural part, the deformations are calculated with a particle system model, based on the assumption that the shape of the kite can be modelled using a wireframe wing model represented by the bridle line attachment points, whose coordinate changes are modelled using a bridle
line system model and canopy billowing relations.

On the aerodynamic side, the load distribution is calculated with a 3D nonlinear vortex step method, coupled with 2D polars obtained with a correlation model derived from Reynolds averaged Navier-Stokes (RANS) analysis, to account for viscous effects and flow separation. Furthermore, with the 2D correlation model it is possible to consider changes in the thickness and the camber of each section. Based on 2D thin airfoil theory, the three-quarter chord point is used to determine the magnitude of the forces, and the one-quarter chord point is used to determine the direction of these forces.
Moreover, the model developed for LEI kites can consider canopy billowing and variations in kite and airfoil geometry while proving robust and inexpensive.
This model has been validated with several geometries and a RANS analysis of the LEI kite, showing great accuracy for pre-stall angles of attack.

The coupling of these two models results in a fast aeroelastic model of LEI kites capable of predicting the steady-state deformations and aerodynamic forces on the kite for the range of actuation settings and inflow conditions expected during a normal pumping cycle. Furthermore, the results show that the deformations follow the same trends as the results from the photogrammetry analysis and that, by taking into account the deformations that the kite undergoes, the aerodynamic forces more closely resemble experimental data.