Technological advancements are increasing system complexities, posing challenges for modern warship design. Early-Stage Ship Design (ESSD) is a vital stage, involving critical decision-making with limited information. The early-stage design of warships entails intricate decision-making considering temporal influences, interdependencies, external factors, trade-offs, high costs, evolving requirements, and other challenges. Developing well-defined system architectures is essential for managing these complexities. Model-Based Systems Engineering (MBSE) is pivotal for overcoming precision issues, inconsistencies, and difficulties in maintaining and reusing information associated with conventional document-centric approaches in systems engineering (SE).
Despite the growing popularity of MBSE, the maritime industry continues to rely on traditional document-centric approaches, lagging behind other sectors in adopting MBSE. These challenges stem from a lack of empirical evidence demonstrating MBSE's full benefits and confusion regarding its implementation practices. Moreover, there is a lack of comparative studies that assess how well MBSE tools are tailored for naval warship design. Thus, this research explores the value of MBSE in the early design stage of naval vessels. Specifically, the research aims to validate and demonstrate MBSE's benefits in developing systems architecture for warships, focusing on operational, functional, logical, and physical perspectives within the context of ESSD. By analyzing industry approaches and utilizing two representative modeling tools, this thesis provides practical insights, clarifies MBSE practices, and promotes its effective implementation in future naval design projects.
A structured research process is formulated to achieve the thesis objective. It begins by defining the mission, capabilities, and requirements. For illustration purposes, a hypothetical Landing Platform Dock vessel mission is chosen, necessitating the creation of fictitious capabilities and requirements. MBSE tools Capella and CDP4-COMET are then selected for this analysis. Next, a metamodel is established for a unified understanding among stakeholders, guiding decision-making throughout the design process. Once the baseline models are constructed, the validation and verification capabilities of the tools are evaluated. The baseline models are modified to simulate the dynamic nature of warship design. Finally, the tools are systematically assessed based on selected key MBSE factors: consistency, traceability, flexibility, and trade-offs.
Transitioning to conclusions, the analysis reveals that both tools effectively validate anticipated benefits. By synthesizing the knowledge gained, it is concluded that MBSE can enhance and accelerate the design process during the early design phase of warships. Capella demonstrates superior performance in the early design stages, whereas CDP4-COMET excels in the latter design stages. This emphasizes the critical role of integrating MBSE tools to enhance design outcomes, leveraging their strengths across diverse phases effectively. Thus, integrating MBSE tools and establishing a unified source of truth is one crucial aspect of advancing MBSE in ship design. Further recommendations are detailed in the thesis.

The rediscovered interest in space exploration has led to plans to establish outposts on the Moon and beyond. The lunar bases currently planned are to be manned incrementally, with robots performing most work. With new trends in robotics, the use of collaborating swarms has become more abundant. To support lunar operation, a distinct area within swarming, namely foraging, is proposed. Foraging systems have the primary objective of recovering resources. From literature, no foraging framework was found that included system maintenance in its mechanisms. This report aims to answer the question if this is possible while maintaining the benefits of foraging.

The research considers the fundamental act of recharging as its required maintenance task. To evaluate it dynamically, a rudimentary energetics model is included. For the framework of foraging, the work of [Adams] is used as a baseline. The newly proposed system implements an additional recharging region and role to perform recharging activities, both having major implications for role selection and agent operation. Furthermore, to enable navigation based on energy considerations, the experience communicated by mobile agents is amended to include the energy cost of a travelled path. In doing so, additional quality indicators of paths are available making path optimization a more dynamic process resulting in finer population behaviour. Finally, the decaying of beacons is updated and a fallback feature is introduced to maximize agent utilization.

The newly developed foraging system is evaluated using data collected through simulation in Webots. Simulation scenarios included obstacles with impenetrable boundaries and surfaces with increased rolling friction to emulate cost-expensive regions. Qualitative analysis identified all features of the foraging system as expected, both in the exploration and exploitation phase. Quantitative results proved that the system is able to function with the added requirements of recharging, perform path optimization with the additional path quality indicator, and can do so in various types of scenarios. With this, the research statement that foraging functionality is achievable with the practical considerations of robotics is confirmed to hold.

In-Situ Resource Utilisation (ISRU) is gaining prominence in space exploration due to its potential to reduce the number of costly launches from Earth. Among these resources, water holds significant importance for future space endeavours, thanks to its multiple applications e.g. as potable water, for harvesting crops, and as fuel after electrolysis to hydrogen and oxygen. While prior research has concentrated predominantly on the extraction of water from regolith, there has been a notable gap in exploring methods for capturing and liquefying the extracted water vapour. Therefore, initial concepts are presented and three experiments are executed. This resulted in a design proposal for the LUWEX experiment at the German Aerospace Center (DLR). LUWEX is an acronym for Validation of Lunar Water Extraction and Purification Technologies for In-Situ Propellant and Consumables Production. In particular, the amount of water extracted is planned to be higher compared to other publicly available experiments.

In recent years, radiation shielding for astronauts has become an increasing priority among international space agencies, including the European Space Agency (ESA). As international efforts advance again towards exploration beyond the magnetosphere, the risks posed by Solar Particle Events (SPEs) to crew health resurface, with threats including acute radiation sickness, increased cancer risk, and damage to internal organs.

This thesis, developed at the European Astronaut Centre (EAC), presents the design of the FLARE suit, a personal radiation shielding system for astronauts in microgravity intravehicular environments. The suit uses 36 liters of on-board water and reduces the effective radiation dose by 36 ± 4.5%, with targeted protection for vital organs and a human-centered design. Simulation and accelerator testing demonstrated shielding effectiveness, while astronaut feedback refined its usability and comfort. In final review, FLARE offers a promising solution for radiation protection in future long-duration deep-space missions.

Keywords: Radiation Shielding, Solar Particle Event, Astronaut Protection Suit, Radiation Emergency Scenario, Human-Centered Space Design.

As the Moon reemerges as a renewed fronteer in space exploration, the Lunar Zebro project proposes to deploy a swarm of miniature rovers for efficient lunar surface exploration. One of their goals is to leverage recent advancements in deep learning and AI-accelerating hardware, in conjunction with Commercial Off-The-Shelf technologies and the NewSpace movement, to enhance the autonomous capabilities of these nano-rovers. This research focuses on integrating AI-accelerating hardware within the stringent Size, Weight, and Power (SWaP) constraints of these lunar rovers. It evaluates the suitability of various hardware configurations. A Convolutional Neural Network for hazard detection was trained and tested on different devices and scenarios. Finally, the operational cycle of the rover was simulated and the constrained resources were tracked for the different design options.

Space radiation monitoring plays an important role in space system engineering. The radiation environment in space has a significant effect on the design considerations of spacecraft. During this thesis performed at CERN, there was research conducted on the Space RadMon-NG payload. The payload is designed as radiation monitor for CubeSats, containing mostly Commercial Off The Shelf components. The payload is able to measure Total Ionizing Dose using the Floating Gate Dosimeter and it is also able to measure the High Energy Hadron Fluence using a commercial SRAM device. The payload was system-level tested in CHARM for its operational and sensor performance. The Floating Gate Dosimeter was temperature characterized for the space environment. Payload data from a previous version of the Space RadMon payload on a mission called CELESTA, were also analyzed.

Modern space systems host increasingly complex payloads in shrinking spacecraft sizes. The data-intensive nature of communication payloads requires sophisticated data handling, where Field Programmable Gate Arrays (FPGAs) play a crucial role. FPGAs, configurable in software, offer custom circuit advantages without high lead times and costs. In the space environment, radiation poses a significant threat to semiconductor devices. The project focuses on improving an existing signal decoder, enhancing fault detection by a duplication with compare approach. Experimental validation under proton radiation demonstrated improved fault detection latency and localization. By optimizing the quantization of the decoding algorithm, the proposed mechanism increased resource utilization much less than typical, and indicated potential for fault detection with minimal overhead.

While image-based georeferencing systems are widely available, none are designed for edge computing on board small satellites in space. Recent advances in autonomous data processing allow points of interest (POIs) to be identified in the captured images, enabling prioritization of data and reducing required downlink bandwidths. Directly attributing geopositional information to these POIs distills the generated insights to labeled locations, eliminating the need for images to be transferred off the satellite platform. Current georeferencing pipelines rely on databases of reference images and substantial computational resources to extract and match keypoints; an operation not feasible with the raw data and hardware limitations on board low-powered satellites. This work proposes a novel architecture which addresses these challenges. By pre-extracting reference keypoints using terrestrial resources, minimal processing on board is reserved for the captured images. Using a modular system architecture, the design is generalized for various small satellite platforms. A proof-of-concept analytical model is implemented based on D2-Net. This is evaluated on a variety of Sentinel-2A datasets, and the resulting state-of-the-art spatial accuracy confirms the feasibility and significant potential of this architecture. Finally, the limitations of this implementation are explored, from which specific recommendations for future improvements are proposed.

Standardization is becoming more commonplace in space missions, aiming to increase reliability and drive down costs. Due to the relative similarities in operating environment between Small Solar System Bodies (SSSBs), a highly-adaptable lander may be able to service a majority of SSSBs with minor adaptations. The model-based systems engineering (MBSE) framework is implemented to enable agile development. The key enabling subsystems required for landing are defined, resulting in an impact dampening and a rebound suppression subsystem. Concepts for these subsystems are evaluated and traded-off. A number of astrodynamics simulations for the trajectory dynamics is made using Tudat for each SSSB, to find maximum deployment height and impact speed. The detailed subsystem design focuses on finding a suitable, highly-adaptable, commercial-off-the-shelf solution; with different variants of the final design allocated to each SSSB. Lastly, the scalability of the key enabling subsystems is analyzed. This results in the complete conceptual design of a highly-adaptable SSSB lander.

Solutions for reducing greenhouse gas emissions are paramount under the current environmental circumstances. With methane and carbon dioxide being the most critical emission gasses, SigmaSat set out to find a way to reduce these emissions and simultaneously fulfill its scientific mission. While executing the scientific mission of designing a small satellite mission to demonstrate the latest advances in artificial intelligence, SigmaSat managed to devise a design that allows players in the energy production industry (such as refineries) to drastically reduce their methane and CO2 emissions.

Programmers usually write test cases to test onboard software. However, this procedure is time-consuming and needs sufficient prior knowledge. As a result, small satellite developers may not be able to test the software thoroughly.

A promising direction to solve this problem is reinforcement learning (RL) based testing. It searches testing commands to maximise the return, which represents the testing goal. Testers need not specify prior knowledge besides the reward function and hyperparameters. Reinforcement learning has matured in software testing scenarios, such as GUI testing. However, migration from such scenarios to onboard software testing is still challenging because of different environments.

This work is the first research to apply reinforcement learning in real onboard software testing and one of few studies that perform RL-based testing on embedded software without a GUI. In this work, the RL agent observes current code coverage and the interaction history, selects a pre-defined command, or organises a command from pre-defined parameters to maximise cumulative reward. The reward function can be code coverage (coverage testing) or estimated CPU load (stress testing). Three RL algorithms, including the tabular Q-Learning, Double Duelling Deep Q Network (D3QN), and Proximal Policy Optimization (PPO), are compared with a random testing baseline and a genetic algorithm baseline in the experiments.

This study also performs regression testing with a trained RL agent, i.e., to test a version of onboard software that it has never seen before. To do that, the agent processes graph input with code coverage information. The graph is extracted from the onboard software source code via static code analysis. The work tries two graph neural network architectures (GGNN and GAT) with several graph pooling mechanisms to process the graph input.

Apart from the test command generation algorithms, some middleware is also implemented, including a command/response parser, a state identification module, a branch coverage collection tool, and a tool to extract the graph representation and node features. During onboard software testing, the onboard computer (OBC) or the electrical group support equipment (EGSE) can be the master of the bus. The command generation algorithms can run on a lab PC or a cloud server.

The research reveals the advantages and drawbacks of using reinforcement learning to test onboard software. On the one hand, RL-based testing performs well in non-deterministic environments (e.g., stress testing) and regression testing. On the other hand, more straightforward methods like random testing and the genetic algorithm are more useful in deterministic environments.

This document also introduces relative background knowledge. It leaves many recommendations for future work, such as improving sampling efficiency, generalization, and learning a model for fault detection in satellite operation.

This thesis evaluates the susceptibility to high energy proton irradiation of the NOEL-V soft processor (SP), a promising and highly modular soft processor by Cobham Gaisler. In order to characterise the performance of the NOEL-V SP in the harsh space radiation environment, the KCU105 development board is used as Device Under Test (DUT).

User logic upsets and configuration SRAM upsets are extracted and compared for multiple configurations of the processor. Although the higher performance configurations utilise more resources of the FPGA, this has not necessarily proven to also cause a higher susceptibility. The biggest influence on user logic upsets is observed to be the use of an operating system. Marginal decrease in susceptibility is found when employing the floating point unit of the processor, also an influence of the L2Cache is shown.
Findings indicate that the NOEL-V processor, with the implementation of targeted fault tolerant measures, can be a viable choice for space missions. Due to its modularity, the processor can be used for a multitude of mission types ranging from high performance general purpose to low end microcontroller applications.

To manage the increasing amount of satellite traffic in orbit and renewed operational ambitions, it is crucial to advance the capabilities of space robotic systems to enable complex manipulability tasks. These abilities will provide the necessary components to perform satellite de-orbiting, servicing of important assets, and assistance in on-orbit operations. With knowledge transfer from the terrestrial domain, the Aut-O-MAGIC research project proposes global graspability maps based on analytic grasp quality metrics to enable robust gripper operations without external intervention. Global graspability maps draw parallels from terrain traversability maps in robotic navigation, which use cost maps to plan optimised traverses ahead of time. Until now, grasping tasks in space have only had sufficient target surface information to plan the next action, disregarding optimisation or complex tasks that require multiple actions. The Aut-O-MAGIC grasp planning engine provides promising graspability cost maps that indicate populous sets of satisfactory grasps on the target object surface.

Over the last five decades, thermal modelling of airless minor planetary bodies such as asteroids have experienced significant improvements. However, at lower wavelengths of the mid-infrared range, thermal models such as the widely used Near-Earth Asteroid Model (NEATM) are still considered unreliable since reflected light starts contributing significantly towards the observed flux density. Through a controversy related to the Wide-field Infrared Survey Explorer (WISE) mission, which was an infrared survey telescope with four observational bands found at 3.4, 4.6, 12, and 22 microns, Nathan Myhrvold suggested that the thermal modelling carried out did not properly account for reflected light and that the results, especially derived from the first two observation bands, were compromised since Kirchhoff's law of thermal radiation was violated. To date, the WISE mission is considered the highest yielding mission with more than 158,000 asteroids detected, however Myhrvold's findings state that the result derived for about half of those detections are compromised. This controversy motivated this master thesis project to create a numerical code, which properly combines thermal and reflected light modelling, to further investigate the influence of the latter at the four WISE observational bands. The initial aim of this master thesis was the create an advanced thermal model, but due to the time scope of this master thesis, an intermediate thermal model named the Asteroid Thermal and Reflected light Model (ATRM) was achieved. On top of being able to model simple spherical and ellipsoidal shapes, the ATRM can model irregularly-shaped asteroids with precise orbital and rotational properties taken into account as do advanced thermal models, but assumes instantaneous thermal equilibrium as do simple thermal models such as the NEATM. Furthermore, the ATRM caters to mostly convex-shaped asteroids due to the simple shadowing algorithm implemented, and not taking into account multiple scattering. However, the ATRM is able to vary the surface albedo distribution pattern of an asteroid through an octant method, which is typically not the case for simple and advanced thermal models which all assume a homogeneous surface albedo. With the aforementioned capabilities of the ATRM, the percentage of reflected light in the total flux density at the four wavelength bands of WISE were estimated for different albedo values covering the majority of asteroids falling under the three broad Bus-DeMeo taxonomic classification system (C-, S-, X-types). Furthermore, the influence of the heliocentric distance, emissivity, and shape of the asteroid on the contribution of reflected light were investigated. Ultimately, this project is another step-wise progress in the field of physical characterisation of airless planetary bodies, especially asteroids, and has far-reaching consequences in terms of planetary formation, in-situ resource utilisation (ISRU), commercial asteroid mining, and planetary defence.

Many contemporary interplanetary missions use efficient low-thrust engines to reach the far corners of our Solar System. Their trajectories, however, have proven to be complicated to optimise due to the non-impulsive manoeuvres involved in low-thrust spaceflight. Even though shaping methods have been used extensively to reduce the computational burden, multiple-gravity assists and the presence of constraints create significant computational hurdles. Reducing the number of fitness evaluations during optimisation is one way of speeding up the search and can be done by `pruning away' regions of infeasible trajectories. In this research, we approach this by applying clustering, an unsupervised machine learning approach, to single leg trajectory optimisation problems based on a hodographic shaping trajectory model in combination with restricted two-body dynamics. Through clustering, groups of promising trajectories can be isolated so that unwanted regions can be discarded. Earth --- Mars, Earth --- Venus, and Earth --- 9P/Tempel 1 trajectories are used as test cases and are shown to exhibit periodic behaviour (related to the synodic periods of the departure and target bodies), which enables clustering on grid search-generated datasets. Different clustering algorithms were compared using the Silhouette, Davies-Bouldin, and Calinski-Harabasz internal validation indices. However, traditional clustering algorithms such as (H)DBSCAN, OPTICS, KMeans, and Gaussian Mixture Models, failed to robustly provide clusterings that can be used for pruning, because of the oblong shape of the clusters and the absence of data/noise density differences due to the artificial nature of the problem. Instead, a multimodality-based clustering model called SkinnyDip was found to be much more promising for this task. This algorithm comes with the additional advantage of having very few hyperparameters, eliminating the need for extensive parameter tuning.

For years, CubeSats have been used as flying testbeds for universities and researches, but now have more commercial applications with every day. Still, they possess certain limitations and their success rate nowadays stands at 75%, with a significant amount of faults being deemed as “unknown”.
The research addresses this issue by defining, developing, implementing and testing a novel FDIR (Fault Detection, Isolation & Recovery) concept - utilizing OBC (On-Board Computer) and TTC (Tracking, Telemetry & Control) units in “lock-step” - and a set of housekeeping telemetry parameters that increase the level of detail of CubeSat faults. To verify the design, software fault injection and fault emulation are used.System testing shows that the FDIR approach indeed increases the level of information - it is possible to determine whether the fault comes from a software bug, from a hardware flaw, if it originates from the computer storage (RAM or flash) or if it propagated from another subsystem.

he radiation shielding capabilities of polymers and Martian regolith simulant MGS-1 are analysed by means of the dose reduction of the absorbed dose radiation quantity. MGS-1 is an analog for Martian surface material while polymers are generally an effective radiation shielding material because of its high hydrogen content. Scenarios of a radiation shield and simplified Martian habitat are defined and simulated in FLUKA. The source of the radiation is a selection of radiation particle types from Galactic Cosmic Rays (GCR) in the Martian environment during a solar minimum, when GCR contributions are largest. Secondary radiation neutrons are substantially the largest contributor to the total flux on Mars, followed by protons, helium and heavy ions.

The density of the MGS-1 shield was found to have a significant influence on the dose reduction while the influence of a polymer layer in a thick MGS-1 shield did not yield beneficial gains in dose reduction. Using a simplified Martian habitat with a 250 cm thick shield resulted in a dose reduction of the selected particles of more than 90%.

Asteroids and comets are thought to be the primordial remnants of our early Solar System, their interior structure and composition providing a snapshot of this turbulent era of planet formation. Thus far, everything we know of the internals of these small Solar System bodies has been inferred from surface observations and meteorite mineralogy. Evidence suggests however that their surface characteristics might not be representative of their interior features, due to effects like granular convection and space weathering. One proposed method to directly measure these features makes use of the already present galactic cosmic ray flux. Muons are created in collisions between the asteroid surface and these cosmic rays, their properties altered by the type and quantity of material traversed. By measuring the muon flux exiting the surface, the interior can be reconstructed. This thesis surveys the feasibility of muon tomography in space, with model creation and particle passage simulations.

Human exploration on Mars is nearing actualisation and the space industry is therefore in need of vehicles with the capability of transporting goods and exploring the surrounding area. Current Mars rovers by NASA, such as the Curiosity, provide excellent researching capabilities from a remote location.

However, these land based drones are slower and less manoeuvrable around obstacles than airborne vehicles would be. Due to the need of transportation and exploration, this report aims to get acquainted with current research on Mars flight and provide a preliminary design concept enforced by the following mission goal:
”To aid future Mars colonists by providing a feasibility study of a flying vehicle capable of carrying a predetermined payload.”

To approach this goal, it was decided to determine which flying vehicle concept would be the most optimal in a Martian atmosphere via a trade-off analysis. Four options were considered and a balloon was deemed more optimal for a Martian environment compared to aircraft, rotor craft and flapping wing vehicles. The main reasons for this are that the balloon is the most power efficient and is a design that provides the least amount of risk concerning likelihood of failure and safety of astronauts.Thereafter, the process required to design a hot air balloon and the programming of the code is described. Results indicate that a balloon with a prolate ellipsoid shape is optimal with the ratio between the minor and major axis being 0.41, therefore resulting in a balloon with a major axis radius of 33.8 푚 and a minor axis radius of 13.86 푚. A surface area of 131.58푚² is selected for the flexible solar cells which have been calculated to be able to power a two propeller system ensuring that the balloon can counter headwinds of up to 15푚/푠.

Finally, to ensure altitude control is possible, a venting system is applied on the top of the balloon ensuring a venting volume of 117푚³ is possible. This is enough to ensure the balloon can land, take-off and control its altitude as desired.This thesis is therefore able to provide an initial design for a Mars exploration balloon capable of controlling not only its altitude but also its position. This is achieved by using the solar energy to increase the balloon’s temperature and a vent area of 19.03 푚² to control this temperature. All this provides a design that can be controlled and meets the versatility requirement as it is capable of carrying a variety of heavy payloads and fly past ground obstacles

GNSS-R technology is a relatively new Earth sensing approach that exploits freely available GNSS signals reflected from the Earth. From sea surface wind speed retrieval to altimetry applications, this technology is growing in popularity. In the recent years, its application to target detection is being further studied.

The goal of this master thesis has been to further explore the capabilities of GNSS-R for detecting small and fast boats. Although the detection from LEO might be rather challenging with current instruments, the results of this work show that it might be possible from lower altitudes. For a HAPS platform flying at 20 km altitude, it is estimated that the wake induced by such ships can be located with accuracies up to 500 meters for sea states
with moderate wind conditions. Nevertheless, the detection of these perturbations are expected to be rather
challenging and further research is encouraged.