The management of toxic leaks in confined environments, such as CERN's experimental caverns and space habitats, is crucial due to potential risks to human life. This thesis examines aggressive carbon dioxide releases from CERN's 2-Phase Accumulator Controlled Loop (2PACL) system, used in both terrestrial (ATLAS, CMS, LHCb) and extraterrestrial (AMS) experiments. Computational fluid dynamics (CFD) simulations were performed to assess the severity of various failure modes. A two-dimensional analysis of the immediate leak area showed that shock waves travel up to 30 centimeters from the failure point. Subsequent three-dimensional simulations in the CMS experimental cavern (UXC55) revealed that while the upper cavern floors are well-protected, the lower floors pose significant risks due to semi-confined spaces and worse ventilation. Recommendations include the use of oxygen deficiency monitors, self-rescue masks, and detailed evacuation plans to enhance worker safety.

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.

The number of satellites in orbit is increasing at an accelerating pace. One major issue that currently hinders the range of satellite applications is data analysis. Most data is transmitted back to ground stations for analysis resulting in bandwidth-related issues, or the processing is performed on board, necessitating large computational power. Spacecraft, particularly miniaturized ones, face stringent energy constraints due to the scarcity of resources and the harshness of the space environment. Processing data at the edge using Spiking Neural Networks (SNN) applied on mixed-signal Neuromorphic Computing (NC) processors has been proposed as a potential solution. Neuromorphic devices focus on low power and energy-efficient operation, which aligns well with the requirements of space applications. Nevertheless, the response of these devices to the harsh conditions of the space environment remains only partially understood. The primary uncertainty lies in the impact of cosmic radiation, which can present substantial challenges, even in low orbits. Radiation not only has the potential to damage hardware but also to interfere with its operation, leading to potentially detrimental software failures. The objective of this work is to describe and analyze the effects of space radiation on NC processors. A behavioral model of different types of radiation effects is composed and experimental verification is performed at a proton beam facility using a mixed-signal NC prototype. The radiation effects are analyzed, and their influence on network operation is discussed. It is demonstrated that mixed-signal NC is resistant to perturbations caused by radiation. Additionally, software mitigation strategies are proposed to further increase the applicability of SNNs in radiation environments.

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.

Neuromorphic architectures are of interest in space application due to their energy efficiency. However, space radiation has been shown to damage computational hardware. Research has been performed on the radiation robustness of (pre-trained) spiking neural networks, and the brain has shown to be able to recover from certain types of lesions. Other work has shown Spiking Neural Networks (SNNs) can obtain advantages for graph algorithms. Such (distributed) SNN graph algorithmsmay become relevant for space applications. For example, SNNs may form a neuromorphic implementation of the Minimum Dominating Set (MDS) algorithm that others have proposed for distributed satellite swarm coordination. This research combines the trend of SNN implementations of distributed graph algorithms with neuromorphic space applications and brain-adaptation induced robustness as inspiration. It shows that brain inspired adaptation mechanisms can increase the simulated radiation robustness of an SNN implementation of an unweighted, distributed Minimum Dominating Set Approximation (MDSA) algorithm. An SNN implementation of the MDSA algorithm by Alipour et al. is created and used for this experiment. That SNN is adapted with a population coding approach, and with a sparse redundancy approach. These three SNNs are exposed to simulated radiation effects in the formof synapticweight increaseswhich occurwith a probability of 0.001% to 20% per synapse per timestep. Separate simulations are performed with a simulated radiation induced permanent neuron death with a probability of 0.01 % to 25 % per neuron per timestep. The SNN performance is measured by comparing its output to the unradiated algorithm output. The sparse redundancy increases the robustness against simulated radiation induced neuron death for radiation probabilities from0.5 % to 25% per neuron per time step, for theMDSA SNN. Belowthese probabilities, the adaptation mechanism is contra productive. Similarly, population coding is contra productive below0.1% and increases radiation robustness for simulated synaptic weight increase probabilities of up to 5%.

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.

With a continuously growing number of satellites in orbit, it becomes increasingly important to assess their impacts on the Earth's environment in a standardised manner. While interest in Life Cycle Assessment (LCA) for space missions has gained in strength in the past few years – particularly in Europe – no consensus has yet been reached on a single-score LCA system.

In this thesis, a consensus-based space LCA single-score is created through an international survey of experts. The report demonstrates retroactively the single-score’s use in ecodesigning the Delft University of Technology’s Delfi-n3Xt space mission. Moreover, a discussion is held on ways of implementing the single-score into early design phases.

Overall, this thesis highlights the importance of an easy-to-understand LCA tool for space systems. It shows the necessity for a tool that is implementable during the design phase of the mission, to incentivise space actors to further consider environmental impacts.

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.

The harsh space radiation environment affects both satellite equipment and astronauts. Although a lot of research on this environment has been performed, knowledge gaps remain. One of the main issues is the lack of data with a large spatial and temporal coverage. This can be resolved by the development of simple, compact, cheap but yet precise radiation monitoring tools. One such recently developed tool is a sensor chip based on floating gate dosimeters. In this thesis research, the interface between this chip and a CubeSat testbed has been tested. Additionally, proton beam tests have been performed to verify the characteristics of the sensor chip. It was concluded that the simple chip can be widely deployed, but that more testing is required to compare its performance with other dosimeter technologies.

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.

A miniaturized instrument was developed to enable in-situ measurements of the radiation environment at the Moon. The instrument is designated to function as the science payload for the first mission of the Lunar Zebro nano-rover. Characterization and testing of the Floating Gate Dosimeter (FGDOS) was advanced with an emphasis on its utilization as the core detector for this payload. A prototype of the Radiation Payload was designed, produced and tested. Radiation environment prediction and analysis was performed for various mission phases using SPENVIS and OLTARIS. A radiation transport model of the payload was prepared, as a foundation for more extensive simulations in the future.

This thesis highlights the need for further research and development of the FGDOS technology, including experimental mapping of the complete envelope of FGDOS sensitivity based on expected mission radiation environments. Additional noise reduction measures and thermal characterization at mission conditions are needed to iterate and improve the payload design such that it can be made flight-worthy.

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.

Relative navigation systems based on monocular cameras are currently under active investigation for their capability to deliver fast pose solutions under low power and mass requirements. Cameras depend on the passive collection of radiation, making their performance highly dependent on illumination conditions and image background. In the presence of undesirable lighting conditions, their performance may decrease significantly. This thesis aims to assess the performance of a guidance system given these challenges of vision-based pose estimation systems. A number of representative undesirable scenarios was defined, and the behaviour of the GNC system, specifically guidance, was evaluated for these scenarios. This work formulated mitigation strategies, required on multiple occasions. Furthermore, the largest source of error was found to be due to the prediction of the target attitude dynamics and a new method was developed to improve this prediction. Finally, the design of trajectories with optimal lighting conditions was explored, resulting in the design of a rendezvous trajectory with favourable lighting conditions.