Coastal regions worldwide face growing flood risks from climate change. As the dominant coastal ecosystem in (sub)tropical zones, mangroves serve as natural buffers, mitigating wave action and reducing flood hazards. However, field evidence of their efficacy during extreme storm events remains scarce, limiting accurate estimation of their protective capacity. Here, we present unique measurements of storm waves (significant wave height up to 0.88 m) and their substantial attenuation (59%) within a mangrove forest, providing critical evidence that mangroves sustain significant wave attenuation capacity under extreme conditions. Utilizing this dataset, we demonstrate that a new predictive method outperforms traditional approaches in predicting wave evolution across mangroves. We have translated this advance into a user-friendly web application, enabling non-specialists to predict storm wave attenuation using available fair-weather field data. These findings confirm mangroves as essential nature-based protection against climate-intensified storm waves and provide a practical tool to advise on this protection. ... Read more

In the quest to answer the question “Are we alone in the universe?”, one of the biggest breakthroughs promises to come from detailed atmospheric characterisation of small, Earth-like exoplanets. To make such observations possible, the Large Interferometer for Exoplanets (LIFE) space mission has been proposed. This mission makes use of the nulling interferometry principle, a concept in which a null is placed on the host star, allowing for the orbiting exoplanets to be observed directly.

To achieve the required angular resolution and observational stability for the interferometric measurements, the LIFE concept would require precise control of a rotating spacecraft formation, over baselines of up to 600 meters. Four collector and one beam combiner spacecraft are arranged into a specific geometry dubbed the Emma X-array and together act as one large telescope.

This thesis, based on LIFE’s scientific requirements, presents a systems-level analysis of a future nulling interferometry formation-flying mission in the Sun-Earth L2 region. To that end, an operational phase mission simulator was developed within the circular restricted three-body problem (CR3BP) framework, with the spacecraft formation employing continuous-thrust propulsion for formation keeping, and higher-thrust impulsive propulsion for formation reconfigurations and orbital maintenance. The results show that, with this setup, a five-year mission is achievable from a ΔV perspective, requiring approximately 20 to 40 kg of propellant per collector and less than 10 kg for the beam combiner, assuming each spacecraft mass would be 1000 kg. In addition, the influence of several system characteristics on mission feasibility and ΔV requirements was quantified, with rotational speed and baseline size identified as the primary drivers of the mission propellant cost. Interestingly, the choice of reference orbit was found to have a negligible effect on the formation design. ... Read more

Life cycle assessment (LCA) is becoming increasingly important for structures in the built environment, but more development is still required. This paper proposes an augmented life quality index (LQI) for social and environmental life cycle assessment (S-LCA and E-LCA) of engineering structures. The original LQI has previously been applied in norms and literature on structural safety, but not yet in social life cycle assessment, nor with inclusion of environmental aspects. Its main application is to determine the societally acceptable failure probability of a structure by weighing costs and benefits of improvement in life safety, making the LQI an ideal candidate for further development of S-LCA and LCA in general.This paper describes how the LQI can be used while adhering to state-of-the-art S-LCA guidelines and norms. A novel augmentation based on E-LCA is then proposed, where shadow costs of embodied emissions are added to the cost of structural safety, combining social, economic and environmental sustainability. This is demonstrated through a case study based on a typical structural design, showing that the LQI can be used in S-LCA to establish the acceptable failure probability of a structure, and that the E-LCA augmented LQI-based criterion allows the acceptable reliability index to be reduced by approximately 0.1 to 0.3. This reduction is dependent on the ratio of the shadow price and material costs. ... Read more

Renewable fillers derived from residual lignocellulosic biomass could provide a more sustainable alternative to mineral fillers commonly used in bulk moulding compounds for facade applications in the built environment. However, untreated biomass fillers are moisture-sensitive and can lose mechanical performance after water exposure. Thermochemical conversion offers a potential route to modify properties such as surface chemistry, porosity, and particle micromechanics. This thesis investigates thermochemically converted carbon-based fillers for bio-based furan-resin bulk moulding compound composites, with particular focus on biochars and hydrochars produced from almond-shell residues for facade applications.

Almond-shell biochars were produced by pyrolysis at 400 (BC 400), 600 (BC 600), and 800 °C (BC 800), while hydrochars were produced by hydrothermal carbonisation (HTC) at 260 (HC 260) and 280 °C (HC 280). They were benchmarked against untreated almond shell and commercial pyrolysis-, gasification-, and activated-coconut-derived carbon fillers. Filler properties were comprehensively characterized, and composite panels were manufactured with a bio-based furan-resin matrix and tested mechanically before and after 96 h of water exposure.

Thermochemical conversion modified the almond-shell fillers, with changes depending on the conversion route. HTC caused partial carbonisation while retaining comparatively high volatile-matter contents, oxygen-containing functional groups, and much of the original cellular morphology. Pyrolysis caused stronger devolatilization, deoxygenation, and carbon enrichment. Fixed-carbon content increased from approximately 20 % in untreated almond shell to 48-52 % in the hydrochars and 71-91 % in the biochars. However, increasing conversion severity did not consistently improve composite performance.

Composite performance was strongly influenced by the manufacturing procedure. For activated coconut biochar, changing from the kneader to the SpeedMixer reduced flexural strength and modulus by approximately 53 % and 44 %, respectively. Observed resin-filler segregation suggests that the shorter mixing time and absence of controlled heating limited filler incorporation and resin contact.

Among the kneader-produced commercial composites, untreated almond shell achieved the highest flexural strength of 73.8 MPa, while activated coconut biochar reached 63.4 MPa and the highest flexural modulus of 8.45 GPa. Activated coconut also retained its mechanical properties after water exposure, demonstrating the potential of biomass-derived carbon fillers for moisture-exposed applications, although thermochemical conversion should only be introduced if it provides benefits in mechanical performance, environmental impact, and cost compared with conventional materials.

Among the SpeedMixer-produced almond-shell composites, BC 400 achieved the highest flexural strength of 45.7 MPa, while BC 800 reached the highest modulus of 3.48 GPa. HC 260 outperformed the slightly more carbonised HC 280.

Carbonisation degree, filler pH, inorganic and crystalline composition, and hydrophilicity were not consistent predictors of composite performance. Instead, three properties emerged as relevant: surface functionality, specific surface area, and filler micro-mechanical properties. Retained surface functionality was associated with the high strength of untreated almond shell, whereas activated coconut combined a highly developed surface structure with apparent resin penetration into larger pores from SEM-EDS images. Nanoindentation results suggested that low local stiffness may limit composite performance. These relationships should be interpreted cautiously because the individual effects of filler properties could not be separated. BC 400 was the most promising almond-shell-derived filler under the applied SpeedMixer conditions, while activated coconut demonstrated that biomass-derived carbon fillers can combine high mechanical performance with resistance to water exposure when effectively incorporated into the furan-resin matrix. ... Read more

Buildings account for a large share of global energy consumption, and coordinating their heating, storage, and energy conversion components effectively requires a controller that can react to changing occupancy, weather, and pricing conditions. Model Predictive Control has been widely used for this purpose, but its performance depends on an explicit dynamical model of the building, which is costly to obtain and must be rebuilt whenever the building changes. Data-Enabled Predictive Control (DeePC) offers an alternative. A controller can be designed directly from measured input-output data, without identifying an explicit model first. This comes at a cost of its own. DeePC's performance depends strongly on a small number of regularization settings, and no systematic procedure currently exists for choosing them for a new building without repeating a full, building-specific tuning process from scratch.

This thesis develops and evaluates a learned mapping from a building's physical properties and the current season to a recommended regularization setting, using Gaussian process regression. Two Gaussian process models are trained separately for each of four seasons on a grid of regularization settings, one predicting closed-loop comfort violation and one predicting energy cost. Each combination is evaluated in closed-loop simulation across three training buildings differing in size, insulation, and thermal time constant. A recommended setting is selected by identifying the candidates with the lowest predicted violation within a fixed tolerance, then choosing the cheapest among them. The same procedure is applied to the fitted Gaussian process predictions and compared against the measured sweep data.

The fitted models recover each training building's own known optimal setting in most cases, and extend to buildings outside the training set through both interpolation and extrapolation in the physical feature space. No single regularization setting performs acceptably across every building and season at once. Applying a setting outside the building it was obtained for can more than double the resulting comfort violation, confirming the need for a building-specific mapping rather than a single fixed rule.

This thesis shows that a small set of training buildings is enough to learn regularization settings that transfer to buildings and seasons not seen during training, reducing the need for a separate manual tuning procedure for every new building. The results also identify the conditions under which this learned mapping is most reliable, providing a basis for extending the approach to a wider range of buildings and operating conditions in future work.
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