Static waste heat recovery, by means of thermoelectric generator (TEG) modules, constitutes a fast-growing energy harvesting technology on the way towards greener transportation. Many commercial solutions are already available for small internal combustion engine (ICE) vehicles, whereas further development and cost reductions of TEG devices expand their applicability at higher-power transportation means (i.e., ships and aircrafts). In this light, the integration of waste heat recovery based on TEG modules in a shipboard distribution network is studied in this work. Several voltage step-up techniques are considered, whereas the most suitable ones are assessed via the LTspice simulation platform. The design procedure of the selected LLC resonant converter is presented and analyzed in detail. Furthermore, a flexible control strategy is proposed, capable of either output voltage regulation (constant voltage) or maximum power point tracking (MPPT), according to the application demands. Finally, both simulations and experiments (on a suitable laboratory testbench) are performed. The obtained measurements indicate the high efficiency that can be achieved with the LLC converter for a wide operating area as well as the functionality and adequate performance of the control scheme in both operating conditions.

The concept of more electric aircraft (MEA) has gained popularity over the last few decades. As the power level of electric loads is constantly increasing, the installation of advanced protection systems becomes of paramount importance. In this context, this paper presents the design process and experimental validation of a solid-state circuit breaker (SSCB), utilizing gallium nitride (GaN) semiconductor switches, under various faulty conditions. In addition, a thermal analysis was carried out in the PLECS simulation platform to find the most appropriate design for the heat dissipation system. Experimental results on the developed GaN SSCB hardware prototype verify its functionality and good performance.

The use of nature-based solutions (NbS) to address the risks posed by hydro-meteorological hazards have not yet become part of the mainstream policy response, and one of the main reasons cited for this, is the lack of evidence that they can effectively reduce disaster risk. This paper addresses this issue, by providing model-based evidence from five European case studies which demonstrate the effectiveness of five different NbS in reducing the magnitude of the hazard and thus risk, in present-day and possible future climates. In OAL-Austria, the hazard is a deep-seated landslide, and the NbS analysed is afforestation. Modelling results show that in today's climate and a landcover scenario of mature forest, a reduction in landslide velocity of 27.6% could be achieved. In OAL-Germany, the hazard is river flooding and the NbS analysed is managed grazing with removal of woody vegetation. Modelling results show that the NbS could potentially reduce maximum flood water depth in the near-future (2031-2060) and far-future (2070-2099), by 0.036m and 0.155m, respectively. In OAL-Greece, the hazard is river flooding, and the NbS is upscaled natural storage reservoirs. Modelling results show that in a possible future climate the upscaled NbS show most potential in reducing the total flooded area by up to 1.26 km2. In OAL-Ireland, the hazard is surface and river flooding, and the NbS is green roofs. Results from a modelled upscaling analysis under two different climate scenarios show that both maximum flood water depth, and total flooded area were able to be reduced. In OAL-UK, the hazard is shallow landslides, and the NbS is high-density planting of two different tree species. Modelling results under two different climate scenarios show that both tree species were able to improve slope stability, and that this increased over time as the NbS matured. The significance of these results is discussed within the context of the performance of the NbS over time, to different magnitude type events, impact with stakeholders in engendering wider support for the adoption of the NbS in the OALs, and the uncertainty in the modelling analyses.