瓦伦西亚理工大学

This study evaluates the cradle-to-grave (CTG) greenhouse-gas (GHG) emissions of a light-duty hydrogen internal combustion engine (HICE) vehicle by combining vehicle simulations, which estimate fuel consumption and GHG emissions under real-world driving conditions, and a life-cycle assessment (LCA) covering the vehicle’s lifetime up to 2050 under four different hydrogen production scenarios. The environmental performance of this vehicle is compared with that of its gasoline-fueled counterpart, considering both conventional and hybrid powertrains, to assess the potential of hydrogen fuel and hybridization to decarbonize the road-transport sector. The results show that HICE vehicles can achieve substantially lower CTG GHG emissions than their gasoline counterparts, although their environmental performance is highly dependent on the hydrogen production pathway. Under the assumptions adopted for the 2050 scenarios, SMR-based hydrogen production pathways incorporating CCS and biogenic feedstocks exhibit the lowest life-cycle GHG emissions. However, when considering the worst-case scenarios under maximum uncertainty, the situation is reversed, with the scenarios dominated by renewable water electrolysis yielding the lowest emissions, suggesting that these findings should be interpreted with caution. Hybridization generally improves fuel efficiency, but its environmental benefits strongly depend on the carbon intensity of hydrogen production and driving conditions, with potential advantages for the selected hybrid architecture only under urban driving. These results highlight the importance of considering representative driving conditions when evaluating the environmental performance of alternative fuels and powertrain configurations, and demonstrate the value of linking vehicle-level driving-cycle analysis with LCA to assess their life-cycle GHG emissions.

With the increasing threats posed by global climate change, artificial coastal structures remain essential to mitigate coastal erosion and flood risks in many regions worldwide. However, these structures are often criticized for introducing non-native materials that disrupt habitats and support lower biodiversity than natural rocks. Over the past three decades, eco-engineering has emerged as a solution to mitigate these impacts while maintaining their protective function. This study analyzes field and laboratory tests in the literature to identify advances in ecological engineering and low-carbon materials for the design of ecologically enhanced coastal structures. Key interventions include increasing surface complexity and heterogeneity of artificial substrates, evaluating the suitability of substrate types for biodiversity enhancement, and developing sustainable bio-enhancing concrete blends. Research highlights that incorporating surface roughness at scales from millimeters to decimeters, coupled with appropriate material selection can significantly enhance biodiversity while maintaining engineering performance. Examples of cost-effective habitat enhancement solutions include the BIOBLOCK unit ($2500 per unit), drill-core pools (€60 per pool), and artificial rock pools (€38 per pool). Concrete, especially with low-binder cements like slag cements (CEM III/B), is a suitable alternative when natural rock is unavailable. In this paper, studies are selected based on their relevance to coastal engineering practices, highlighting areas where further research is needed and offering recommendations to overcome barriers to the effective implementation and broader adoption of more sustainable and multifunctional coastal defences. Challenges include strengthening the evidence on achievable enhancements, clarifying cost-benefit analyzes, and improving data presentation for decision-making.

This paper investigates the stabilization of time-varying input delayed Linear Parameter Varying (LPV) systems with rational dependence on the scheduling parameters under sequential subpredictor (SSP) approaches. Although the SSP-based control scheme has the advantage that larger delays can be tolerated in the control loop by introducing more and more subpredictors for linear systems, the LPV nature of the system together with the presence of time-varying delay mismatches may compromise its effectiveness. To alleviate this limitation and enhance robustness against time-varying delay uncertainties, a novel SSP with gain-scheduling is developed generalizing prior related works. Finally, the effectiveness of the proposed method is illustrated by a simulation example.

Process-based models (PBMs) are increasingly required to evaluate forest management under climate change, but their use in decision support is limited by site-specific parameterisation, which remains a manual, expert-dependent step performed outside the decision framework. We extended the CAFE Decision Support System, which couples the Biome-BGCMuSo model with multi-objective evolutionary optimisation, with (i) an autocalibration plugin that calibrates the model within the same optimisation environment used to generate management alternatives, and (ii) new indicators that respond directly to management and disturbance: a fuel-based ignition risk index and prospective indices of resistance and resilience to an imposed drought. The workflow was tested in a semi-arid Pinus halepensis plantation in eastern Spain, where a long-term thinning experiment provides independent field data. Calibrated against transpiration, soil water content and stored biomass, the selected parameter set achieved Nash-Sutcliffe efficiencies of 0.65, 0.73 and 0.95 and the same set was selected under every aggregation criterion tested. A global sensitivity analysis identified maintenance respiration, leaf nitrogen in Rubisco and boundary layer conductance as the dominant parameters, acting mainly through interactions. Across 100 Pareto-optimal solutions, a first thinning intensity of 60% or higher was observed in half of the cases (49 out of 100 solutions), followed by a second intervention around 12–13 years, consistent with the treatment and the duration of its effects documented in the field. Relative to the unmanaged control, the alternatives increased deep percolation 3.5-fold, reduced ignition risk by 24%, at the cost of a 48.9% reduction in standing biomass. Mean drought resistance and resilience did not differ from the control, but 37 alternatives improved on it in both. The study provides a replicable framework for coupling PBMs with multi-objective optimisation in forest planning, in which the case study serves as a test of the workflow rather than as a silvicultural recommendation.

This study performs the analysis of oxymethylene ether (OMEx) spray combustion in a quiescent combustion chamber at Diesel-like conditions following the Engine Combustion Network guidelines. The paper presents the incorporation of an Unsteady Flamelet Progress Variable combustion model into a Large Eddy Simulation work flow and the corresponding experimental validation that produces strong agreement in terms of main spray development (tip penetration) and combustion (ignition delay and lift-off length) metrics. Furthermore, good agreement is also found for the spatial distribution of relevant species that describe the low-and high-temperature ignition stages, namely CH2O and OH. Particular attention is paid to the prediction of chemiluminescence species (OH*, CH*, C2*), which can be validated by experimental image recording. This step is shown to be critical for the prediction of experimental OH* fields. Using ground-state OH, which is often found in the literature, is not good practice, as the spatial location of both excited-and ground-state species is highly different, especially for non-sooting fuels such as OMEx. Among the different simulated chemiluminescent species, OH* is found to be the best tracer of the flame location due to its widely spread spatial distribution. Finally, the fuel age concept is implemented into the simulation as a passive scalar, and it is found to provide valuable insights into mixing history and transient combustion processes. Its correlation with species distributions reveals that regions of high OH concentration coincide with elevated fuel age, reflecting near-equilibrium conditions, while OH* dominates younger rich mixtures near the lift-off length, where chemical state is not yet at equilibrium. Future work will focus on applying the proposed methodology across a wider range of ECN conditions and fuels to both improve the understanding of e-fuels combustion behaviour relative to conventional fuels and further develop integrated CFD-optical diagnostic validation strategies for spray combustion research.