This study presents a data-driven methodology to optimize the operational efficiency of a tugboat equipped with a Voith-Schneider Propeller (VSP) based on full-scale fuel consumption and vessel performance data. The objective is to identify optimal combinations of engine RPM and propeller pitch to reduce fuel consumption during low-demand phases without compromising maneuverability. Sea trials were conducted under controlled conditions using a dual flowmeter system and onboard speed measurements. The data enabled the construction of performance curves, efficiency ratios, and interpolated maps of fuel consumption. Optimal configurations were identified across defined speed ranges, and continuous efficiency zones were visualized through iso-consumption and contour plots. The results reveal a nonlinear relationship between propeller pitch, speed, and fuel demand, with maximum efficiency occurring at medium-to-high pitch values and speeds between 3 and 6 knots. This methodology provides a replicable tool for energy management in port operations and supports informed decisions during accompanying operations and standby periods. Efficiency differences over 300% between RPM–pitch settings were found, highlighting the operational impact of informed configuration choices. Moreover, the structured dataset and visual analysis framework lay the groundwork for future digital twin models aimed at enhancing operational efficiency in VSP-powered tugboats.

Most of the research on ship rolling motion concerns a specific type of ship, such as containerships, or analyzes sailing during certain wave characteristics, rather than the full spectrum of waves that can be encountered during the entire sea route. To date, the most frequent merchant ships in the world are general cargo ships, where the ship’s behaviour at any sea condition has a great influence on overall safety, as well as the lashing design of non-standardized cargo. For this reason, the present paper aimed to study ship-performance models, starting with concepts of the basic physics of ships and waves. Firstly, the ship’s behaviour was analyzed from a theoretical point of view, both in calm waters and when sailing in waves, but independently. Afterwards, the ship-waves system was analyzed during rolling with all variables accounted for at the same moment, with the objective of obtaining roughly realistic models. Relevant results are shown in each of the models, which may be of great interest for ship operators and, in general, for the shipping industry, so as to improve the safety of maritime transport. Finally, these results were validated with a case study.

Engineering education worldwide is facing two major challenges: a shortage of students and a high dropout rate in this field of study. The main objective of this study is identifying new and improved teaching methodologies that motivate students, enhance their essential professional skills, and provide insight into the societal impact of engineering work. In order to achieve this, the study focused on a topic of great concern to the general population: hydrometeorological emergencies resulting from climate change. These emergencies are increasing in frequency and intensity, posing significant challenges where engineering can contribute solutions to mitigate their severe effects. A dual hackathon was designed, incorporating a real-world immersion experience. Graduate students in engineering disciplines participated in a simulation of flooding in a nearby town, experiencing firsthand the planning and execution of interventions, the application of technology for various rescue operations, and the management of interoperability among resources and teams. Following this immersive experience, students took part in a hackathon where they developed innovative solutions in diverse fields to improve the mitigation of human and economic damages. The competency evaluations conducted after the dual hackathon revealed improvements ranging from 20% to 70%, with key technical skills such as "preventive planning" and "audits and certifications" increasing by 33% and 21%, respectively. Paired t-tests confirmed these gains (p < 0.005), with large and moderate effect sizes (Cohen’s d = 0.87, 0.59). Moreover, civil protection and risk management competencies saw increases of over 70% (p < 0.001, Cohen’s d > 1.4), reinforcing the strong impact of this approach. These results consolidate the dual hackathon as a comprehensive, attractive and effective teaching methodology that significantly improves the training of Engineering 5.0 students.

Maritime transport is the most widely used means of transporting goods, and forecasts indicate that it will continue to grow in the coming years, which is why the IMO is regulating energy efficiency and emissions from maritime transport with exhaustive monitoring. The most widely used measure of energy efficiency during operation on a ship is the Energy Efficiency Operational Indicator (EEOI); however, the difficulty in obtaining references for this indicator, together with the great variability that exists between shipowners when choosing terms such as “cargo transported”, among others, means that this operational indicator of energy efficiency in ships does not have the expected results. This work develops a two-phase procedure for the selection of representative EEOI values for ships, taking into account parameters such as ship speed, cargo, mode of operation and the subsequent determination of a suitable control system that allows the continuous and real-time implementation of measures to improve energy and environmental efficiency on the ship. The proposed final procedure is effective in terms of improving energy efficiency and emissions, on top of being simple, easily implementable, working in real time and adapting to navigation circumstances while keeping emissions under control.

There are different monitoring procedures in wind farms with two main objectives: (i) to improve energy production by the capability of the national electrical network and (ii) to reduce the stooped hours due to preventive and or corrective maintenance activities. In this sense, different sensors are employed to sample in real-time the working conditions of equipment, the electrical production and the weather conditions. Despite this, just the anemometer measurement can be related to the more important errors of interruption of power regulation and anemometer errors. Both errors are related to gusty winds and contribute to more than 33% of the cost of a wind farm. The present paper reports some mathematical relations between weather and maintenance but there are no extreme values of each variable that let us predict a near failure and its corresponding loss of working hours. To achieve this, statistical analysis identifies the relation between weather variables and errors and different models are obtained. What is more, due to the difficulty and economic implications involving the implementation of complex algorithms and techniques of artificial intelligence, it is still a challenge to optimize this process. Finally, the obtained results show a particular case study that can be extrapolated to other wind farms after different case studies to adjust the model to different weather regions, and serve as a useful tool for weather maintenance.