Unmanned aerial vehicles (UAV) are spreading their usage in many areas, including last-mile distribution. In this research, a UAV is used for performing light parts delivery to workstation operators within a manufacturing plant, where GPS is no valid solution for indoor positioning. A generic localization solution is designed to provide navigation using RFID received signal strength measures and sonar values. A system on chip computer is onboarded with two missions: first, compute positioning and provide communication with backend software; second, provide an artificial vision system that cooperates with UAV’s navigation to perform landing procedures. An Industrial Internet of Things solution is defined for workstations to allow wireless mesh communication between the logistics vehicle and the backend software. Design is corroborated through experiments that validate planned solutions.

This research investigates the optimization of airfoil design for fixed-wing drones, aiming to enhance aerodynamic efficiency and reduce drag. The research employs Kulfan CST and Bézier surface parameterization methods combined with global sensitivity analysis (GSA) and machine learning techniques to improve airfoil performance under various operational conditions. Particle swarm optimization (PSO) is utilized to optimize the airfoil design, minimizing drag in cruise and ascent conditions while ensuring lift at takeoff. Computational fluid dynamics (CFD) simulations, primarily using XFOIL, validate the aerodynamic performance of the optimized airfoils. This study also explores the generative design approach using a neural network trained on 10 million airfoil simulations to predict airfoil geometry based on desired performance criteria. The results show important improvements in drag reduction, especially during low-speed cruise and ascent phases, contributing to extended flight endurance and efficiency. These results can be used for small unmanned aerial vehicles (UAVs) in real-world applications to develop better-performance UAVs under mission-specific constraints.

Micro combined heating and power (micro-CHP) systems are becoming more than important, and even essential, if we pretend to take full advantage of available energy. The efficiency of this kind of systems reaches 90% and important savings in energy transport processes can occur. In this research, an internal combustion engine (ICE)-based micro-CHP system was developed and tested under specific constraints. The system uses a two cylinder Otto engine as prime mover, coupled to an electrical alternator, and it uses exhaust gases and engine cooling circuit heat. The micro-CHP system was developed to match the electrical power of a typical Stirling engine (SE)-based micro-CHP unit, in order to later compare both systems’ performance under similar circumstances. Different operating modes were tested under different engine speeds, in order to find the optimum operating point. A stand-alone portable application of this system was performed using recreational sailing boats as mobile homes. Specific considerations had to be taken, related to boundary conditions with sea water, and a transient simulation was performed, considering the boat under three different European climates. Results were compared for the different locations and the performance of the equipment shown. A comparative study with the SE-based micro-CHP system performance was done, and a sensitivity analysis of the influence of the battery size was carried out under the same conditions. The SE and ICE-based proposed micro-CHP system have similar behavior, except for the differences found due to the electric/thermal power ratios in both systems. Battery bank size sensitivity analysis reflects a limit in performance improvement. This limit is caused by the uniform distribution of electrical demand profile.

It is thought that there are 14 million standardised sea containers out of order, equal to 23.3 million twenty-foot equivalent unit (TEU), which could be devoted for other purposes. The COR-TEN^®steel containers are the best, due to the features of the materials, to be used in applications intended for the accommodation of people. One of the uses is framed in the area of humanitarian help, as service modules in areas of social emergency. Moreover, the deployment of these kinds of modules is quite simple, as the Army is generally able to transport containers in its units. In this project we have selected three typologies of service modules and we have designed modules based on sea containers which can fit into these typologies. The container chosen to be recycled is the 1 TEU, to which the closings and divisions are defined, which must reflect the different typologies of the service modules chosen. Afterwards, a study of thermal charges is carried out on three different settings of service modules located in five locations spread in different climate areas. Throughout a thermal simulation in Trnsys^® (V17, Thermal Energy System Specialists, LLC, Madison, WI, USA), the consumption and maximum demand of heat and cold are analysed. Based on these results, the best equipment to cover the energy demand of the modules analysed in this study could be chosen. Moreover, the results can be used to estimate the operational costs of the deployment of the modules.

The design of a vehicle launch comprises many factors, including the optimization of the climb path and the distribution of the mass in stages. The optimization process has been addressed historically from different points of view, using proprietary software solutions to obtain an ideal mass distribution among stages. In this research, we propose software for the separate optimization of the trajectory of a launch rocket, maximizing the payload weight and the global design, while varying the power plant selection. The launch is mathematically modeled considering its propulsive, gravitational, and aerodynamical aspects. The ascent trajectory is optimized by discretizing the trajectory using structural and physical constraints, and the design accounts for the mass and power plant of each stage. The optimization algorithm is checked against various real rockets and other modeling algorithms, obtaining differences of up to 9%.