Recent experimental studies report that addition of nanoparticles (tipically metal oxides nanopowders, such as Al2O3) can somehow modify the ignition mechanism of liquid fuels, probably influencing and accelerating the thermal exchange process between the fuel droplets and the surrounding air.In this paper it has been experimentally analised a stationary Diesel spray, produced by a gas assisted atomizer, preliminarily comparing the behaviour of the flame generated using the unseeded fuel with that one of the same fuel additivated with Al2O3 nanoparticles. Results obtained through light emission analysis are encouraging, in the sense that the presence of a small nano-additive quantity (0.1% in volume) seems to produce effects similar to a shift towards higher flame ventilation, with a possible positive consequence upon combustion stability and efficiency.

Nowadays the design of large-scale structures can be effectively improved by the adoption of numerical models. Even if experimental tests still play a fundamental role, a methodological approach that combines experimental testing and modelling technique can significantly improve the understanding of the matter. This, in fact, would result in a more reliable optimization process, drastically reducing efforts and uncertainties towards the implementation of the final product. The present work deals with the development of a finite element model for the analysis of a full-scale prototype of an innovative parabolic trough collector. The collector is analysed under several load conditions in order to evaluate its structural behaviour. Each load configuration is also numerically reproduced. Moreover, it is demonstrated that the model is capable of reproducing both the global (stiffness) and local (strain state) behaviour of the structure. Specifically, the comparison between experimental data and numerical results show a good agreement for the global parameter torsional stiffness. Local strain values are also well reproduced in high-stressed zone. Thus, the model can be used as a reliable “virtual tool” for designers to evaluate the suitability of layout modifications, thereby replacing and reducing the amount of commonly needed experimental tests and, consequently, reducing time and costs. Finally, an example of the potentiality of the finite element model adopted for a computer-aided engineering approach is shown to determine the most promising solution for increasing the torsional stiffness of the trough, while simultaneously limiting the required experimental tests.

Traditional aeronautical power systems, typically based on fossil fuels, present a series of limitations, including: i) the added weight associated with onboard fuel storage, ii) limited endurance due to fuel consumption, and iii) the emission of atmospheric pollutants. These constraints become particularly critical in application scenarios where extended endurance and environmental sustainability are key requirements. A notable example is represented by high-altitude, long-endurance (HALE) unmanned aerial vehicles (UAVs), whose deployment is rapidly expanding due to their suitability for a wide range of missions, including surveillance, environmental monitoring, and long-range communications. To enable the technological advancement of such platforms, alternative power generation architectures must be explored. In this context, hybrid electric power systems, integrating solar panels, lithium-ion batteries, and fuel cells, offer a promising solution. Solar and fuel cell subsystems ensure stable and continuous power generation over extended periods, including during night-time operations, while lithium-ion batteries provide high-power bursts during transient phases such as take-off, landing, or auxiliary system activation. Nevertheless, the use of these hybrid systems introduces unique challenges in terms of safety, reliability, and system complexity. They must operate in harsh environmental conditions and often require remote monitoring capabilities that enable condition-based intervention without interrupting critical missions. To address these challenges, this paper presents a Health and Usage Monitoring System (HUMS) for a hybrid power system composed of a solar panel, a lithium-ion battery, and a fuel cell, developed through the integration of digital twin modeling and artificial intelligence (AI) techniques. In particular, AI data-driven methods provide a powerful and flexible framework for monitoring the complex system composed of multiple energy sources. However, to achieve reliable performance, they require large and representative datasets, which are often unfeasible to obtain experimentally. To overcome this limitation, a digital twin of the hybrid power system is developed in the MATLAB/Simulink environment and used to simulate system behavior under both healthy and faulty conditions. The resulting synthetic data are then employed to train diagnostic/prognostic algorithms. This approach offers an efficient and scalable solution for implementing intelligent health monitoring in hybrid power systems, enhancing reliability, autonomy, and operational availability in long-endurance UAV applications.