Despite the increasing demand of Unmanned Aerial Vehicles (UAVs) for a wide range of civil applications, there are few methodologies for their initial sizing. Nowadays, classical methods, mainly developed for transport aircraft, have been adapted to UAVs. However, these tools are not always suitable because they do not fully adapt to the plethora of geometrical and propulsive configurations that the UAV sector represents. Therefore, this work provides series of correlations based on off-the-shelf components for the preliminary sizing of propulsion systems for UAVs. This study encompassed electric and fuel-powered propulsion systems, considering that they are the most used in the UAV industry and are the basis of novel architectures such as hybrid propulsion. For these systems, weight correlations were derived, and, depending on data availability, correlations regarding their geometry and energy consumption are also provided. Furthermore, a flowchart for the implementation of the correlations in the UAV design procedure and two practical examples are provided to highlight their usability. To summarize, the main contribution of this work is to provide parametric tools to size rapidly the propulsion system components, which can be embedded in a UAV design and optimization framework. This research complements other correlation studies for UAVs, where the initial sizing of the vehicle is discussed. The present correlations suit multiple UAV categories ranging from micro to Medium-Altitude-Long-Endurance (MALE) UAVs.

The operation of various types of turbomachines is importantly affected by sediment erosion. Francis turbines used for power generation typically suffer said effects due to the fact that they are used in sediment-laden rivers and are usually operated disregarding the long-term effect of the erosion on turbine performance. This investigation seeks to study the erosion rate for the main components of the turbines located at San Francisco hydropower plant in Pastaza, Ecuador. A sediment characterization study was performed in order to determine the properties of the particles present in Pastaza River and accurately predict their effect on the turbine flow passages. A numerical approach combining liquid–solid two-phase flow simulation and an erosion model was used to analyze the erosion rates at different operating conditions and determine wear patterns in the components. As expected, the results indicated that an increase in the erosion rate was obtained for higher intake flows. However, a dramatic increase in the erosion rate was observed when the turbine was operated at near-full-load conditions, specifically when guide vane opening exceeded a 90% aperture.

Precise temperature control in high-temperature furnaces is challenged by nonlinearities, parameter drift, and high thermal inertia. This study proposes an adaptive control strategy combining a classical PID loop with real-time gain updates from a feed-forward artificial neural network (ANN). Implemented on an 18 kW retrofitted Blue-M furnace, the system was characterized by second-order transfer functions for heating and forced convection cooling. A dataset of 9702 samples was built from eight fixed PID configurations tested under a multi-ramp thermal profile. The selected 3-64-64-32-2 ANN, executed in Python and interfaced with LabVIEW, computes optimal gains in 0.054 ms while preserving real-time monitoring capabilities. Experimental results show that the ANN-assisted PID reduces the mean absolute error to 5.08 °C, limits overshoot to 41% (from 53%), and shortens settling time by 20% compared to the best fixed-gain loop. It also outperforms a fuzzy controller and remains stable under ±5% signal noise. Notably, gain reversals during cooling prevent temperature spikes, improving transient response. Relying on commodity hardware and open-source tools, this approach offers a cost-effective solution for legacy furnace upgrades and provides a replicable model for adaptive control in high-temperature, safety-critical environments like metal processing, battery cycling, and nuclear systems.

Current environmental policies for the aviation sector motivate the use of cleaner propulsion alternatives in order to reduce their CO₂ footprint and noise pollution in the coming years. In this context, hybrid propulsion systems have emerged as a potential solution, as they have demonstrated a good trade-off between performance and low pollutant emissions. The present work carries out a comparison between parallel and series hybrid propulsion systems using heterogeneous and homogeneous distributed propulsion architectures. In order to highlight the opportunities of distributed propulsion systems and validate the methodology developed, a single propulsion hybrid configuration is used as baseline case for this study. For the propulsion system sizing, this work uses a parametric modelling tool, which includes a constraint analysis coupled with a weight estimation module to determine suitable configurations for a environmental monitoring mission. The latter module includes semi-empirical correlations to size the electric and mechanical components for each propulsion setup. From the results, it has been found that for the representative case of monitoring in the Galapagos Islands, which requires an endurance of approximate 7 h, the parallel hybrid system using three distributed propulsors presents the best performance features in terms of fuel savings, showing a 34% reduction compared with the baseline case. To summarize, the main contribution of this study lies on the development of a methodology to set potential hybrid distributed propulsion configurations for UAVs aimed for determined monitoring missions.

A representative urban driving cycle was developed for Quito, Ecuador, using the K-Means clustering method. From 64 samples and 188,713 geospatial and speed data points, a 2870 s driving cycle was constructed to capture real-world traffic characteristics. Key parameters include an average speed of 22.68 km/h, acceleration and deceleration rates of 0.55 m/s² and −0.57 m/s², and a dwell time of 9.66%. Due to Quito’s linear urban development, where mobility is limited to north–south/south–north corridors, the driving cycle reflects frequent accelerations and decelerations along congested arterial roads. A comparative analysis with international driving cycles revealed that Quito’s traffic follows a unique pattern shaped by its geographic constraints. The HK cycle in China showed the greatest similarities, although differences in instantaneous speeds highlight the need for localized models. While this study primarily focuses on methodological robustness, the developed driving cycle provides a foundational dataset for future research on traffic flow optimization, emissions estimation, and sustainable urban mobility strategies. These insights contribute to data-driven decision-making for improving transportation efficiency and environmental impact assessment in cities with similar urban structures.

In this work, the NSGA-II multi objective genetic algorithm, numerical methods, and parametric design techniques found in the Autodesk Inventor professional 2023 CAD software were combined to perform the geometrical optimization of the Pelton bucket geometry. The validation of the proposed method was carried out with numerical simulations using the OpenFOAM CFD program and taking into account the case study turbine’s operating conditions, as well as the k-SST turbulence model. The CFD simulation results and operational data from the case study turbine from the “Illuchi N°2” hydrocenter have been compared in order to validate the proposed methodology. The implementation of the NSGA-II in the design process resulted in optimized bucket geometrical parameters: bucket length, width, inlet angle, and outlet angle. These parameters not only resulted in a 2.56% increase in hydraulic efficiency, but also led to a 0.1 [kPa] reduction in the maximum pressure at the bottom of the bucket. Further research will involve testing these parameters using 3D printing methods to validate their effectiveness.