Sprinkler irrigation is widely used in agriculture because it allows for rational use of water. However, it can induce negative effects of soil erosion and of surface waterproofing. The scholars of these phenomena use the numerical integration of the equation of motion, but if there was an analytical solution, the study would be facilitated, and this solution could be used as software for regulating sprinklers. Therefore, in this study, the solution of the differential equation of the flight of droplets produced by sprinklers in the absence of wind was developed. The impossibility of an exact analytical solution to the ballistic problem due to the variability of the drag coefficient of the droplets is known; therefore, to find the integrals in closed form, the following were adopted: a new formula for the drag coefficient; a projection of the dynamic’s equation onto two local axes, one tangent and one normal to the trajectory and some linearization. To reduce the errors caused by the latter, the linearization coefficients and their calculation formulas were introduced through multiple non-linear regressions with respect to the jet angle and the initial droplet speed. The analytical modeling obtained, valid for jet angles from 10° to 40°, was compared to the exact numerical solution, showing, for the total travel distance, a high accuracy with a mean relative error MRE of 1.8% ± 1.4%. Even the comparison with the experimental data showed high accuracy with an MRE of 2.5% ±1.1%. These results make the analytical modeling capable of reliably calculating the travel distance, the flight time, the maximum trajectory height, the final fall angle and the ground impact speed. Since the proposed analytical modeling uses only elementary functions, it can be implemented in PLC programmable logic controllers, which could be useful for controlling water waste and erosive effects on the soil during sprinkler irrigation.

Diminishing fossil fuels, the continually increasing demand for energy due to rapid urbanization, pollution caused by the increased generation of electricity using fossil fuels, the consequent environmental effect, and concerns about man-made global warming have prompted a call for renewable energy solutions [...]

The research described in this paper focuses on the development of an innovative unmanned aerial vehicle (UAV) tailored for a specific mission: detecting the acoustic signature emitted by chainsaws, identifying deforestation, and reporting its location for legality assessment. Various calculations were conducted to determine the optimal solution, resulting in the choice of a fixed-wing UAV. A comparative analysis between tri-rotor and quadcopter systems was performed, leading to the selection of the tri-rotor configuration. The primary objective of this study is to design an innovative hybrid UAV concept with key features including a fixed-wing design and integrated VTOL (vertical takeoff and landing) capability in the experimental model. The aircraft has been constructed using advanced materials such as fiber-reinforced polymer composites, manufactured using both conventional and advanced techniques like continuous fiber additive manufacturing and the use of a polymer matrix. Additionally, the aerodynamic configuration is optimized to achieve a cruise speed of approximately 50 km/h and a flight autonomy exceeding 3 h. The UAV has been equipped with payloads for mounting sensors to collect meteorological data, and crucially, the VTOL system has been optimized to vectorize thrust for improved performance during the transition from hover to cruise flight. This paper details the entire manufacturing and assembly process of the drone, covering both the structural framework and associated electrical installations. A dedicated sound detection system is incorporated into the drone to identify chainsaw noise, with the aim of preventing deforestation.

Convolutional neural networks (CNN) have been widely adopted in fluid dynamics investigations over the past few years due to their ability to extract and process fluid flow field characteristics. Both in sparse-grid simulations and sensor-based experimental data, the establishment of a dense flow field that embeds all spatial and temporal flow information is an open question, especially in the case of turbulent flows. In this paper, a deep learning (DL) method based on computational CNN layers is presented, focusing on reconstructing turbulent open channel flow fields of various resolutions. Starting from couples of images with low/high resolution, we train our DL model to efficiently reconstruct the velocity field of consecutive low-resolution data, which comes from a sparse-grid Direct Numerical Simulation (DNS), and focus on obtaining the accuracy of a respective dense-grid DNS. The reconstruction is assessed on the peak signal-to-noise ratio (PSNR), which is found to be high even in cases where the ground truth input is scaled down to 25 times.

This paper focuses on both voltage transients and short-duration RMS variations, and presents a unique and heterogeneous approach to their assessment by applying AI tools. The database consists of both real (obtained from Lithuanian PQ monitoring campaigns) and synthetic data (obtained from the simulation and literature review). Firstly, this paper investigates the fundamental grid component and its harmonics filtering with an IIR shelving filter. Secondly, in a key part, both SVM and KNN are used to classify PQ events by their primary cause in the voltage–duration plane as well as by the type of short circuit in the three-dimensional voltage space. Thirdly, since it seemed to be difficult to interpret the results in the three-dimensional space, the new method, based on Clarke transformation, is developed to convert it to two-dimensional space. The method shows an outstanding performance by avoiding the loss of important information. In addition, a geometric analysis of the fault voltage in both two-dimensional and three-dimensional spaces revealed certain geometric patterns that are undoubtedly important for PQ classification. Finally, based on the results of a PQ monitoring campaign in the Lithuanian distribution grid, this paper presents a unique discussion regarding PQ assessment gaps that need to be solved in anticipation of a great leap forward and refers them to PQ legislation.

The aim of the Special Issue on Automatic Control and System Theory and Advanced Applications, the second volume of a previous paper selection, is to emphasize the role of new inventions in the area of automatic control applications [...]

This paper presents a parametric study of the multistorey hydro-powered pump, known as ‘Bunyip’, which has demonstrated significant potential in contributing to rural regions. The study is aimed at understanding the underlying physics of the system and ways to enhance its hydraulic performance. A transient three-dimensional model using the commercial Computational Fluid Dynamics (CFD) tool Ansys-Fluent is utilized to gain insights into its fundamental flow mechanics, operational efficiency, standard capacity, and relative delivery. The investigation involves an initial assessment of performance for three Bunyip devices based on manufacturing data. A parametric analysis is conducted for the dataset generated through meticulous application and numerical modelling. The CFD results are validated against experimental data. Three main design configurations are considered, and 58 sets of varied input parameters are examined. The best design configuration is evaluated against five cases of conventional hydro-power pump systems. The results indicate that a smaller diameter of the pressure chamber and a higher supply head lead to higher pressure, achieving a target head of 3 m with 15% efficiency and a flowrate of 11.82 L/min.

This study presents the manufacturing process-driven development of an interlocking metasurface; (ILM) mechanism for fused filament fabrication; (FFF) with a focus on open-source accessibility. The presented ILM is designed to enable strong contact between two planar surfaces. The mechanism consists of spring elements and locking pins which snap together when forced into contact. The mechanism is designed to deliver optimized mechanical properties, functionality, and printability with common FFF printers. The mechanism is printed from a thermoplastic polyurethane; (TPU) filament which was selected for its flexibility, which is necessary for the proper functioning of the spring elements. To characterize the designed mechanism, a tensile test is carried out to assess the holding force of the ILM. The force-displacement profiles are analyzed and categorized into distinct phases, highlighting the interplay between spring deformation, sliding, and disengagement. Finally, from the measurements of multiple printed specimens, a representative holding force is determined through averaging and assigned to the mechanism. The resulting tolerance, which can be attributed to geometric and material-related factors, is discussed. The testing results are discussed and compared with a numerical simulation carried out with a frictionless approach with a nonlinear Neo-Hookean material law. The study underscores the importance of meticulous parameter control in three-dimensional (3D) printing for the consistent and reliable performance of interlocking metasurface mechanisms. The investigation leads to a scalable model of an ILM element pair with distinct three-phase snapping characteristics ensuring reliable holding capabilities.

Transport phenomena draw from the fields of continuum mechanics and thermodynamics with diverse industrial applications [...]

To better consume high-density photovoltaics, in this article, the application of energy storage devices in the distribution network not only realizes the peak shaving and valley filling of the electricity load but also relieves the pressure on the grid voltage generated by the distributed photovoltaic access. At the same time, photovoltaic power generation and energy storage cooperate and have an impact on the tidal distribution of the distribution network. Since photovoltaic output has uncertainty, the maximum photovoltaic output in each scenario is determined by the clustering algorithm, while the storage scheduling strategy is reasonably selected so the distribution network operates efficiently and stably. The tidal optimization of the distribution network is carried out with the objectives of minimizing network losses and voltage deviations, two objectives that are assigned comprehensive weights, and the optimization model is constructed by using a particle swarm algorithm to derive the optimal dispatching strategy of the distribution network with the cooperation of photovoltaic and energy storage. Finally, a model with 30 buses is simulated and the system is optimally dispatched under multiple scenarios to demonstrate the necessity of conducting coordinated optimal dispatch of photovoltaics and energy storage.