Aerotecnica Missili & Spazio最新文献

Autonomous navigation is becoming an increasing area of interest, especially in the space sector. This is evident in the development of planetary exploration rovers, which rely heavily on terrain traversability capabilities to explore extraterrestrial terrains. This technology assesses the ability of a rover to identify potential obstacles of a given terrain based on its physical characteristics and visual appearance. In this paper, we present an innovative architecture tailored for such analysis. Our approach combines stereo visual SLAM for trajectory reconstruction with supervised learning from labeled ground-truth data, specifically utilizing DeepLabv3+, for precise pixel labeling of terrain types. The segmented point cloud generated from stereo vision is then converted into an occupancy grid map, facilitating comprehensive terrain characterization. Implementing our method within the Robot Operating System (ROS) framework enables seamless integration with rover systems. The proposed system was deployed and tested on a prototype rover, successfully demonstrating its ability to map the surrounding environment and identify potential obstacles. Finally, the realized mapping is compared with satellite images, and the results prove the effectiveness of the studied algorithm to carry out terrain traversability analysis.

This work presents the longitudinal and transverse coupling vibrations of a deployable Telescopic Tubular Mast (TTM), a multi-stepped structure integrated into a spacecraft system, while considering the rigid-flexible coupling phenomenon. The model is derived using the principle of virtual work and discretized via the variable separation method. The von Kármán strain is employed to incorporate geometric nonlinear effects. Semi-analytical results for the shape functions and natural frequencies of the quasi-static multi-stepped boom are obtained using the extended transfer matrix method (ETMM). These natural frequencies are validated against results from Nastran, confirming the ETMM's accuracy. In addition, the model accounts for the continuously changing natural frequencies and shape functions during the deployment phase. Finally, the dynamic phenomena of the longitudinal and transverse displacements are analyzed at various deploying states, including locking and restart behaviors. The influence of the structural damping on the vibration evolution is also contained in the numerical analysis.

The overall heating of satellites operating in low Earth orbit, essentially due to direct radiation from the Sun, terrestrial albedo, and planetary radiation, is well studied, but little is found specifically on transparent plates used for windowing spacecrafts. The most historic material of choice is fused silica; more recently, acrylic glass is being used, but its refractive properties are poorly documented. The effects of electromagnetic waves incident on multilayer windows composed of panes of fused silica or acrylic glass, or a combination of these, are analyzed. Using data of refractive index from the literature, which, however, have been found incomplete and sometimes contradictory, the problem is addressed by accounting for the dependence of transmissivity and absorptivity of each material on the energy spectrum, which is different for solar and planetary radiation. Worked examples show that fused silica allows most radiation to pass directly through, while acrylic glass is characterized by absorption depending on the thickness; this can potentially increase its temperature, posing a problem since the mechanical properties decay at temperatures near (100 ^{circ }textrm{C}). This study contributes to the analysis of thermal problems for space windows, of considerable interest since glazing can fail due to thermal shocks, constrained thermal variations, or temperature concentrations.

This paper presents a numerical approach to assess the influence of process parameters on a composite part’s mechanical properties. A one-dimensional thermochemical model is used to predict the curing progress of the resin during the curing cycle. Material properties are derived using the cure hardening instantaneous linear elastic model, and a refined one-dimensional model derived within the Carrera Unified Formulation framework is used to obtain accurate results concerning process-induced stresses. Various process parameters, such as the holding temperature and heating rate, are evaluated. The results show that some process parameters, such as the fiber volume fraction and holding temperature, significantly influence composite characteristics and process-induced stresses. It is also shown that modifications to curing cycles leading to reduced energy overhead may not affect performances.

Gradient-based optimization techniques need precise and efficient sensitivities. Numerical sensitivity methods such as finite differences are easy to implement but imprecise and computationally inefficient. In contrast, analytical sensitivity methods, such as the discrete and continuum ones are highly accurate and efficient. Continuum Sensitivity Analysis (CSA) is an analytical method used to calculate derivatives of shape or value parameters. While CSA has been successfully applied in static analysis and dynamic problems in the time domain, this work presents an extension of the approach to eigenvalue sensitivities for the first time. However, CSA revealed limitations, prompting the exploration of an alternative approach based on discrete analytical differentiation. This method is employed for the first time in shape sensitivities. The derivatives of the stiffness and mass matrices required by the method are calculated analytically, resulting in high accuracy and computational efficiency. In addition, an element agnostic approach has been developed leveraging primary analysis matrices to calculate their derivative. This characteristic, along with the nonintrusivity, makes the method employable with standard commercial software. Both approaches have been applied and validated in a wide range of scenarios, involving vibration and buckling problems.

The reduction of components and the development of more powerful satellites with smaller dimensions have allowed an increasing number of users, including universities, research centers, and space agencies in developing countries, to have access to technologies and/or equipment that would have been impossible a few decades ago, paving the way for the development of low-cost space missions. However, the space drive in developing countries includes not only the acquisition of small satellite components, or the construction of CubeSats, but also the creation of spaceports, from which agencies could launch their own delivery vehicles. The rapid worldwide increase of CubeSats, and the long waiting periods between launches of the main payloads, prompted the present investigation, where the main factors to consider in the selection of a spaceport location were stipulated. Therefore, to meet the objective of studying the various factors, a series of points to be analyzed were established: low latitudes, azimuth limitations, natural factors, political and social considerations, airspace, accessibility, and political stability. After analyzing the factors, we proceeded to the analysis of various locations in Venezuela for small space vector missions, where starting from three possible locations and after analyzing each one, we obtained as a result “El Pao”, a location in the Venezuelan plains, far from urban centers, with good communication routes, latitude of only 9° and few azimuth limitations. However, the factors analyzed in this research are not exclusively framed in the Venezuelan space effort as they are common to any country wishing to establish a spaceport in its territory.

In recent years, machine learning algorithms have experienced rapid advancement, driven by the exponential growth of data availability and computational capabilities. Among these algorithms, artificial neural networks stand out as one of the most renowned and effective classes, possessing the ability to discern relationships within data. In this study, we harness neural networks to deduce the relationship between flight mechanics parameters and resulting loads in an articulated rotor configuration. The accuracy of these algorithms hinges closely on the quality of the dataset used for training. Given that rotor loads manifest as time-periodic signals with precise harmonic content, we train dedicated neural networks to predict each harmonic individually. Subsequently, the load time history is reconstructed post hoc by amalgamating predictions from each individual network. Various network architectures are explored, and a sensitivity analysis is conducted on hyper-parameters to determine the optimal configuration for this specific application. Moreover, these predictions serve as input for a fatigue damage calculation algorithm.

Structured fabrics are made by interwoven rigid elements that form flexible garments such as chain mail armours. Traditionally, the mechanical properties of these materials were considered fixed. However, recent research has revealed their mechanical properties may be varied. Notably, studies have demonstrated that applying vacuum pressure between layers of 3D-printed chain mails enclosed in a bag induces particle and layer jamming of the elements, thereby affecting the material’s bending modulus. This arises from both compressive frictional forces and the complex geometrical interlocking of the rigid elements. This paper presents a comprehensive set of experimental tests for the characterisation of the stiffness and damping properties with respect to the type and number of fabrics and with respect to the vacuum pressure. More specifically, the study examines experimentally the changes in static properties in response to vacuum pressure changes evaluated on a four- and six-point bending setups. The outcome of this measurement campaign is then reported into the Ashby diagrams to compare the mechanical properties of the in-vacuum structured fabrics with those of classical materials. The potential applications of this research include the development of lightweight adaptive and semi-active vibration mitigation devices.