Altran (Italy)

Computational fluid dynamics is employed to evaluate the mean aerodynamic loading on the retractable landing-gears of a regional transport commercial aircraft. The mean turbulent flow around simplified landing-gear systems including doors is simulated by using the Reynolds-averaged Navier–Stokes approach, where the governing equations are solved with a finite volume-based numerical method. Using a dynamic meshing method, the computational grid is automatically and continuously adapted to the time-changing geometry, while following the extension/retraction of the landing-gear systems. The temporal evolution of the aerodynamic forces on both the nose and the main landing-gears, along with the hinge moments of the doors, is numerically predicted. The proposed computational modeling approach is verified to have good practical potential when compared with reference experimental data provided by the Leonardo Aircraft structural loads group.

In this paper, a unified control framework is proposed to realize a robotic ball catching task with only a moving single-camera (eye-in-hand) system able to catch flying, rolling, and bouncing balls in the same formalism. The thrown ball is visually tracked through a circle detection algorithm. Once the ball is recognized, the camera is forced to follow a baseline in the space so as to acquire an initial dataset of visual measurements. A first estimate of the catching point is initially provided through a linear algorithm. Then, additional visual measurements are acquired to constantly refine the current estimate by exploiting a nonlinear optimization algorithm and a more accurate ballistic model. A classic partitioned visual servoing approach is employed to control the translational and rotational components of the camera differently. Experimental results performed on an industrial robotic system prove the effectiveness of the presented solution. A motion-capture system is employed to validate the proposed estimation process via ground truth.

This paper deals with convergence analysis of the extended Kalman filters (EKFs) for sensorless motion control systems with induction motor (IM). An EKF is tuned according to a six-order discrete-time model of the IM, affected by system and measurement noises, obtained by applying a first-order Euler discretization to a six-order continuous-time model. Some properties of the discrete-time model have been explored. Among these properties, the observability property is relevant, which leads to conditions that can be directly linked with the working conditions of the machine. Starting from these properties, the convergence of the stochastic state estimation process, in mean square sense, has been shown. The convergence is also explored with reference to the difference between the samples of the state of the continuous-time model and that estimated by the EKF. The results theoretically achieved have been also validated by means of experimental tests carried out on an IM prototype.

Conventional elastic optical networking, EON, uses elasticity in two domains, time and frequency, to optimize utilization of optical network resources in the presence of fluctuating traffic demand and link quality. Currently, networking exploiting a third domain, space, is the focus of significant research efforts since space-division multiplexing, SDM, has the potential to substantially improve future network capacity and spectral efficiency. This article extends 2D-EON to include elasticity in all three domains: time, frequency, and space. We introduce enabling technologies, architectures, and algorithms for 3D-EONs. Based on sample network topologies, we investigate algorithms for routing, spectrum, spatial mode, and modulation format assignment — RSSMA. In particular, we investigate fragmentation-aware RSSMA and how the constraints in the formation of super-channels in MIMO-based SDM systems can impact the network performance in terms of blocking probability.

Critical Infrastructure Systems (CIS) are essential services to sustain both society and economy. In fact, CIS can be considered as vital systems for a geographic area or a country. Such valuable assets have to be carefully monitored because their partial or complete failure (caused by natural hazards or criminal acts) could produce severe costs in terms of environment, economy and, in the worst scenario, human lives. The need to protect and maintain CIS and the surrounding environment is pushing the research for the development of intelligent monitoring systems, able to detect anomalies and events and to adapt autonomously to the changes in the system under investigation. In this chapter, we describe an intelligent hardware-software architecture for CIS monitoring, specifically designed for asynchronous events detection, remote configurability and diagnosis. In particular, this monitoring system is based on a novel hybrid architecture, in which different sensors, architectures and physical phenomena under monitoring coexist and cooperate to provide different views of the same physical phenomenon. In fact, the proposed monitoring system is able to gather both high frequency signals (microscopic level), such as accelerometer signals, and low-dynamic signals (macroscopic level), such as temperature and inclination. The monitoring system is connected to a remote data center, which collects, interprets and forwards them to the stakeholders in the desired format. The design principles driving the monitoring system are introduced. As a practical application will be shown a CIS monitoring system employed to monitor the Rialba’s tower, a rock tower-like limestone complex overlooking an area of strategic importance connecting the Lecco and Como provinces in north Italy. The rock tower is indeed exposed to a rock toppling risk, thus menacing an area characterized by the presence of a freeway, a railway line and gas and power distribution pipelines.

qualication standards for the simulator, a series of features are added to the baseline model so as to deal with all ight conditions possibly encountered during vehicle operation. These additional features include engine model and governor, a simple undercarriage and ground{eect model, the representation of vortex ring state and icing phenomena. The architecture of both hardware and software for the simulator is presented and discussed.

The raw output of a generic infrared vision system, based on staring arrays, is spatially not uniform. This spatial noise can be much greater than the system NETD, and determines a strong drop in system performance. Therefore we need to model all system non-uniformity (NU) sources to highlight the parameters that should be controlled by optical and mechanical design, the ones depending on the focal plane array and those that can be corrected in post-processing. In this paper, we identify the main NU sources (optical relative irradiance, housing straylight, detector pixel-pixel differences and non linearity), we show how to model these sources and how they are related to the design and physical parameters of the system. We then describe the total signal due to these sources at the detector output. Applying different NUC algorithms to this signal, the final results on the image can be simulated finding a proper correction algorithm. At the end we show the agreement between the model with the experimental data taken on a real system. Changing a limited set of parameters, this model can be applied to many third generation thermal imager configurations.