Browsing by Author "Fragonara, Luca Zanotti"
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Item Open Access Adaptive intelligent traffic control systems for improving traffic quality and congestion in smart cities(University of Montenegro, 2021-01-31) Ahmed, Aminah Hardwan; Fragonara, Luca ZanottiA systematic review was undertaken to examine the solutions available for traffic congestion and associated problems in smart cities. Google Scholar and Google were used as search engines, leading to the final selection of 35 eligible papers for inclusion in this review, after a serious of screening based on definite criteria. Intelligent transport systems were found to be the most suitable solution to traffic congestion and associated problems in smart cities. Certain models and frameworks of smart cities include smart mobility and transport management systems. These can be approximated to intelligent transport systems. True intelligent transport systems are infrastructure-based or intelligent vehicle based or more preferably, a combination of both. The Internet of Things and cloud computing should be built into the system as they enable the operation of smart transport networks. Some methods of designing traffic control systems combining both Eulerian and Lagrangian approaches have been discussed for the possibility of using any of them to design a new automatic traffic monitoring and control system for smart cities. The practical implication of this research is that it can improve quality of life of people by minimizing traffic congestion. Limitations of this paper include this being a systematic review, availability of very few papers and not considering adaptive intelligent traffic control systems. Explanations for these limitations have been providedItem Open Access Application of fibre optic sensing systems to measure rotor blade structural dynamics(Elsevier, 2021-03-09) Weber, Simone; Kissinger, Thomas; Chehura, Edmond; Staines, Stephen; Barrington, James; Mullaney, Kevin; Fragonara, Luca Zanotti; Petrunin, Ivan; James, Stephen; Lone, Mudassir; Tatam, Ralph P.This paper compares two fibre optic sensing techniques for vibration characterisation: (a) optical fibre Bragg grating (FBG) strain gauges and (b) a novel direct fibre optic shape sensing (DFOSS) approach based on differential interferometric strain measurements between multiple fibres within the same fibre arrangement. Operational mode shapes and frequency measurements of an Airbus Helicopters H135 bearingless main rotor blade (5.1 m radius) were acquired during a series of ground vibration tests undertaken in a controlled laboratory environment. Data recorded by the fibre optic instrumentation systems were validated using commercially available accelerometers and compared against a baseline finite element model. Both fibre optic sensing systems proved capable of identifying the natural frequencies of the blade in the frequency range of interest (0–100 Hz). The data from the FBG sensors exhibited a dependency on their position relative to the neutral axes of the blade, which meant that full characterisation of the flapping and lagging modes required careful consideration of sensor location in the chordwise direction. The DFOSS system was able to identify all structural dynamics, despite being located on the neutral axis in the lagging direction, due to its sensitivity to angle changes, rather than strain, and its biaxial measurement capability. The DFOSS system also allowed the operational mode shapes of the blade to be determined directly, without the requirement for strain transfer from the blade to the sensor and without the requirement for a model of the underlying structure. The accuracy of obtained natural frequencies and operational mode shapes is assessed, demonstrating the potential of the use of both fibre optic sensing systems for determining blade structural dynamics.Item Open Access Bladesense – a novel approach for measuring dynamic helicopter rotor blade deformation(European Rotorcraft Forum, 2018-12-31) Weber, Simone; Southgate, Dominic; Mullaney, Kevin; James, Stephen; Rutherford, Robert; Sharma, Anuj; Lone, Mudassir; Kissinger, Thomas; Chehura, Edmond; Staines, Stephen; Pekmezci, Huseyin; Fragonara, Luca Zanotti; Petrunin, Ivan; Williams, Dan; Moulitsas, Irene; Cooke, Alastair; Rosales, Waldo; Tatam, Ralph P.; Morrish, Peter; Fairhurst, Mark; Atack, Richard; Bailey, Gordon; Morley, StuartTechnologies that allow accurate measurement of rotorblade dynamics can impact almost all areas of the rotorcraft sector; ranging from maintenance all the way to blade design. The BladeSense project initiated in 2016 aims to take a step in developing and demonstrating such a capability using novel fibre optic sensors that allow direct shape measurement. In this article the authors summarise key project activities in modelling and simulation, instrumentation development and ground testing. The engineering approach and associated challenges and achievements in each of these disciplines are discussed albeit briefly. This ranges from the use of computational aerodynamics and structural modelling to predict blade dynamics to the development of direct fibre optic shape sensing that allows measurements above 1kHz over numerous positions on the blade. Moreover, the development of the prototype onboard system that overcomes the challenge of transferring data between the rotating main rotor to the fixed fuselage frames is also discussed.Item Open Access Fast, accurate, and reliable detection of damage in aircraft composites by advanced synergistic infrared thermography and phased array techniques(MDPI, 2021-03-19) Padiyar M, Janardhan; Fragonara, Luca Zanotti; Petrunin, Ivan; Raposo, João; Tsourdos, Antonios; Gray, Iain; Farmaki, Spyridoyla; Exarchos, Dimitrios; Matikas, Theodore E.; Dassios, Konstantinos G.This paper presents an advanced methodology for the detection of damage in aircraft composite materials based on the sensor fusion of two image-based non-destructive evaluation techniques. Both of the techniques, phased-array ultrasonics and infra-red thermography, are benchmarked on an aircraft-grade painted composite material skin panel with stringers. The sensors systems for carrying out the inspections have been developed and miniaturized for being integrated on a vortex-robotic platform inspector, in the framework of a larger research initiative, the Horizon-2020 ‘CompInnova’ project.Item Open Access GAPointNet: Graph attention based point neural network for exploiting local feature of point cloud(Elsevier, 2021-01-26) Chen, Can; Fragonara, Luca Zanotti; Tsourdos, AntoniosExploiting fine-grained semantic features on point cloud data is still challenging because of its irregular and sparse structure in a non-Euclidean space. In order to represent the local feature for each central point that is helpful towards better contextual learning, a max pooling operation is often used to highlight the most important feature in the local region. However, all other geometric local correlations between each central point and corresponding neighbourhood are ignored during the max pooling operation. To this end, the attention mechanism is promising in capturing node representation on graph-based data by attending over all the neighbouring nodes. In this paper, we propose a novel neural network for point cloud analysis, GAPointNet, which is able to learn local geometric representations by embedding graph attention mechanism within stacked Multi-Layer-Perceptron (MLP) layers. Specifically, we highlight different attention weights on the neighbourhood of each center point to efficiently exploit local features. We also combine attention features with local signature features generated by our attention pooling to fully extract local geometric structures and enhance the network robustness. The proposed GAPointNet architecture is tested on various benchmark datasets (i.e. ModelNet40, ShapeNet part, S3DIS, KITTI) and achieves state-of-the-art performance in both the shape classification and segmentation tasksItem Open Access Go wider: an efficient neural network for point cloud analysis via group convolutions(MDPI, 2020-04-01) Chen, Can; Fragonara, Luca Zanotti; Tsourdos, AntoniosIn order to achieve a better performance for point cloud analysis, many researchers apply deep neural networks using stacked Multi-Layer-Perceptron (MLP) convolutions over an irregular point cloud. However, applying these dense MLP convolutions over a large amount of points (e.g., autonomous driving application) leads to limitations due to the computation and memory capabilities. To achieve higher performances but decrease the computational complexity, we propose a deep-wide neural network, named ShufflePointNet, which can exploit fine-grained local features, but also reduce redundancies using group convolution and channel shuffle operation. Unlike conventional operations that directly apply MLPs on the high-dimensional features of a point cloud, our model goes “wider” by splitting features into groups with smaller depth in advance, having the respective MLP computations applied only to a single group, which can significantly reduce complexity and computation. At the same time, we allow communication between groups by shuffling the feature channel to capture fine-grained features. We further discuss the multi-branch method for wider neural networks being also beneficial to feature extraction for point clouds. We present extensive experiments for shape classification tasks on a ModelNet40 dataset and semantic segmentation task on large scale datasets ShapeNet part, S3DIS and KITTI. Finally, we carry out an ablation study and compare our model to other state-of-the-art algorithms to show its efficiency in terms of complexity and accuracyItem Open Access Improving depth resolution of ultrasonic phased array imaging to inspect aerospace composite structures(MDPI, 2020-02-20) Mohammadkhani, Reza; Fragonara, Luca Zanotti; Padiyar M, Janardhan; Petrunin, Ivan; Raposo, João; Tsourdos, Antonios; Gray, IainIn this paper, we present challenges and achievements in development and use of a compact ultrasonic Phased Array (PA) module with signal processing and imaging technology for autonomous non-destructive evaluation of composite aerospace structures. We analyse two different sets of ultrasonic scan data, acquired from 5 MHz and 10 MHz PA transducers. Although higher frequency transducers promise higher axial (depth) resolution in PA imaging, we face several signal processing challenges to detect defects in composite specimens at 10 MHz. One of the challenges is the presence of multiple echoes at the boundary of the composite layers called structural noise. Here, we propose a wavelet transform-based algorithm that is able to detect and characterize defects (depth, size, and shape in 3D plots). This algorithm uses a smart thresholding technique based on the extracted statistical mean and standard deviation of the structural noise. Finally, we use the proposed algorithm to detect and characterize defects in a standard calibration specimen and validate the results by comparing to the designed depth information.Item Open Access In-orbit system identification of a flexible satellite with variable mass using dual Unscented Kalman filters(Elsevier, 2025-01-01) Elliott, Alex J.; Gutierrez, Aydin Nakhaeezadeh; Felicetti, Leonard; Fragonara, Luca ZanottiModern space mission concepts are increasingly dependent on the robust and reliable deployment of spacecraft with large appendages, such as antennas, booms or solar panels. Such deployment requires the ability to properly capture and control the coupled system dynamics, which requires accurate in-orbit system identification of the mass and structural properties. This paper utilises dual Unscented Kalman filters (DUKF) to develop an online system identification strategy that captures both the structural and mass properties, and the attitude and orbit dynamics. The dynamics of the flexible multibody problem are derived from the Lagrangian equations, with the flexible body characteristics modelled with finite element software. A genetic algorithm is used to optimise the accelerometer placement, and hence improve the DUKF performance. We demonstrate that this approach can accurately capture the coupled attitude, orbit, and structural dynamics, as well as being able to provide in-orbit updates for mass properties such as the moment of inertia. The methodology is explored for two illustrative cases: one in which the initial moment of inertia is incorrectly characterised, one in which the moment of inertia changes with time. In both cases, the DUKF approach captures both the system dynamics and the mass properties, which are captured with an error of less than 1%.Item Open Access A multi‐objective genetic algorithm strategy for robust optimal sensor placement(Wiley, 2021-02-17) Civera, Marco; Pecorelli, Marica Leonarda; Ceravolo, Rosario; Surace, Cecilia; Fragonara, Luca ZanottiThe performance of a monitoring system for civil buildings and infrastructures or mechanical systems depends mainly on the position of the deployed sensors. At the current state, this arrangement is chosen through optimal sensor placement (OSP) techniques that consider only the initial conditions of the structure. The effects of the potential damage are usually completely neglected during its design. Consequently, this sensor pattern is not guaranteed to remain optimal during the whole lifetime of the structure, especially for complex masonry buildings in high seismic hazard zones. In this paper, a novel approach based on multi‐objective optimization (MO) and genetic algorithms (GAs) is proposed for a damage scenario driven OSP strategy. The aim is to improve the robustness of the sensor configuration for damage detection after a potentially catastrophic event. The performance of this strategy is tested on the case study of the bell tower of the Santa Maria and San Giovenale Cathedral in Fossano (Italy) and compared to other classic OSP strategies and a standard GA approach with a single objective function.Item Open Access Structural health monitoring through vibration-based approaches(Hindawi, 2019-02-17) Boscato, Giosuè; Fragonara, Luca Zanotti; Cecchi, Antonella; Reccia, Emanuele; Baraldi, DanieleItem Open Access Treed gaussian process for manufacturing imperfection identification of pultruded GFRP thin-walled profile(Elsevier, 2020-08-28) Civera, Marco; Boscato, Giosuè; Fragonara, Luca ZanottiThe process of manufacturing pultruded FRP (Fiber Reinforced Polymers) profiles involves unavoidable imperfections that affect their structural performances. This is is even more relevant for the stability of axially loaded slender elements, due to the importance of imperfections and notches to initiate the buckling phenomenon. Thus, they become a predominant factor for the design of lightweight FRP beam-like structures. A Bayesian approach is proposed to estimate the presence and location of manufacturing imperfections in pultruded GFRPs (Glass Fiber Reinforced Polymers) profiles. Specifically, the Treed Gaussian Process (TGP) procedure is applied. This approach combines regression Gaussian Processes (GP) and Bayesian-based Recursive Partitioning. The experimental and numerical modal shapes of wide flange pultruded profile were investigated. The experimental data were compared with the numerical results of several Finite Element Models (FEM) characterised by different crack sizesItem Open Access Using video processing for the full-field identification of backbone curves in case of large vibrations(MDPI, 2019-05-21) Civera, Marco; Fragonara, Luca Zanotti; Surace, CeciliaNonlinear modal analysis is a demanding yet imperative task to rigorously address real-life situations where the dynamics involved clearly exceed the limits of linear approximation. The specific case of geometric nonlinearities, where the effects induced by the second and higher-order terms in the strain–displacement relationship cannot be neglected, is of great significance for structural engineering in most of its fields of application—aerospace, civil construction, mechanical systems, and so on. However, this nonlinear behaviour is strongly affected by even small changes in stiffness or mass, e.g., by applying physically-attached sensors to the structure of interest. Indeed, the sensors placement introduces a certain amount of geometric hardening and mass variation, which becomes relevant for very flexible structures. The effects of mass loading, while highly recognised to be much larger in the nonlinear domain than in its linear counterpart, have seldom been explored experimentally. In this context, the aim of this paper is to perform a noncontact, full-field nonlinear investigation of the very light and very flexible XB-1 air wing prototype aluminum spar, applying the well-known resonance decay method. Video processing in general, and a high-speed, optical target tracking technique in particular, are proposed for this purpose; the methodology can be easily extended to any slender beam-like or plate-like element. Obtained results have been used to describe the first nonlinear normal mode of the spar in both unloaded and sensors-loaded conditions by means of their respective backbone curves. Noticeable changes were encountered between the two conditions when the structure undergoes large-amplitude flexural vibrations.