Tesi di Dottorato

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Optimal design and numerical modelling of imperfection sensitive shell structures
(Università della Calabria, 2020-02-24) Liguori, Francesco Salvatore; Garcea, Giovanni; Bartolino, Roberto
A brand-new design philosophy tends to harness the load-carrying capacity hidden beyond the onset of buckling phenomena in shell structures. However, when designing in the postbuckling range, among other effects, attention should be given at imperfection sensitivity which may generate catastrophic and unexpected consequences on the optimised structures. Therefore, what would be necessary is an optimisation strategy able to deal with the complex geometries of full-scale structures and, meanwhile, efficiently gather the complexity of their postbuckling response. The aim of this work is to meet this demand by proposing numerical methods that face the problem from different sides, namely the geometrically nonlinear description of the shell, the solution algorithm and the optimisation strategy. As a starting point, a convenient format to describe geometrically nonlinear shell structures is identified in the solid-shell model. On the basis of this model, a discretised environment is constructed using isogeometric analysis (IGA) that, by taking advantage from the high continuity of the interpolation functions, leads to a reduced number of variables with respect to standard finite elements. Afterwards, an IGA-based multimodal Koiter’s method is proposed to solve the geometrically nonlinear problem. This method meets the aforementioned requirements of efficiency, accuracy and is capable of providing information on the worst-case imperfection with no extra computational cost with respect to the analysis of a perfect structure. Additionally, a new strategy for improving the accuracy of the standard version of Koiter’s algorithm in the presence of geometrical imperfections is devised. The last part of the thesis concerns the optimal design of full-scale structures undergoing buckling phenomena. In particular, the design focuses on variable angle tow laminates, namely multi-layered composites in which fibre tows can describe curvilinear paths, thereby providing great stiffness-tailoring capacity. Two optimisation strategies are proposed, both based on the use of Koiter’s method to evaluate the postbuckling response. The first one makes use of a fibre path parameterisation and stochastic Monte Carlo random search as a global optimiser. The second one is based on direct stiffness modelling using lamination parameters as intermediate optimisation variables that lead to a reduction of the nonlinearity of the optimisation problem and remove the direct dependence from the number of layers.
Distributed Optimization Over Large-Scale Systems for Big Data Analytics
(Università della Calabria, 2020-01-17) Shahbazian, Reza; Leone, Nicola; Grandinetti, Lucio; Guerriero, Francesca
A large-scale system is defined as one that supports multiple, simultaneous users who access the core functionality through some network. Nowadays, enormous amount of data is continually generated at unprecedented and ever-increasing scales. Large-scale data sets are collected and studied in numerous domains, from engineering sciences to social networks, commerce, bimolecular research, and security. Big Data is a term applied to data sets whose size or type is beyond the ability of traditional relational databases to capture, manage, and process with acceptable latency. Usually, Big Data has one or more of the characteristics including high volume, high velocity, or high variety. Big Data challenges include capturing data, data storage, data analysis, search, sharing, transfer, visualization, querying, updating, information privacy and data source. Generally, Big Data comes from sensors, devices, video or audio, networks, log files, transactional applications, web, and social media, in a very large-scale. Big Data is impossible to analyze by using traditional central methods and therefore, new distributed models and algorithms are needed to process the data. In this thesis, we focus on optimization algorithms for Big Data application. We review some of the recent machine learning, convex and non-convex, heuristic and stochastic optimization techniques and available tools applied to Big Data. We also propose a new distributed and decentralized stochastic algorithm for Big Data analytics. Our proposed algorithm is fully distributed to decide large-scale networks and data sets. The proposed method is scalable to any network configuration, is near real-time (in each iteration, a solution is provided although it might not be the optimum one) and more critical, robust to any missing data or communication failures. We evaluate the proposed method by a practical example and simulations on cognitive radio networks. Simulation results confirmed that the proposed method is efficient in terms of accuracy and robustness. We assume that the distributed data-sources should be capable of processing their data and communicate with neighbor sources to find the network objective as an optimal decision. Some challenges are introduced by new technologies such as 5G or high-speed wireless data transfer, including imperfect communications that damage the data. We propose an optimal algorithm that uses optimal weighting to combine the shared data coming from neighbors. This optimal weight improves the performance of the decision-making algorithm in terms of error and convergence rate. We evaluate the performance of the proposed algorithm mathematically and introduce the step-sized conditions that guaranteed the convergence of the proposed algorithm. We use computer simulations to evaluate the network error. We prove that in a network diagram with ten datasources, the network performance of the proposed algorithm outperforms some of the known optimal solutions such as Metropolis and adaptive combination. Keywords: Optimization, Big Data, Large-Scale, Distributed, Optimal Weight.
CORE: an intelligent trasportation systems in Calabria
(2017-02-22) Santoro, Francesco; Leone, Nicola; Laganà, Demetrio; Musmanno, Roberto

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