Browsing by Keyword "Offshore structures"
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Item Condition monitoring of mooring systems for Floating Offshore Wind Turbines using Convolutional Neural Network framework coupled with Autoregressive coefficients(2024-06-15) Sharma, Smriti; Nava, Vincenzo; RENOVABLES OFFSHOREThis research presents a novel approach proposed for the monitoring of mooring systems in Floating Offshore Wind Turbines (FOWTs), employing a combination of Convolutional Neural Networks (CNNs) and Auto-Regressive (AR) models. CNN finds broad application in monitoring intricate structures, as they adeptly handle noisy response data without necessitating profound domain expertise. The precision of CNNs relies on the extraction of meaningful features from input data, necessitating meticulous data curation and labeling for optimal computational efficiency and accurate estimation. Emphasis is placed on the preference for feature-rich small datasets over voluminous yet sparse datasets, aiming to enable CNNs to discern crucial patterns more effectively and mitigate issues such as overfitting and extensive preprocessing. The novelty of the proposed approach lies in the integration of AR models, which serve to compress data and enhance damage-sensitive characteristics in the input for CNNs. This integration involves deploying regression models fitted to historical responses, parameterized with AR coefficients sensitive to damage, and further classifying severity using CNNs. The sequential nature of this approach addresses challenges such as vanishing/exploding gradients, particularly for extended historical data, while also attenuating the impact of noise and irrelevant information through data compression. The study explores the effectiveness of the coupled AR-CNN method in monitoring FOWT mooring lines, with a specific focus on two levels of damage identification: detection with classification and damage severity across diverse damage and operational scenarios. The modified methodology exhibits superior outcomes by conducting a performance analysis against traditional CNNs and other machine-learning methods, highlighting the potential of the AR-CNN strategy to improve the precision of FOWT mooring line condition monitoring. These findings underscore the AR-CNN strategy's potential to enhance the accuracy of FOWT mooring line condition monitoring.Item On intermediate-scale open-sea experiments on floating offshore structures: Feasibility and application on a spar support for offshore wind turbines(2018-09) Ruzzo, Carlo; Fiamma, Vincenzo; Collu, Maurizio; Failla, Giuseppe; Nava, Vincenzo; Arena, Felice; RENOVABLES OFFSHOREExperimental investigation of floating structures represents the most direct way for achieving their dynamic identification and it is particularly valuable for relatively new concepts, such as floating supports for offshore wind turbines, in order to fully understand their dynamic behaviour. Traditional experimental campaigns on floating structures are carried out at small scale, in indoor laboratories, equipped with wave and wind generation facilities. This article presents the results of an open-sea experimental activity on a 1:30 scale model of the OC3-Hywind spar, in parked rotor conditions, carried out at the Natural Ocean Engineering Laboratory (NOEL) of Reggio Calabria (Italy). The aim of the experiment is two-fold. Firstly, it aims to assess the feasibility of low-cost, intermediate-scale, open-sea activities on offshore structures, which are proposed to substitute or complement the traditional indoor activities in ocean basins. Secondly, it provides useful experimental data on damping properties of spar support structures for offshore wind turbines, with respect to heave, roll and pitch degrees of freedom. It is proven that the proposed approach may overcome some limitations of traditional small-scale activities, namely high costs and small scale, and allows to enhance the fidelity of the experimental data currently available in literature for spar floating supports for offshore wind turbines.