Browsing by Author "Nava, V"
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Item NAUTILUS-DTU10 MW Floating Offshore Wind Turbine at Gulf of Maine: Public numerical models of an actively ballasted semisubmersible: Public numerical models of an actively ballasted semisubmersible(2018-10-10) Galván, J; Sánchez-Lara, M J; Mendikoa, I; Pérez-Morán, G; Nava, V; Rodríguez-Arias, R; RENOVABLES OFFSHORE; Tecnalia Research & InnovationThis study presents two numerical multiphysics models of the NAUTILUS-10 floating support structure mounting the DTU10 MW Reference Wind Turbine at Gulf of Maine site, and analyses its dynamics. With the site conditions and the FAST model of the onshore turbine as the starting point, the floating support structure: tower, floating substructure with its corresponding active ballast system and station keeping system, was designed by NAUTILUS. The numerical models were developed and the onshore DTU wind energy controller was tuned to avoid the resonance of the operating FOWT by TECNALIA, in the framework of H2020 LIFES50+ project. This concept and its subsystems are fully characterised throughout this paper and implemented in opensource code, FAST v8.16. Here, the mooring dynamics are solved using MoorDyn, and the hydrodynamic properties are computed using HydroDyn. Viscous effects, not captured by radiation-diffraction theory, are modelled using two different approaches: (1) through linear and quadratic additional hydrodynamic damping matrices and (2) by means of Morison elements. A set of simulations (such as, decay, wind only and broadband irregular waves tests) were carried out with system identification purposes and to analyse the differences between the two models presented. Then, a set of simulations in stochastic wind and waves were carried out to characterise the global response of the FOWT.Item Predictive Maintenance of Floating Offshore Wind Turbine Mooring Lines using Deep Neural Networks(2022-05-13) Gorostidi, N; Nava, V; Aristondo, A; Pardo, D; RENOVABLES OFFSHOREThe recent massive deployment of onshore wind farms has caused controversy to arise mainly around the issues of land occupation, noise and visual pollution and impact on wildlife. Fixed offshore turbines, albeit beneficial in those aspects, become economically unfeasible when installed far away from coastlines. The possibility of installing floating offshore wind turbines is currently hindered by their excessive operation and maintenance costs. We have developed a comprehensive model to help companies plan their operations in advance by detecting failure in mooring lines in almost real time using supervised deep learning techniques. Given the lack of real data, we have coupled numerical methods and OpenFAST simulations to build a dataset containing the displacements and rotations of a turbine's floating platform across all directions. These time series and their corresponding frequency spectra are used to obtain a set of key statistical parameters, including means and standard deviations, peak frequencies, and several relevant momenta. We have designed and trained a Deep Neural Network to understand and distinguish amongst a series of common failure modes for mooring lines considering a range of metocean and structural conditions. We have obtained promising results when monitoring severe changes in the line's mass and damping using short time spans, achieving a 95.7% validation accuracy when detecting severe biofouling failure.