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Digital Models for floating offshore wind

Authors: Ciuriuc, Alexandra; Pozo-Gonzalez, Hector Del; Rapha, Jose Ignacio; Domínguez-García, Jose Luis; Paradell, Pol; Yalçin, Tolga; Vaughan, David; +4 Authors

Digital Models for floating offshore wind

Abstract

Despite having a great potential, the current costs of floating offshore wind prevent it from being deployed massively at a global scale. This situation is driving many research works to improve the floating wind feasibility and speed up its implantation. One of the newest fields of innovation is the digitalisation of assets, which applied to the floating wind farms can lead to a reduction of their LCOE. The reasons include the complexity of the floating wind projects, the large investments required, the size of the components, the environments where the farms are installed and the inherent accessibility and workability issues. Other technologies have been digitalised before the floating wind industry, therefore there is already experience in the industry and general guidelines can be extracted. A key stage where the digitalisation can lead to an LCOE reduction is the maintenance, and the first step is determining the typical failures that occur in a floating wind farm. As the floating wind technology is still advancing on its first steps, there is a noticeable lack of failure rates of floating wind components and some of them have been extracted from the bottom-fixed technology or the oil and gas platforms. An inverse relationship between failure rates and repair times is observed and fatigue and corrosion are identified two critical failure causes.

Keywords

Machine Learning, Digital Twin, Floating Wind, Digitalitzation, BIM, Wind Farm control

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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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