Mechanics of the scouring and sinking of submerged structures in a mobile bed: A physical model study

Khan-Mozahedy, A.B.M.; Muñoz-Pérez, Juan J.; Neves, M. G.; Sancho, Francisco; Cavique, Rita

doi:10.1016/j.coastaleng.2016.01.002

Cited by 7 publications

(1 citation statement)

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“…As previously stated, some authors utilized geospatial datasets in geographic information systems (GISs) with the aim of implementing data-driven decision-making methods in the management or in the planning phases. Through an intensive review, it was detected that many of the published studies relate to the analysis of bridges, pipelines or beach breakwaters [42]. Only a few studies have been applied to offshore wind farms.…”

Section: Geospatial Analysis Of Data In Offshore Wind Farmsmentioning

confidence: 99%

Geospatial Analysis of Scour in Offshore Wind Farms

et al. 2023

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Climate change has highlighted the need to promote renewable energies. The offshore wind industry is growing exponentially because of some political strategies supported by various organizations, such as the European Union. The implementation of these strategies is commonly associated with significant investments, public acceptance, or achieving better installations and greater cumulative capacities. To ensure that offshore renewable energy projects could reach their ambitious targets, this study promotes the implementation of political strategies or planning decisions using data mining techniques and analytical tools. Strategic decisions based on real data analysis could help to achieve more suitable and optimal infrastructures. The scour phenomenon jeopardizes the operability of offshore wind farms, making it necessary to study its evolution over the years. In this work, extensive research on the scour phenomenon in offshore wind farms using real data (from the Lynn and Inner Dowsing offshore wind farms located in the UK) was performed, which revealed an evident lack of consideration of this phenomenon for data-driven decision-making processes. As a novelty, this research develops a detailed geospatial analysis of data, studying the possible autocorrelation of scour data measured from each turbine between 2011 and 2015. The conclusions obtained could be used to improve future planning tasks in offshore wind farms.

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Section: Geospatial Analysis Of Data In Offshore Wind Farmsmentioning

confidence: 99%