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Enregistrement W6912885123 · doi:10.5281/zenodo.7147604

Mooring systems integrity management technologies

2020· article· en· W6912885123 sur OpenAlex

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Notice bibliographique

RevueZenodo (CERN European Organization for Nuclear Research) · 2020
Typearticle
Langueen
DomaineComputer Science
ThématiqueEnvironmental Engineering and Cultural Studies
Établissements canadiensIntecsea (Canada)
Organismes subventionnairesHorizon 2020 Framework Programme
Mots-clésMooringIntegrity managementTurbineDowntimeLeverage (statistics)Structural integrityOperating expenseHull

Résumé

récupéré en direct d'OpenAlex

The MooringSense project aims to reducing OPEX and increasing efficiency of FOWT through the development of efficient risk-based integrity management strategies for mooring systems based on a cost effective and reliable on-line monitoring technology and digital twin. This deliverable provides an overview of the state-of-the-art of current technologies, tools and techniques related to integrity management of mooring systems currently applied to the O&G industry, from the perspective of FOW, with the aim of identifying the technological gaps applied to the integrity management of mooring systems. In the first instance, it shall be highlighted that the purpose of a mooring system in both O&G and FOW is station keeping, namely to keep a floating structure within reasonable proximity of a designated location and to avoid excessive movement that will hinder safe operation. In the O&G industry, mooring systems have been utilized for many years and there is a level of understanding of vessel motion and wave interaction that is significantly higher compared to the concepts employed by the floating offshore renewables sector. Although this can be seen as a weakness, the FOW industry can leverage the O&G experience to ensure that reliable and cost-effective solutions are employed. However, a key difference when comparing a mooring system for a traditional O&G installation (i.e. a semisubmersible or an FPSO) and a FOW farm is in the number of mooring lines. A traditional O&G installation will generally comprise of a limited number of mooring lines (i.e.10-30); however, for a medium size FOW of 50 FOWT, each turbine will have 3 to 6 mooring with a total number in the range of 150-300. Given the high cost of offshore operations (inspection, maintenance, repairs) it is of vital importance that a cost-effective integrity management strategy is implemented to keep OPEX at an acceptable level. This deliverable details the key aspect related to integrity management of mooring system including degradation mechanism in chain, wire ropes and synthetic ropes; inspection and integrity management techniques; failure detection, line tension monitoring, control algorithms and digital twin. In addition, the deliverables highlight some technological gaps, which are summarised below: With regards to international standards and guidelines, there is a clear need for tailored documentation focusing on the challenges of FOW and, in particular, there is the need of a tailored risk-based approach that can be applied to the FOW industry to enhance the effective operation of the floating structures whilst reducing costs. SHM for monitoring the integrity of FOW substructures is in the development phase. First systems are available on the market, but there are reasonable doubts regarding their reliability and robustness, and local inspection remains necessary for making decisions on O&M. Effective mooring line failure detection systems are still required. Work on the exploitation of novel sensors such as those proposed in this project is not abundant, for obvious reasons. More generally, the relationship between turbine control, platform position and mooring line loads requires careful study. Several monitoring technologies are available today to provide mooring line tension measurements in floating platform as a source of information for integrity assessment and management. However, these technologies present several issues related to robustness and reliability, as well as costs if they are to be applied to FOW, where long term operation and low cost are mandatory requirements Designing and implementing a Digital Twin of a mooring line requires simultaneous adoption of several technologies and tools. Some of these technologies are still at early stage of development however are evolving at fast pace.

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Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesCharge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,990
Score d'incertitude au seuil0,999

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0010,000
Communication savante0,0000,000
Science ouverte0,0010,002
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,002

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,038
Tête enseignante GPT0,205
Écart entre enseignants0,167 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle