Mooring systems integrity management technologies
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.024 | 0.018 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".