Shared mooring systems for offshore floating wind farms: A review
Bibliographic record
Abstract
Offshore wind energy, as a form of renewable power, has seen rapid development in recent years. While fixed-bottom wind turbines are typically used in water depths less than 50 m, the utilization of floating offshore wind turbines (FOWTs) becomes essential for deeper waters. Secure and effective mooring systems play a crucial role in making FOWTs commercially viable. The concept of a shared mooring system offers an innovative solution for deploying floating wind farms in clusters or arrays, which can reduce overall construction costs for large-scale floating wind farms. It is imperative to optimize the shared mooring arrangement for maximum cost-effectiveness and wind farm stability. However, implementing a shared mooring system introduces complexity to the dynamics of FOWTs, requiring the development of advanced simulation tools to meet modelling requirements. Under the shared mooring arrangement, mooring lines and anchors face more significant challenges, such as chain-seabed interactions, soil cyclic weakening, and anchor out-of-plane loading, which underscore the need for innovative, reliable, and efficient shared anchor designs. This article offers an overview of the current research status on shared mooring systems for floating wind farms, which might serve as a valuable reference for the construction of large-scale floating wind farms worldwide.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".