Damping Contributions of Semi-Taut Mooring for Floating Wind Turbines
Bibliographic record
Abstract
Abstract In this paper, the energy dissipation characteristics of a semi-taut mooring for floating offshore wind turbines are investigated. The experiment is performed in a 2 m depth towing tank at the Memorial University of Newfoundland. The setup is scaled to 1 : 60 to represent a 120 m ocean depth. A model mooring line is set up in the semi-taut configuration, and a programmable linear motor, equipped with both force and position sensors, is employed to excite the mooring line horizontally in a sinusoidal manner. A load cell is installed with the mooring line to measure axial tension. A pre-tension is applied to the line to ensure it is in the desired initial state before sinusoidal motions are imposed. Varying low-frequency amplitudes and periods of oscillations are analyzed to assess the energy dissipation performance of the semi-taut mooring configuration. This is done using the indicator diagram approach, which estimates energy loss within the system. The results reveal that energy dissipation is higher at shorter oscillation periods due to stronger drag forces but decreases with longer periods as the motion becomes quasi-static. The equivalent linear damping coefficient was derived, showing an inverse trend with energy dissipation: lower coefficients at shorter periods with rapid cycles and higher coefficients at longer periods due to extended damping time.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".