Comparison of Seabed Friction Formulations in a Lumped-Mass Mooring Model
Bibliographic record
Abstract
This paper explores the impact of friction models on mooring line simulations. Seabed friction can play an important role in the determination of mooring loads of slack-moored floating offshore wind turbines. Most mooring models include a relatively simple seabed friction formulation, if any, and little examination of their accuracy is available in literature. Current implementations typically represent seabed contact as coulombic friction with ramping near zero velocity to mitigate instability in the numerical time integration. To assess the impact of this friction model’s use, we compare it against a more sophisticated friction model. This model differentiates between static and kinetic friction, where the former is dependent upon the forces acting on the line and the latter is a function of seabed’s normal response. Both friction models have been implemented into the MoorDyn mooring dynamics simulator and tested under a set of prescribed scenarios including snap loads and oscillatory motion, where the fairlead of a mooring line was driven along both linear and circular paths. Additionally, coupled floating wind turbine simulations using the OC4-DeepCwind semisubmersible show how the friction models affect the platform global response and the extreme and fatigue mooring loads. The results highlight practical differences between the models in terms of both loads prediction and simulation stability/consistency.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 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".