Evaluating the importance of mooring line model fidelity in floating offshore wind turbine simulations
Bibliographic record
Abstract
Accurate computer modelling is critical in achieving cost-effective floating offshore wind turbine designs. Although a range of modelling fidelities are available for all parts of the simulation, a lower-fidelity quasi-static approach that neglects inertia and hydrodynamics is often used for the mooring line model. The loss of accuracy from using this approach has not been thoroughly studied across different support structure designs. To test the adequacy of this widely used simplified mooring line modelling approach, the floating wind turbine simulator FAST (National Renewable Energy Laboratory, Golden, Colorado) was modified to allow the use of a high-fidelity dynamic mooring line model, ProteusDS (Dynamic Systems Analysis Inc. of Victoria, BC, Canada). Three standard floating wind turbine designs were implemented in this new simulator arrangement and tested using a set of steady and stochastic wind and wave conditions. The static equivalence between the built-in quasi-static mooring model and the dynamic mooring model is within 0.6% in terms of fairlead tension. Tests of the systems’ responses in still water indicate that the hydrodynamic damping of the mooring lines can constitute anywhere from 1% to 35% of the overall system damping in pitch, depending on the design. Tests in steady and stochastic operating conditions show that for very stable designs with slack moorings, or designs with taut moorings, a quasi-static mooring model can in many conditions predict the platform motions and turbine loads with reasonable accuracy. For slack-moored designs with larger platform motions, however, a quasi-static model can lead to inaccuracies of as much as 30% in the damage-equivalent and extreme loads on the turbine. An important observation is that even in situations where the platform response is predicted reasonably well by a quasi-static model, larger inaccuracies can arise in the response of the rotor blades. These inaccuracies are more severe in the time series (with instantaneous discrepancies as high as 50% of the mean load) than in the corresponding damage-equivalent and extreme loads calculated over multiple stochastic simulations. Consequently, differences in damage-equivalent and extreme load metrics should be considered a floor to the measure of inaccuracy caused by a quasi-static mooring model. Copyright © 2013 John Wiley & Sons, Ltd.
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".