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Record W2113250278 · doi:10.1002/we.1669

Evaluating the importance of mooring line model fidelity in floating offshore wind turbine simulations

2013· article· en· W2113250278 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueWind Energy · 2013
Typearticle
Languageen
FieldEngineering
TopicWave and Wind Energy Systems
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsMarine engineeringOffshore wind powerTurbineSubmarine pipelineMooringLine (geometry)FidelityEngineeringHigh fidelityEnvironmental scienceAeronauticsAerospace engineeringTelecommunicationsElectrical engineeringGeotechnical engineering

Abstract

fetched live from OpenAlex

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.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.582

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.043
GPT teacher head0.281
Teacher spread0.238 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it