Evaluation of Reduced Order Models for an Initial Assessment of Floating Wind Turbine Dynamics
Bibliographic record
Abstract
Abstract The technical development of Floating Offshore Wind Turbines (FOWTs) relies on understanding their dynamics under critical environmental conditions. Unlike other types of floating offshore units, for which the limiting loading cases are usually related to extreme environmental conditions, FOWTs may face additional challenges due to the rotor thrust, which can result in critical loadings even in operational conditions. Therefore, during a FOWT design it is necessary to assess a broad range of environmental conditions typically based on extensive time-domain simulations. In this context, the present study investigates the accuracy of frequency-domain and reduced-order models in estimating the dynamic responses of FOWTs hull and mooring system tensions, comparing the results with those obtained from time-domain simulations and model scale experimental data. More specifically, the performance is assessed in terms of mooring loads, maximum platform offsets and nacelle’s accelerations. The case-study is based on a semitaut moored semisubmersible FOWT supporting the RWT IEA15MW in a water depth of 2000m. Time-independent models adopted for mooring characterization and platform’s offset estimation are based on analytical formulation, while the floater dynamics is evaluated in the frequency-domain from estimated RAOs. Time-domain simulations were performed in OpenFAST for more than 300 environmental conditions, and the model-scale data were obtained through a dedicated experimental campaign for a selected set of environmental conditions. Results indicate that time-independent models may offer significant advantages during the early design stages, allowing faster analysis, optimization and identification of environmental conditions that can lead to critical loadings.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".