Validation of a hybrid modeling approach to floating wind turbine basin testing
Bibliographic record
Abstract
Abstract Hybrid modeling combining physical tests and numerical simulations in real time opens new opportunities in floating wind turbine research. Wave basin testing is an important validation step for floating support structure design, but current methods are limited by scaling problems in the aerodynamic loadings. Applying wind turbine loads with an actuation system controlled by a simulation that responds to the basin test offers a way to avoid scaling problems and reduce cost barriers for floating wind turbine design validation in realistic coupled conditions. In this work, a cable‐based hybrid coupling approach is developed and implemented for 1:50‐scale wave basin tests with the DeepCwind semisubmersible floating wind turbine. Tests are run with thrust loads provided by a numerical wind turbine model. Matching tests are run with physical wind loads using an above‐basin wind maker. When the numerical submodel is set to match the aerodynamic performance of the physical scaled wind turbine, the results show good agreement with purely physical wind‐wave tests, validating the hybrid model approach. Further hybrid model tests with simulated true‐to‐scale dynamic thrust loads and wind turbulence show noticeable differences and demonstrate the value of a hybrid model approach for improving the true‐to‐scale realism of floating wind turbine basin tests.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".