Prediction of Seabed Scour Induced by Full-Scale Darrieus-Type Tidal Current Turbine
Bibliographic record
Abstract
Scour induced by a Darrieus-type tidal current turbine was investigated by using a joint numerical and experimental method with emphasis on the scour process of a full-scale turbine. This work proposes a new numerical method to estimate turbine scour developments, followed by model validation through experimental data in the initial stage. The small-scale numerical model was further extended to a full-scale model for the prediction of turbine scour. The numerical model consists of (1) k- ω turbulence closure, (2) a sediment transport model, and (3) a sediment slide model. The transient-state model was coupled with a morphologic model to calculate scour development. A dynamic mesh updating technique was implemented, enabling the autoupdate of data for the grid nodes of the seabed at each time step. Comparisons between the numerical results and the experimental measurements showed that the proposed model was able to capture the main features of the scour process. However, the numerical model underestimated about 15–20% of the equilibrium scour depth than experimental data. An investigation of the temporal and spatial development of seabed scour around a full-scale Darrieus-type tidal current turbine is demonstrated. This work concludes that the proposed numerical model can effectively predict the scour process of tidal current turbines, and the rotating rotor has a significant impact on the equilibrium scour depth for full-scale turbines.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".