Towing Tank and Flume Testing of Passively Adaptive Composite Tidal Turbine Blades: Preprint
Bibliographic record
Abstract
Composite tidal turbine blades with bend-twist (BT) coupled layups allow the blade to self-adapt to local site conditions by passively twisting. Passive feathering has the potential to increase annual energy production and shed thrust loads and power under extreme tidal flows. Decreased hydrodynamic thrust and power during extreme conditions meann that the turbine support structure, generator, and other components can be sized more appropriately, resulting in a higher utilization factor and increased cost effectiveness. This paper presents new experimental data for a small-scale turbine with BT composite blades. The research team tested the turbine in the Kelvin Hydrodynamics Laboratory towing tank at the University of Strathclyde in Glasgow, United Kingdom, and in the recirculating current flume at the l Institut Francais de Recherche pour l Exploitation de la Mer Centre in Boulogne-sur-Mer, France. Tests were also performed on rigid aluminum blades with identical geometry, which yielded baseline test sets for comparison. The results from both facilities agreed closely, supporting the hypothesis that increased blade flexibility can induce load reductions. Under the most extreme conditions tested the turbine with BT blades had up to 11 percent lower peak thrust loads and a 15 percent reduction in peak power compared to the turbine with rigid blades. The load reductions varied as a function of turbine rotational velocity and ambient flow velocity.
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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) | 0.002 | 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".