Effect of Fouling on the Performance of an Instream Turbine
Bibliographic record
Abstract
As the tidal energy industry starts to mature towards commercial projects a key focus is on reliable power performance. As for any marine application, fouling poses a potential performance reduction risk for instream turbine deployments. SCHOTTEL HYDRO have developed their current commercial SCHOTTEL Instream Turbines. Four drivetrains with 6.3m rotors were deployed on the surface platform PLAT-I by Sustainable Marine Energy. One of PLAT-Is key features is access to the turbines for inspection and maintenance in situ. The system has undergone sea testing from 2017 to 2021 in Scotland and Nova Scotia (Canada). This paper presents the hydrodynamic rotor performance reduction due to fouling based on full-scale experimental results. An in-house blade element momentum model is used to quantify the changes of the hydrodynamic forces in terms of lift and drag for the hydrofoils used. Furthermore, the effect of fouling on the downstream wake was quantified in the field. The performance reduction due to fouling is significant and leads to a power drop of up to 43%, whereas the thrust is reduced by 25%. This is also reflected in a reduction of the turbine’s downstream wake as a “fouled” rotor extracts less energy from the flow. Modifications of the polar data, used for semi-empirical performance predictions, are able to predict the effect of fouling on the rotor performance. In general, the results derived from the testing prove the significance of access to the turbines in order to avoid reduction in the turbines’ performance due to fouling.
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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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".