Assessment of arrays of in-stream tidal turbines in the Bay of Fundy
Bibliographic record
Abstract
Theories of in-stream turbines are adapted to analyse the potential electricity generation and impact of turbine arrays deployed in Minas Passage, Bay of Fundy. Linear momentum actuator disc theory (LMADT) is combined with a theory that calculates the flux through the passage to determine both the turbine power and the impact of rows of turbine fences. For realistically small blockage ratios, the theory predicts that extracting 2000-2500 MW of turbine power will result in a reduction in the flow of less than 5 per cent. The theory also suggests that there is little reason to tune the turbines if the blockage ratio remains small. A turbine array model is derived that extends LMADT by using the velocity field from a numerical simulation of the flow through Minas Passage and modelling the turbine wakes. The model calculates the resulting speed of the flow through and around a turbine array, allowing for the sequential positioning of turbines in regions of strongest flow. The model estimates that over 2000 MW of power is possible with only a 2.5 per cent reduction in the flow. If turbines are restricted to depths less than 50 m, the potential power generation is reduced substantially, down to 300 MW. For large turbine arrays, the blockage ratios remain small and the turbines can produce maximum power with a drag coefficient equal to the Betz-limit value.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".