IN-SITU TESTING OF A DARRIEUS HYDRO KINETIC TURBINE IN COLD CLIMATES
Bibliographic record
Abstract
There is a significant potential for kinetic turbine technology in Canada. An estimated 225 GW of power have been identified in wave and tidal energy with river energy yet to be adequately assessed. Manitoba is an ideal location for river turbines, and thus this study was conducted to demonstrate the turbine’s feasibility in cold climates. Frazil ice is a cause for concern in northern regions because it reduces the output power of larger hydro installations and can adversely impact kinetic turbine installations. Along with environmental concerns, a 5 kWe Darrieus turbine was evaluated for its performance. Sources of power loss were quantified in this study. It was found that the turbine’s support arms contributed a significant loss of up to 66%. The non-ducted Darrieus design self-started in a flow of 2 m/s and saw a peak power coefficient of 0.35 while producing reliable and consistent power to the grid throughout the winter and summer months. ii
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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 | 0.001 | 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".