In situ wake measurement behind a 25-kW freewheeling vertical axis hydrokinetic turbine in energetic riverine environment using acoustic Doppler current profiler and acoustic Doppler velocimeter
Bibliographic record
Abstract
Hydrokinetic turbines present an opportunity for generating renewable energy sustainably in support of microgrids. This research examines the performance and environmental impact of a 25-kW New Energy vertical axis hydrokinetic river turbine under freewheeling conditions, focusing on flow and turbulence behavior. Field measurements of flow velocity at the Canadian Hydrokinetic Turbine Test Center on the Winnipeg River are measured using an acoustic Doppler current profiler and an acoustic Doppler velocimeter. Measurements are taken at various distances downstream of the turbine, from 1 to 17 turbine diameters, to analyze turbulence intensity, turbulent kinetic energy, and mean velocity profiles. The results indicated that turbulence intensity was highest near the turbine, with peaks at the centerline reaching 84% in the first acoustic Doppler current profiler test and 119% in the second, while acoustic Doppler velocimeter measurements showed 41% and 55%, respectively. As expected, turbulence levels gradually decreased with increasing distance from the turbine and are documented. Additionally, the TKE values exhibited a similar trend, demonstrating significant energy dissipation and flow stabilization further downstream. The mean velocity profiles revealed the maximum velocity deficit near the turbine, which gradually recovered with distance. River in-situ values measured do not compare favorably with scaled turbine water tunnel studies. This comprehensive analysis, comparing acoustic Doppler current profiler and acoustic Doppler velocimeter data, provides valuable insights into the wake dynamics and turbulence characteristics of vertical-axis turbines, which are required for optimizing turbine efficiency and assessing environmental impacts.
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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 | 0.000 |
| 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".