Designing, optimizing, and testing of a river hydrokinetic prototype turbine system for remote northern communities
Bibliographic record
Abstract
A hydrokinetic turbine extracts energy from river currents and can enable communities worldwide to establish micro-grids to address part of their base loads. Hydrokinetic turbines offer a viable solution to produce power year-round to displace diesel generation in northern communities. However, these turbines must operate in reduced winter flows and not be impacted by ice. A passive-counter-torque and river-prototype hydrokinetic turbine integrated system is presented that offers a simpler and lower-cost approach to deploy, operate and maintain hydrokinetic turbines year-round in cold climates. A 500-W river prototype designed with a 0.48 m diameter two-blade impeller is optimized, built and tested. It produces a torque of 24.44 Nm at 200 RPM for a flow velocity of 2.0 m/s. For this in-situ prototype, shrouds, winglets, and wingtips are developed and optimized to reduce the levelized cost of electricity and micro-grid performance when flow velocities are lower than the design set point of 2.0 m/s. Such off-design conditions are mainly experienced during winter seasons. Numerical simulations confirm that the winglet design maintains the design power when experiencing up to 18% reduction in velocity. Various design combinations were tested by varying component dimensions: 2,216 combinations for shrouds and 4,103 for winglets. The optimal results were achieved using the shrouds, while the winglets were found to have the advantage to prevent stalling. Testing of the prototype turbine at the Canadian Hydrokinetic Turbine Testing Centre shows that the counter-torque design selected was stable. However, using a field vacuum pump to control the ballast in the configuration tested needs to be reconsidered.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".