Experimental and computational investigations of the flow within a scale model of a hydroelectric generator
Bibliographic record
Abstract
Decarbonization of the Atlantic Northeast’s electricity is achievable by utilizing Québec’s power system, if the reliability of the latter can be ensured. However, a critical element is the thermal management of the power system’s aging hydroelectric generators. To improve the thermal management, a 1:4 scale model of a hydroelectric generator was developed by Hydro-Québec. The research presented in this work utilized the scale model to refine a thermal mass flow meter and develop a numerical model to simulate the flow and heat transfer within hydroelectric generators. The new design of the flow meter enabled the first measurements of the flow rate within the rotor rim ducts of an in-service hydroelectric generator. Particle image velocimetry measurements demonstrated that the improved design had an accuracy of 8% and a 3.5% measurement repeatability, and allowed for the characterization of the flow in the rotor rim of the scale model. To further investigate the thermal management of hydroelectric generators, a numerical model capable of predicting the locations of hot-spots on the scale model’s rotor pole was developed. The numerical model employed a meshing technique that reduced the mesh generation time for hydroelectric generators from months to hours, and predicted the net mass flow rate, windage losses, maximum and average pole temperatures to within 5%, 4%, 3°C, and 5°C of experimental results, respectively. Furthermore, the numerical model was utilized to investigate alternate ventilation configurations for the scale model, which showed that i) adding a deflector at the pit outlet reduced the windage losses by 8.8%, and ii) increasing the surface area of the spider arms reduced the pole’s maximum surface temperature by 2.6°C
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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".