Abstract IGHEM Montreal ‘96 MEASURING HYDRAULIC TURBINE DISCHARGE WITH THE ACOUSTIC SCINTILLATION FLOWMETER
Bibliographic record
Abstract
Hydraulic turbine discharges in low-head hydroelectric plants, and plants with awkward intake geometries can be measured relatively easily with the Acoustic Scintillation Flowmeter (ASFM). The ASFM is non-intrusive, and may be deployed in intake gate slots in a straightforward manner, lending itself to multiple measurements in the same plant. Examples of measurements in two generating stations are presented. Tow-tank tests have shown the current speed measured by the ASFM to be accurate to within ±0.3 % over a range of towing speeds from 0.5 to 5.0 m/sec. A recent comparison of discharge measured by an ASFM and an acoustic timeof-travel meter, made at B.C. Hydro's Revelstoke Dam this spring was invalid due to installation problems. The factors affecting the accuracy of ASFM discharge measurements are discussed, and plans for further comparison testing are outlined. Résumé Le débit de turbines à basse chute, ou à travers des prises d'eau à géometrie compliquée, peut être mesuré directement avec la méthode par scintillation acoustique "ASFM". L'instrumentation “ASFM ” peut être déployée facilement dans les guides de vannes de prise d'eau, ce qui permet de faire des mesures multiples dans une même usine. L'example de mesures effectuées dans deux usines hydroélectriques est presenté. Des essais de remorquage dans un bassin ont demontré que la précision des mesures est de l'ordre de ±0.3 % pour des vitesses de remorquage entre 0.5 et 5.0 m/s. Une comparaison du débit mesuré par "ASFM " et par une méthode mesurant le temps de trajet d'un signal acoustique été effectuée par BC Hydro au barrage de Revelstoke le printemps passeé. Malhereusement, des difficultés d'installation n'ont permis des mesures qu'on peut utiliser pour faire des comparaisons. Les facteurs influençant la précision des mesures par "ASFM " sont discutés, et des projets pour d'autres essais sont expliqués.
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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 | 0.001 |
| 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.026 | 0.006 |
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".