Numerical study of the Winter-Kennedy method for relative transient flow rate measurement
Bibliographic record
Abstract
Abstract The Winter-Kennedy (WK) method is used to estimate relative flow rate using the differential pressure between two taps located at a radial section of a spiral casing (SC). It is widely used in index testing, for double regulated turbines optimization and sometimes for continuous discharge measurement in low head plants. This paper explores the possibility of using the WK method for relative transient flow rate measurements. A numerical model of a Kaplan model turbine from the penstock to the distributor has been developed. Unsteady RANS simulations with k-ω SST turbulence model are performed. Previously conducted experiments on the model turbine are used to validate the numerical results. In the simulations, the guide vanes (GVs) are closed from 26.5°, the best efficient point (BEP), to about 5° opening angle. Two azimuthal locations of the SC and four different WK configurations at each location are considered. The variation of the WK coefficients with time are investigated and compared to the ones at several stationary GV angles. The results showed a difference between the WK coefficients obtained at transient and stationary operations. However, there may be a possibility of using the WK method during transients by locating the pressure taps in appropriate locations for an acceptable variation of the WK coefficient from its BEP value.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".