Numerical Study of the Winter–Kennedy Flow Measurement Method in Transient Flows
Bibliographic record
Abstract
This paper explores the possibility of using the Winter–Kennedy (WK) method for transient flow rate measurement in hydraulic turbines. Computational fluid dynamic (CFD) analysis of a numerical model of an axial turbine was carried out for accelerating and decelerating flows. Those were obtained by linearly opening and closing of the guide vanes, respectively, while retaining the inlet pressure constant during the simulations. The behavior of several WK configurations on a cross-sectional plane and along the azimuthal direction of the spiral casing was studied during the transients. The study showed that there are certain WK configurations that are more stable than others. The physical mechanism behind the stability (or instability) of the WK method during transients is presented. Using the steady WK coefficient obtained at the best efficiency point (BEP), the WK method could estimate the transient flow rate with a deviation of about 7.5% and 3.5%, for accelerating and decelerating flow, respectively.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".