Experimental and Numerical Studies for a High Head Francis Turbine at Several Operating Points
Bibliographic record
Abstract
Experimental and numerical studies on a high head model Francis turbine were carried out over the entire range of turbine operation. A complete Hill diagram was constructed and pressure-time measurements were performed at several operating conditions over the entire range of power generation by installing pressure sensors in the rotating and stationary domains of the turbine. Unsteady numerical simulations were performed at five operating conditions using two turbulent models, shear stress transport (SST) k-ω and standard k-ε and two advection schemes, high resolution and second order upwind. There was a very small difference (0.85%) between the experimental and numerical hydraulic efficiencies at the best efficiency point (BEP); the maximum difference (14%) between the experimental and numerical efficiencies was found at lower discharge turbine operation. Investigation of both the numerical and experimental pressure-time signals showed that the complex interaction between the rotor and stator caused an output torque oscillation over a particular power generation range. The pressure oscillations that developed due to guide vanes and runner blades interaction propagate up to the trailing edge of the blades. Fourier analysis of the signals revealed the presence of a vortex rope in the draft tube during turbine operation away from the BEP.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".