Numerical Investigation of Flow in a Runner of Low-Head Bulb Turbine and Correlation With Particle Image Velocimetry and Laser Doppler Velocimetry Measurements
Bibliographic record
Abstract
It is a well-known fact and a much studied problematic that the performance of low-head hydraulic turbines is highly dependent on the runner–draft tube coupling. Around the optimal operating conditions, the efficiency of the turbine follows closely the performance of the draft tube that in turn depends on the velocity field exiting the runner. Hence, in order to predict correctly the performance of the draft tube using numerical simulations, the flow inside the runner must be simulated accurately. Using results from unique and detailed particle image velocimetry (PIV) and laser Doppler velocimetry (LDV) measurements inside the runner channel of a bulb turbine, this paper presents an extensive study of the predictive capability of a widely used simulation methodology based on unsteady Reynolds-averaged Navier–Stokes equations with a k-epsilon closure model. The main objective was to identify the main parameters influencing the numerical predictions of the velocity field at the draft tube entrance in order to increase the accuracy of the simulated performance of the turbine. This paper relies on a comparison of simulations results with already published LDV measurements in the draft tube cone, interblade LDV, and stereoscopic PIV measurements within the runner. This paper presents a detailed discussion of numerical–experimental data correlation inside the runner channel and at the drat tube entrance. It shows that, contrary to widely circulated ideas, the near-wall predictions at the draft tube entrance is surprisingly good while the simulation accuracy inside the runner channels deteriorates from the leading to the trailing edges.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 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".