Inlet Velocity Profile Optimization of the Turbine 99 Draft Tube
Bibliographic record
Abstract
In recent years, several investigations on hydraulic turbine draft tube performance have shown that the hydrodynamic field at the runner’s outlet is a direct outcome of the runner design and the operating point. This has shown the dependence of the diffuser efficiency on the flow rate and the inlet swirling flow intensity, mostly on turbines that present low head (high specific velocity) and operate away from their best efficiency point. The numerical optimization of the inlet velocity profile is presented as an attempt to control these two inlet flow characteristics. The goal is the improvement of the flow through the draft tube to allow for better turbine performance. This methodology is based on the automatic coupling of several commercial softwares and is used to manipulate the analytical representation of the swirling flow, which has led to the minimization of hydraulic losses. Also, a qualitative and quantitative analysis of the draft tube flow field provoked by a redesigned inlet velocity profiles, has helped to understand how it is possible to suppress or at least mitigate undesirable draft tube flow characteristics.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".