Efficiency Assessment for Rehabilitated Francis Turbines Using URANS Simulations
Bibliographic record
Abstract
Due to the large number of aging hydraulic turbines in North America, rehabilitation is a growing market as these turbines have low efficiency compared to modern ones. Computational Fluid Dynamics identifies components with poor hydraulic performance. The models often used in industry are based on individually analyzing the sub-components of a turbine instead of full turbine simulations due to computational and time limitations. An industrial case has shown that such analyses may lead to underestimating the efficiency increases by modifying the stay vane. The unsteady full turbine simulation proposes to simulate all components simultaneously to assess this efficiency augmentation due to stay vane rehabilitation. The developed simulation methodology is used to evaluate the efficiency increase and the flow of two rehabilitated turbines with stay vane modifications. Comparison with model tests shows the accuracy of the simulations. However, the methodology used shows imprecision in predicting the efficiency increase compared to model tests. Further works should consider the use of more complex flow modeling methods to measure the efficiency increase by the stay vane modifications.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".