Experimental and numerical validation of a Francis turbine draft tube designed for mitigation of pressure fluctuations
Bibliographic record
Abstract
Abstract This paper presents an experimental and numerical investigation of the internal flow in a Francis turbine draft tube previously designed for minimizing pressure fluctuations and energy losses in off-design conditions. The design of the draft tube geometry is based on an original approach combining Design of Experiments and steady/unsteady Computational Fluid Dynamics (CFD) simulations of the draft tube internal flow. The proposed method provides new insight on the influence of the draft tube geometry on the flow dynamic behaviour on one hand and enables the determination of a geometry promoting flow stability and hydraulic performance on another hand. CFD simulations of the internal flow in the final geometry showed promising results in terms of flow stability compared with the initial geometry designed by conventional CFD-aided methods. A reduced-scale model of the prototype machine featuring the final draft tube geometry is finally installed and tested in laboratory. Tests include performance and pressure fluctuations measurements over the complete operating range. The analysis of the results shows that the draft tube flow remains globally stable over the complete part-load range with pressure fluctuations amplitude lower than 1% of the net head. It is also shown that the dominant pressure component at the runner outlet in the draft tube cone is of synchronous nature. The physical mechanisms of excitation are finally highlighted by analysis of unsteady CFD simulation results.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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".