Columnar vortex array in hydraulic turbines and mitigation device
Bibliographic record
Abstract
With increasing climate change concerns and the fast addition of new renewables such as wind and solar power on electrical grids, the operation's flexibility of hydraulic turbines needs to be expanded. However, operating in low loads or speed-no-load (SNL) damages the turbine. In those operating regimes, energetic and harmful flow structures form. Mitigation techniques must therefore be developed to increase the safe operating range of hydraulic turbines. SNL operation is when the runner rotates at the synchronous speed, linked to the electrical grid, but no energy is extracted from the flow yet. It is the last step of a start-up or it can be used as spinning reserve. In low-head turbines operating at SNL, a columnar vortex array forms and generates important pressure fluctuations. In medium-head turbines, more often, interblade vortices are found. This study shows that a columnar vortex array can also be generated in a medium-head Francis turbine by removing the runner blades numerically. The geometry changes allowed a different instability mode to be excited. With a larger vaneless space, columnar vortices were generated instead of interblade vortices. Thus, columnar vortices are not dependent on the shape of the meridional channel. Therefore, to mitigate columnar vortices, another strategy must be found. In this presentation, a mitigation device lowering the input swirl is developed. The device is tested numerically on a low-head propeller turbine. Unsteady simulations with scale adaptative simulation turbulence model were performed. The results showed that the device effectively eliminates the columnar vortices and significantly reduces the pressure fluctuations on the runner blades.
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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 | 0.000 |
| 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 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".