Investigations of Spoilers to Mitigate Columnar Vortices in Propeller Turbines at Speed-No-Load Based on Steady and Unsteady Flow Simulations
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Bibliographic record
Abstract
Abstract With the introduction of an ever-larger share of renewable but intermittent energy sources on electrical grids, hydraulic turbines are more often used as network stabilizers. In such a role, they are generally operated in off-design operations like speed-no-load (SNL). No energy is extracted from the flow at SNL operation, but the runner rotates at the synchronous speed linked to the electrical grid. The flow inside the runner of low-head turbines operating at SNL is often dominated by a columnar vortex array that may induce damaging pressure fluctuations. This paper presents the study of a control device to mitigate those vortices. At SNL, the small guide vane opening leads to a high swirl in the runner generating secondary flows such as columnar vortices and backflows. The proposed concept is to move SNL operation toward a higher guide vane opening and hence lower swirl, preventing the formation of a columnar vortex array. Lowering the input swirl of SNL is accomplished by opening up the guide vanes while using a control device to limit the discharge. The control device, like a spoiler on an aircraft wing, is introduced on the guide vanes to generate added head losses, significantly decreasing the discharge in high guide vane angles. This paper compares the hydrodynamics of the flow in a propeller turbine with different spoiler geometries. The study is based on both Reynolds-averaged Navier–Stokes (RANS) and unsteady RANS (URANS) flow simulations. It highlights how such devices can successfully mitigate columnar vortices and their associated pressure fluctuations on runner blades.
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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 | 0.001 | 0.001 |
| 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) | 0.000 | 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 it