Physical Mechanism of Interblade Vortex Development at Deep Part Load Operation of a Francis Turbine
Bibliographic record
Abstract
For seamless integration of growing electricity production from intermittent renewable energy sources, Francis turbines are under increasing demand to extend their operating range. This requires Francis turbines to operate under off-design conditions, where various types of cavitation are induced. At deep part load condition, an interblade cavitation vortex observed in a runner blade channel is a typical cavitation phenomenon causing pressure fluctuations and erosion, which prevent a reliable operation of Francis turbines at deep part load. The underlying mechanisms of its development are, however, yet to be understood. In an objective of revealing its developing mechanisms, the present study is aimed at investigating flow structures inside runner blade channels by comparison of three different operating conditions at deep part load using numerical simulation results. After demonstrating interblade vortex structures are successfully simulated by performed computations, it is shown that flow inside the runner at deep part load operation is characterized by a remarkable development of recirculating flow on the hub near the runner outlet. This recirculating flow is concluded to be closely associated with interblade vortex development. The skin-friction analyses applied to the hub identify the flow separation caused by a nonuniform distribution of flow, which describes the underlying physical mechanism of interblade vortex development. Investigations are further extended to include a quantitative evaluation of the specific energy loss induced by interblade vortex development. The integration of energy flux defined by rothalpy evidences the energy loss due to the presence of strong interblade vortex structures.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".