Mitigation of vibrations caused by inter-blade vortices using pumping cap for natural aeration
Bibliographic record
Abstract
Abstract In 2014, Andritz received a contract to refurbish six Francis turbines with a total capacity of 840 MW. During the commissioning of the first unit, unexpected vibrations occurred within a power range of 25 to 65 MW, and Andritz was requested to find a solution. Unsteady computational fluid dynamics CFD simulations, revealed that the vibrations originated from intermittently cavitating inter-blade vortices. Conventional solutions involving compressed air injection were rejected, leaving natural aeration as the only viable option. Andritz explored various options for natural aeration by CFD and later, by means of homologous model tests. The solution was able to accommodate suction heads of approximately 10 meters and consisted of a specialized runner cap, referred to as pumping cap, which allowed for natural aeration through the hollow turbine shaft. The cap was optimized using two-phase CFD, and once installed on the prototype, it demonstrated a reduction of aproximately 50% in the peak level of vibration and noise. This achievement fully satisfied the customer, leading to the acceptance of the machine.
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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) | 0.001 | 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".