Validation of deep part load dynamic stresses for axial runners
Bibliographic record
Abstract
Abstract To accommodate renewable energy production and load demand variability, hydropower plant owners need to increase their operating range regardless of their units’ original design load envelop. When this increased operating range is an issue for the fatigue life of the old runners, solutions need to be found with the design of a new runner to sustain those new challenging loads of the increased operating range. In recent years, many papers have been published to show the challenging loads on Francis runners at speed-no-load and deep part load conditions. Andritz demonstrated a good numerical prediction capability for stress levels at deep part load conditions for Francis runners. However, for axial units, very little has been published. Very recently, some papers showed good predictability by CFD of the flow behavior at deep part load including the vortices present at those conditions. This paper demonstrates the prediction capability of the numerical tools by comparing strain gage measurements on an axial runner to CFD-FEA stress predictions. The measurement campaign was conducted conjointly by the unit owner and the manufacturer for research purposes. In the deep part load operating zone under the effect of columnar vortices, frequency analysis of the measured vibrations and strain gage signals confirmed the flow behavior predicted by CFD, and the measured dynamic strain amplitudes were well predicted. Numerical prediction of dynamic stress in the complete range from 0-100% power of the measured unit as well as detection of high vibration zones was successful.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".