Hydrodynamic damping and stiffness prediction in Francis turbine runners using CFD
Bibliographic record
Abstract
Fluid-structure interaction (FSI) has a major impact on the dynamic response of the structural components of hydroelectric turbines. On mid- to high-head Francis runners, the rotor-stator interaction (RSI) phenomenon has to be considered carefully during the design phase to avoid operational issues on the prototype machine. The RSI dynamic response amplitudes of the runner are driven by three main factors: (1) pressure forcing amplitudes, (2) excitation frequencies in relation to natural frequencies and (3) damping. All three of the above factors are significantly influenced by both mechanical and hydraulic parameters. The prediction of the first two factors has been largely documented in the literature. However, the prediction of hydro-dynamic damping has only recently and only partially been treated. Two mode-based approaches (modal work and coupled single degree of freedom) for the prediction of flow-added dynamic parameters using separate finite element analyses (FEA) in still water and unsteady computational fluid dynamic (CFD) analyses are presented. The modal motion is connected to the time resolved CFD calculation by means of dynamic mesh deformation. This approach has partially been presented in a previous paper applied to a simplified hydrofoil. The present work extends the approach to Francis runners under RSI loading. In particular the travelling wave mode shapes of turbine runners are considered. Reasonable agreement with experimental results is obtained in parts of the operating range.
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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 | 0.001 |
| 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".