Hydro-dynamic damping theory in flowing water
Bibliographic record
Abstract
Fluid-structure interaction (FSI) has a major impact on the dynamic response of the structural components of hydroelectric turbines. On mid-head to high-head Francis runners, the rotor-stator interaction (RSI) phenomenon always has to be considered carefully during the design phase to avoid operational issues later on. The RSI dynamic response amplitudes are driven by three main factors: (1) pressure forcing amplitudes, (2) excitation frequencies in relation to natural frequencies and (3) damping. The prediction of the two first factors has been largely documented in the literature. However, the prediction of fluid damping has received less attention in spite of being critical when the runner is close to resonance. Experimental damping measurements in flowing water on hydrofoils were presented previously. Those results showed that the hydro-dynamic damping increased linearly with the flow. This paper presents development and validation of a mathematical model, based on momentum exchange, to predict damping due to fluid structure interaction in flowing water. The model is implemented as an analytical procedure for simple structures, such as cantilever beams, but is also implemented in more general ways using three different approaches for more complex structures such as runner blades: a finite element procedure, a CFD modal work based approach and a CFD 1DOF approach. The mathematical model and all three implementation approaches are shown to agree well with experimental results.
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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) | 0.001 | 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".