Eigenfrequencies of rotating discs in dense fluid: imposed modal motion approach
Bibliographic record
Abstract
Abstract The mode split on disc like structures rotating in a dense fluid leads to a deviation of eigenfrequencies at high rotational speeds compared to their values in still water. Predicting eigenfrequencies correctly is essential to avoid fatigue cracks on prototype turbine runners. Analytical models for simple geometric configurations and complex numerical models using fully coupled fluid structure interaction to predict the mode split on arbitrary geometries exist. We are presenting a complementary approach of intermediate complexity applicable to arbitrary geometries. Mode shapes and modal parameters are computed by finite element analysis in still water. These mode shapes are imposed with a harmonic variation in time during an unsteady computational fluid dynamics computation. From the interaction between the flow and the modal motion, the modal force and the modal work can be computed. These can be converted into added modal mass and hydrodynamic damping and further into the shift of the eigenfrequency under rotation due to the fluid for a given mode. The tendencies of the frequencies with rotation compare reasonably well with experimental data. The numerical method can be applied to disc rotation speeds far beyond the range of experimental data revealing interesting tendencies and a phenomenological interpretation of the cause of the mode split.
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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 | 0.001 | 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) | 0.002 | 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".