Two-way simulations of resonances during the acceleration of a rotatingstructure undergoing rotor-stator interactions
Bibliographic record
Abstract
With the increase of fluctuating energy sources like wind and solar on the electric grid, hydraulic turbines are used more often in a compensating role. This translates into an increase in the number of starts and stops sequences of turbines. Start-up sequences are recognized as highly damaging events. Nowadays, part of hydropower research efforts is oriented toward the understanding of fluid-structure interactions during start-up. An explanation of the high strain level during start-up is linked to a momentary match between an eigenmode of the runner structure, at a given eigenfrequency, and the rotor-stator interaction (RSI). The objective of the present research is to study, using simulations, the FSI of a simplified runner model while going through a resonance during an acceleration. First of all, the presentation will present an FSI simulation methodology using Star CCM+ software and validated using a hydrofoil test case. The methodology uses two-way FSI coupling of the flow and structural dynamics, combining high-order Segregated finite volume solver for the fluid and a finite element solver for the solid. This validated methodology was then applied to a simplified turbine test case where the runner was specifically designed to undergo a resonance with the rotor-stator interactions during an acceleration of its rotating speed. The presentation details the methodology and its validation and presents partial results of the turbine test case. At term, this research will lead to the development of a methodology to perform two-way simulations of RSI induced resonances during rotation speed variations in turbomachinery. Using the generated databases, it will also provide a unique insight into transient fluid-structure interactions that might be related to high stresses during the start-up of hydraulic turbines.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".