Determination of turbine runner dynamic behaviour under operating condition by a two-way staggered fluid-structureinteraction method
Bibliographic record
Abstract
This paper presents the application of the two-way fluid-structure interaction method introduced by ANSYS to calculate the dynamic behaviour of a Francis turbine runner under operating condition. This time-dependant calculation directly takes into account characteristics of the flow and particularly the pressure fluctuations caused by the rotor-stator interaction. This formulation allows the calculation of the damping forces of the whole system implicitly. Thereafter, the calculated dynamic stress can be used for a fatigue analysis. A verification of the mechanical and fluid simulations used as input for the fluid-structure interaction calculation is first performed. Subsequently, a connection of these two independent simulations is made. A validation according to the hydraulic conditions is made with the measurements from the scale model testing. Afterwards, the static displacement of the runner under the hydraulic load is compared with the results of a classical static analysis for verification purposes. Finally, the natural frequencies deduced by the post-processing of the dynamic portion of the runner displacement with respect to time are compared with the natural frequencies obtained from a classical acoustic modal analysis. All comparisons show a good agreement with experimental data or results obtained with conventional methods.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".