Development of Beam-To-Beam Contact Detection Algorithms for Rotor-Stator Rubbing Applications
Bibliographic record
Abstract
In nuclear power plant turbosets, the design-basis accident consists of a blade-off on the low pressure turbine last stage. During the accidental shutdown, a severe rotor-casing interaction may occur at critical speeds due to large shaft line displacements originated by a high unbalance excitation. The contact between the shaft and the stator, also called the diaphragm in this study, induces an important angular deceleration rate and greatly modifies the turbogenerator dynamics including the amplitude of the loads in the bearings. Therefore the main objective is to verify that the designed turbine is capable of going through critical speeds without catastrophic consequences for the shaft line. To this end, a model of a turbogenerator has been developed to compute rotor speed transients by considering the rotating speed of the rotor as an unknown, which allows for the angular deceleration due to rubbing to be calculated in a more realistic fashion. Lagrange multipliers method is applied to compute contact forces. The diaphragm, which is a non-rotating bladed disks assembly, is modeled by curved and straight beams, and different assumptions for the contact detection are studied to find a compromise between CPU time and accuracy. Results of the numerical tool show that the contact forces are sensitive to the retained assumptions only when heavy rub occurs. Nevertheless, the rotating speed and bearing loads are computed with a satisfactory accuracy, even with an approximation on contact detection that saves CPU time.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".