Turbomachinery Transient Dynamics of Radial Rotor-Stator Contact Occurrences With Friction
Bibliographic record
Abstract
Abstract The present paper focuses on the numerical investigation of the transient response of a rotorshaft affected by unilateral contact occurrences with friction mechanisms. Rotor-stator contact is initiated radially at the shaft level. The components are modeled under the rigid bodies assumption and the rotor dynamical response is investigated in the time domain. The turbomachine is assumed to be driven by two torques: the first one is prescribed while the second one is induced by friction using Coulomb’s model. Particular attention is paid to the accurate prediction of the unknown rotational speed transient. The proposed methodologies are grounded on the Carpenter and Moreau-Jean time-marching algorithms, implying the use of Lagrange multipliers to solve the frictional and unilateral contact conditions. The simplest procedure considers only sliding friction while the most sophisticated one involves convex analysis in order to deal with normal and friction forces independently is case of stiction. The solutions predicted by the algorithms are compared and show good agreement. The sensitivity study on the stator properties and friction coefficient allows the identification of the conditions affecting the rotational speed limitation. Based on the response post-processing in the time and frequency domains, it is found that a higher friction coefficient, a stiffer stator support or a lighter stator leads to a decrease of the rotational speed maximum value.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".