Squeeze film dampers supporting high-speed rotors: Rotordynamics
Bibliographic record
Abstract
This work develops a finite element based multi-mass flexible rotor model for theoretical investigation of the influence of the squeeze film damper lubricant inertia on the unbalance-induced steady-state and transient vibration amplitudes of high speed turbomachinery. The rotordynamic model is developed by applying the principles of finite element analysis to discretize the rotor components, including the rotor shaft and disk, into local elements with mass, stiffness, and gyroscopic matrices. Subsequently, the local matrices are assembled together to develop the global model of the rotordynamic system. The influence of squeeze film damper lubricant inertia is incorporated into the model by using short-length cavitated damper models with retaining springs executing circular-centered orbits. Additionally, the rotordynamic model incorporating the nonlinear squeeze film damper models is iteratively solved in the time domain by applying a predictor-corrector transient modal integration numerical method and the steady-state and transient motions of the rotor system are investigated under different rotor and squeeze film damper parameters. The results of the study verify the substantial influence of squeeze film damper lubricant inertia on attenuating the vibrations of high-speed turbomachinery. Furthermore, the developed rotordynamic model delivers an efficient and powerful platform for the analysis of high-speed turbomachinery, including jet engines and gas turbines.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".