The Evaluation Of Modelling Techniques For Lubricant Cavitaion In The Application Of Squeeze Film Dampers
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Bibliographic record
Abstract
Squeeze film damper (SFD) is widely adopted in turbo-engines to suppress the rotor vibration. However, the prediction of SFD performance is complicated due to the inevitable occurrence of lubricant cavitation. This paper shows the application of three different cavitation algorithms for SFD with sealed conditions. In particular, the linear complementarity problem (LCP) method, which is advanced from a previous research study, is applied to compare results from the well-known methods, i.e. the -film model and the Elrod cavitation method, for SFD executing circular centered orbits with fully degassed lubricant in the absence of oil feeding. Moreover, numerical models are developed incorporating the mentioned algorithms to predict the hydrodynamic pressure distribution over the cavitated fluid film. Results show that the conventional -film model overestimates the cavitation region but under-estimates the reaction force.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it