Application of computational fluid dynamics for thermohydrodynamic analysis of high-speed squeeze-film dampers
Bibliographic record
Abstract
In high-speed turbomachinery, the presence of rotor vibrations, which produce undesirable noise or shaft deflection and losses in performance, has brought up the need for the application of a proper mechanism to attenuate the vibration amplitudes. Squeeze-film dampers (SFDs) are a widely employed solution to the steady-state vibrations in high-speed turbomachinery. SFDs contain a thin film of lubricant that is susceptible to changes in temperature. For this reason, the analysis of thermohydrodynamic (THD) effects on the SFD damping properties is essential. This paper develops a computational fluid dynamics (CFD) model to analyze the THD effects in SFDs, and enabling the application of CFD analysis to be a base-line for validating the accuracy of analytical THD SFD models. Specifically, the CFD results are compared against numerical simulations at different operating conditions, including eccentricity ratios and journal whirl speeds. The comparisons demonstrate the effective application of CFD for THD analysis of SFDs. Additionally, the effect of the lubricant THDs on the viscosity, maximum and mass-averaged temperature, as well as heat generation rates inside the SFD lubricant are analyzed. The temperature of the lubricant is seen to rise with increasing whirl speed, eccentricity ratios, damper radial clearance, and shaft radii.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".