A Robust Modification to the Universal Cavitation Algorithm in Journal Bearings
Bibliographic record
Abstract
In the current study, a modified fast converging, mass-conserving, and robust algorithm is proposed for calculation of the pressure distribution of a cavitated axially grooved journal bearing based on the finite volume discretization of the Adams/Elrod cavitation model. The solution of cavitation problem is shown to strongly depend on the specific values chosen for the lubricant bulk modulus. It is shown how the new proposed scheme is capable of handling the stiff discrete numerical system for any chosen value of the lubricant bulk modulus (β) and hence a significant improvement in the robustness is achieved compared to traditionally implemented schemes in the literature. Enhanced robustness is shown not to affect the accuracy of the obtained results, and the convergence speed is also shown to be considerably faster than the widely used techniques in the literature. Effects of bulk modulus, static load, and mesh size are studied on numerical stability of the system by means of eigenvalue analysis of the coefficient matrix of the discrete numerical system. It is shown that the impact of static load and mesh size is negligible on numerical stability compared to dominant significance of varying bulk modulus values. Common softening techniques of artificial bulk modulus reduction is found to be tolerable with maximum two order of magnitudes reduction of β to avoid large errors of more than 3–40% in calculated results.
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 | 0.001 | 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 | 0.000 | 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".