A coupled pressure-based computional method for caviation in steady-state– a finite element modeling strategy applied to journal bearing
Bibliographic record
Abstract
This study presents a cavitation model applied to the Navier-Stokes equations in a steady state condition. The development of the proposed model targets the study of systems with textured surfaces, where the Reynolds equation cannot be applied to produce accurate evaluations. Indeed, the Reynolds equation is effective in restricted ranges of sliding speeds and for limited ratios of the texture dimensions. The developed approach eliminates these limitations. Instead of the finite volume method common in CFD, the proposed model is based on a finite element discretization. The cavitation phase is modelled by a barometric formulation. Using a pseudo-compressibility model, it is possible to simulate the compressibility according to the bulk modulus of the fluid in all phases. Compared to models using Rayleigh-Plesset equation, the advantage of the proposed method is that no parameter must be correlated experimentally. The obtained preliminary results agree with both experimental and numerical evaluations extracted from reference papers. In particular, the predicted cavitation areas are in perfect agreement with the reference numerical results presented for journal bearings.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".