Texture shape effects on hydrodynamic journal bearing performances using mass-conserving numerical approach
Bibliographic record
Abstract
It is a known fact that incorporating textures in the contact surfaces can significantly enhance bearing performances. The purpose of this paper is to outline the effects of texture bottom profiles and contour geometries on the performances of hydrodynamic textured journal bearings. The analysis was conducted using computational approach to test eight texture shapes: rectangular, cylindrical, spherical, triangular (TR, T1, T2, T3) and chevron. The steady-state Reynolds equation for modelling the hydrodynamic behaviour of thin viscous film was solved using finite difference technique and mass conservation algorithm (JFO boundary conditions), taking into account the presence of textures on both full film and cavitation regions. The comparison with the benchmark data shows good consistency and an enhancement in bearing performances (load carrying capacity and friction). The results clearly show that the mechanisms of wedge effect and micro-step bearing for the full/partial texturing feature are the main crucial parameters, where the convergent wedge effect present in T2 triangular texture shape can significantly enhance the load-carrying capacity, while the divergent wedge action causes a net load loss. Considering the right arrangement of textures on the contact surface, their surface contours can have a significant impact on the performance of hydrodynamic journal bearings at high eccentricity ratios.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".