Development of a finite element model for solving 2D Navier-Stokes equations in deep surface textures causing cavitation
Bibliographic record
Abstract
This study investigates the cavitation effect in a partially textured parallel slider, focusing on deep textures where cavitation naturally occurs due to the geometry and operational conditions. The proposed model employs the Navier-Stokes equations under steady-state conditions, providing a more accurate representation of flow dynamics by incorporating convection effects, which are neglected in the Reynolds equation. Cavitation is modeled using a barometric formulation. Unlike models based on transport equations, the proposed approach does not rely on experimentally correlated parameters, making it more versatile and suitable for a broader range of applications. The solution is implemented using a two-dimensional finite element method program, employing an uncoupled pressure-based approach to effectively link velocity and pressure fields. Preliminary results indicate that cavitation reduces surface shear by lowering viscous drag, thereby decreasing friction. However, this reduction in friction comes at the expense of diminished load-carrying capacity, as deep textures generate less hydrodynamic lift under cavitating conditions.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".