Closed-form solutions for contact pressure distribution generated by 2D rough profiles
Bibliographic record
Abstract
This study investigates the influence of surface roughness on contact mechanics, addressing the limitations of existing models that often rely on idealized and symmetric asperity shapes. We introduce a generalized representation of asperity geometries, including non-symmetric profiles, to better capture the diversity of surface characteristics encountered in real-world applications. By applying this parametric asperity model, we perform numerical simulations to analyze the impact of different parameters on contact behavior, effectively identifying various interaction regimes. The analysis is based on the assumption of elastic contact and focuses on two-dimensional roughness profiles characterized by surfaces that remain invariant along the axis orthogonal to the rolling direction. This approach effectively simulates geometries that display sufficient invariance along this axis, thereby representing realistic asperities in the form of streaks. Our theoretical framework quantifies the resulting analytical pressure distribution as a function of both geometric and mechanical parameters of the generalized asperities. By accommodating non-symmetric asperity geometries, our approach enhances model accuracy while significantly reducing computational time and resource requirements compared to traditional numerical methods.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".