Using Green’s function molecular dynamics to rationalize the success of asperity models when describing the contact between self-affine surfaces
Bibliographic record
Abstract
We use Green's function molecular dynamics to evaluate the effectiveness of asperity models when describing the contact mechanics of elastic solids with self-affine surfaces. Surfaces are created with the help of a Fourier filtering algorithm, and the interactions between the solids are modeled via hard-wall potentials. We illustrate how the real area of contact A_{real} is formed by a set of contact clusters. Two different regimes are identified when the normal force per cluster L_{c} is plotted as a function of its area A_{c} . Small clusters satisfy a Hertzian-type law L_{c} approximately A_{c};{32} , while large clusters display a linear L_{c} approximately A_{c} behavior. It is shown how the area A_{c};{*} , where the crossover between the two regimes takes place, depends only on the roughness at the smallest length scale if the longitudinal dimension of the surface remains unaltered. Moreover, our results display a distribution of cluster sizes P(A_{c}) remaining nearly constant for areas smaller than A_{c};{*} , while showing power law decay above such a critical value. Furthermore, we found the heights of the contacting atoms to be normally distributed with width inversely proportional to the surface roughness.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".