Skin friction blistering: computer model
Bibliographic record
Abstract
BACKGROUND/PURPOSE: Friction blisters, a common injury in sports and military operations, can adversely effect or even halt performance. Given its frequency and hazardous nature, recent research efforts appear limited. Blistering can be treated as a delamination phenomenon; similar issues in materials science have been extensively investigated in theory and experiment. An obstacle in studying blistering is the difficulty of conducting experiment on humans and animals. Computer modeling thus becomes a preferred tool. METHOD: This paper used a dynamic non-linear finite-element model with a blister-characterized structure and contact algorithm for outer materials and blister roof to investigate the effects on deformation and stress of an existing blister by changing the friction coefficient and elastic modulus of the material in contact with the blister. RESULTS: Through the dynamics mode and harmonic frequency approach, we demonstrated that the loading frequency leads to dramatic changes of displacement and stress in spite of otherwise similar loading. Our simulations show that an increased friction coefficient does not necessarily result in an increase in either the stress on the hot spot or blister deformation; local maximum friction stress and Von Mises stress exist for some friction coefficients over the wide range examined here. In addition, the stiffness of contact material on blistering is also investigated, and no significant effects on deformation and Von Mises stress are found, again at the range used. The model and method provided here may be useful for evaluating loading environments and contact materials in reducing blistering incidents. CONCLUSION: The coupling finite-element model can predict the effects of friction coefficient and contacting materials&apos stiffness on blister deformation and hot spot stress.
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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) | 0.012 | 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".