Bibliographic record
Abstract
Abstract New data analysis based on a spring‐dashpot model is developed to provide very close simulation of stress variation in polyethylene (PE) when subjected to a multi‐relaxation (MR) test. The model consists of a quasi‐static branch and two (long‐ and short‐term) viscous branches. The study shows that viscous stresses applied to the two dashpots and the model parameters can be quantified as functions of displacement (stroke) by simulating closely the stress variation in relaxation. Using these parameters, influences of stress triaxiality and material grade on PE's stress response at relaxation stages are examined, and along with experimental data at the loading stages, spring constants in the two viscous branches are determined. The results indicate that among three PE grades, spring constants and their trend of variation are similar in the long‐term viscous branch, but not in the short‐term counterpart. The results also show that by decreasing specimen gauge length 10 times to increase stress triaxiality, spring constants in the two viscous branches become closer to each other. The work concludes that the new data analysis can determine all parameters in the model, which can then be considered to quantify the role of the viscous stress response for PE's load‐carrying performance. Highlights Use a simple model to simulate complex, multiple relaxation behaviors of PE. Determine all model parameter values to characterize PE's relaxation behavior. Illustrate the effect of stress triaxiality on PE's viscous stress response. Show the similarity and difference of the viscous stress response among 3 PEs.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".