Application of a master curve and the modified superposition principle for modeling creep and loading rate effects at small strains in high-density polyethylene
Bibliographic record
Abstract
Studying the nonlinear viscoelastic behavior of high-density polyethylene (HDPE) at small strains and stresses is still a matter of interest in engineering applications such as laying submerged pipelines. Although sound modeling of such behavior requires complex phenomenological or micromechanical constitutive laws, many works have focused on the development of simplified procedures for approximating this type of nonlinear response. Usually, when these simplified methods are employed to reproduce creep behavior, they are not capable to simultaneously provide good estimates for traction tests even at constant stress or strain rates. This work describes a methodology, which has shown a good compromise to reproduce both types of responses within a given stress range. The procedure can be understood as an interpolative approach based on a master curve and the modified superposition principle to account for nonlinear effects. With this strategy, it is possible to predict the nonlinear creep behavior for an HDPE sample subjected at any constant stress level within a given experimental range. Once this predictive capability is achieved, we use an incremental algorithm based on the modified superposition principle to simulate traction tests at constant strain rates. We show that the combined application of the proposed master curve approach and the modified superposition principle results in good approximations for creep tests and simultaneously leads to remarkable agreement with experimental traction tests reported in the literature.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 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".