Aspects on viscoelasticity modeling of HDPE using fractional derivatives: Interpolation procedures and efficient numerical scheme
Bibliographic record
Abstract
© 2021 Taylor & Francis Group, LLC.Among the wide range of structural polymers currently available, this work deals with high-density polyethylene (HDPE). The typical viscoelastic behavior of this material is not trivial to model and has already been investigated by many authors. We employ the fractional Zener model to fit our experimental creep results of HDPE evaluated at different stress levels. This model produces fractional constitutive equations with excellent curve-fitting properties and fewer parameters to be identified in relation to traditional models. The results are compared with those ones provided by the application of the Prony series method. The first novelty of this paper is the application of the time-stress equivalence principle (TSEP), coupled to the fractional model, to estimate creep at intermediate stress levels, that in turn, were not measured experimentally but lie within the stress range used to calibrate the model. We compare the results provided by this method with those based on linear interpolation of the parameters. Although there is clear benefits requiring fewer parameters, fractional derivatives render costly computations due to their history memory. To cope with this, we propose a new algorithm, called GPE, which shows a compromise between enhanced efficiency and accuracy when compared with other proposals of the literature. These features are verified with simulations for simple functions, and a long term creep test with the fractional Zener model. The combined application of fractional derivatives, TSEP and the new GPE algorithm results in a novel efficient and effective alternative to account for the creep modeling of HDPE.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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".