Predictive performance of viscous potential functions for modeling strain rate sensitivity of soft materials
Bibliographic record
Abstract
Constitutive models are crucial for predicting and optimizing complex material systems via numerical techniques such as the finite element method. In addition to large nonlinear elastic deformation, strain rate sensitivity is an intrinsic mechanical characteristic of soft materials, including elastomers, hydrogels, and biological tissues. Accurate mathematical formulations describing these mechanical characteristics ensure time and cost efficiency, reliability, and improved design performance. Several modeling approaches have been proposed in the literature. The external state variable approach is advantageous thanks to its relative ease in numerical implementation and satisfaction of the principles of thermodynamics. This study presents the predictive capabilities of three different forms of viscous potential functions over five soft materials, including polyvinyl alcohol hydrogel, optically clear adhesive, elastomeric polyurethane, very high bond 4910, and styrene-ethylene-butylene-styrene gel. Accuracy of the predictions was quantified using the coefficient of determination and the normalized mean absolute difference. Results demonstrated that a recently proposed viscous potential function, named model 3 in this study, is relatively accurate and versatile in describing the rate-dependent behavior of soft materials. The results presented herein help researchers and design engineers to select the right models, provide insights into existing limitations, and guide the development of improved and more versatile models.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.000 | 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".