A Novel Space-Time Finite Element Algorithm to Investigate the Hygro-Mechanical Behaviours of Wood Fiber-Polymer Composites
Bibliographic record
Abstract
Moisture-induced swelling, or hygroexpansion, has been known to greatly deteriorate the durability of wood fiber-polymer composites (WPC). It is generally very expensive to perform experiments to completely obtain the diffusion kinetics as the process occurs in a very extended period of time. For the first time, we have developed a space-time finite element algorithm that employs time discontinuous Galerkin (TDG) method for time-dependent 3D hygro-mechanical behaviours of WPC. The formulation of matrix equations in spatial and temporal domains are explained in detail. A block Gauss-Seidel iterative method is used in the predictor/multi-corrector multi-pass algorithm, which efficiently yields unconditionally stable and high-order accurate solutions. The model is validated by comparing the predicted time-dependent hygroexpansion with that obtained in a previous experimental study. The quantitative analysis ensures the reliability of model, based on a Fickian diffusion process. With our adaptive time-stepping scheme that bases on the embedded solution from the multi-pass iterations, the model efficiently progresses the kinetics with relatively large time steps. A runtime of a few hours compared to about three months of actual laboratory experimentation confirms the novelty and robustness of our model.
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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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".