Numerical modeling of the medium-density fiberboard hot pressing process.Part 2, Coupled mechanical and heat and mass transfer models
Bibliographic record
Abstract
In this study, coupled mechanical and heat and mass transfer models describing mat compression and heat and moisture transfer occurring during hot pressing of medium-density fiberboard mats are presented. The mat is considered an aging linear elastic material and is described by a mechanical model in three dimensions. Rheological properties of the mat depended on time, space, temperature, moisture content, and resin cure. Hardening and softening phases of the material behavior were accounted for and treated with separate constitutive laws. Press closing was taken into account, and a coupling procedure between mechanical and heat and mass transfer models was elaborated. Development of the vertical density profile was dynamically predicted by the model. Both mechanical and heat and mass transfer models were discretized in space by the finite element method. An implicit second-order backward finite difference scheme was used for time discretization. All calculations were carried out on a moving geometry whose deformation (compression) was a function of a press closing schedule. Model results exhibited good agreement with experimental results. Under various press closing schedules, the model gave information on variables such as density profile, total gas pressure, air and vapor pressure, temperature, moisture content, RH, and degree of resin cure.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".