Sensitivity study of a numerical model of heat and mass transfer involved during the medium-density fiberboard hot pressing process
Bibliographic record
Abstract
The objective of this work was to estimate the impact of the variability of the medium-density fiberboard mat heat and moisture transfer properties on the results predicted by a numerical model of hot pressing. The three state variables of the model, temperature, air pressure, and vapor pressure, depend on parameters describing the material properties of the mat known with a limited degree of precision. Moreover, different moisture sorption models and initial moisture contents also have an impact on the numerically predicted results. In this sensitivity study, we determined the impact of variations of the mat properties, sorption models, boundary conditions, and initial MC on the state variables. Our study shows that mat thermal conductivity, convective mass transfer coefficient of the external boundary, and gas permeability have the most significant impact on temperature, gas pressure, and MC within the mat. On the other hand, the convective heat transfer coefficient of the external boundary has no impact on the state variables. The sorption model affects significantly mat MC predictions only. The initial MC of the mat has a strong influence on the internal gas pressure.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".