Modeling and Simulation of the Drying of Beech Timber (Fagus sylvatica)Using Oscillating Regimes
Bibliographic record
Abstract
This paper represents a numerical study of the massive (60mm) and thin (38mm) wood drying using oscillating drying regimes applied on beech timber (Fagus sylvatica). All thermo-physical properties relative of studied wood are taken from the literature on appropriate experiments. We showed that Luikov’s model can be used to predict temperature and moisture content evolutions using oscillating regimes. First numerically tests on 60mm timber wood consists in increasing and decreasing alternation of the drying parameters (temperature), at the rising 12-15°C and at the descent 10- 12°C, every alternation being higher than the precedent with 2-4°C. Second numerical test consists to oscillate the values of equilibrium moisture content with the oscillation amplitudes of ±10% and ±20% at the frequencies of 6hours. These practical experiments are detailed in the literature. Luikov’s model gives satisfactory results, according to the experiments obtained in the literature. But, convective transfer coefficients are function of each experiment and we observed a short difference between oscillation amplitudes of ±10% and ±20% on moisture content evolution. Thus, Luikov’s model can be a tool to study oscillation drying timber in order to reduce consumption of energy during the drying process.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".