Numerical and Experimental Validation of The Transient Heat And Mass Transfer During Heat Treatment Of Pine Wood
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Bibliographic record
Abstract
AbstractIn the current work, the three-dimensional Navier-Stokes equations along with the energy and concentration equations for the fluid coupled with the energy and mass conservation equations for the solid (wood) are solved to study the transient heat and mass transfer during the heat treatment of wood. The model for wood is based on Luikov's approach and solves a set of coupled heat and mass transfer equations. The model equations are solved numerically for the temperature and moisture content histories under different treatment conditions. The simulation of the proposed conjugate problem allows the assessment of the effect of the heat and mass transfer within wood on the transfer in the adjacent gas, providing good insight on the complexity of the transfer mechanisms. To generate data for comparison, measurements of temperature and moisture content of wood samples in a thermogravimetric system were conducted under different operating conditions. It is shown that the predicted and measured values compared very favourably, implying that the proposed numerical algorithm can be used as a useful tool in designing high-temperature wood treatment processes.Key Words: Mathematical modellingLuikov's modelconjugate problemhightemperaturewood treatmentheat and mass transfervalidation Additional informationNotes on contributorsR. YounsiRamdane Younsi is a professor at the applied science department of the University of Quebec at Chicoutimi.D. KocaefeDuygu Kocaefe is a professor and head of the Thermo Transformation Research Group (GRTB), University of Quebec at Chicoutimi.S. PoncsakSandor Poncsak is a professor at the applied science department of the University of Quebec at Chicoutimi.T. JunjunDuygu Kocaefe is a professor and head of the Thermo Transformation Research Group (GRTB), University of Quebec at Chicoutimi.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| 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 it