Conjugate Mass Transfer Modeling for VOC Source and Sink Behavior of Porous Building Materials: When to Apply It?
Bibliographic record
Abstract
Volatile organic compounds (VOC) are major indoor air pollutants. Physical models have been developed to predict VOC source (emission) and sink behavior (sorption) of building materials. They frequently adopt the conventional convection approach using a third-kind boundary condition. This conventional convection approach in conjunction with the commonly used Sherwood number correlation is based on the assumptions of constant wall concentration at the material-air interface and quasi-steady convective mass transfer in the fluid (air). In this study, the validity of these assumptions is theoretically investigated. An analytical model using the conventional convection approach and a numerical conjugate mass transfer model are developed. The conjugate mass transfer models consider unsteady two-dimensional laminar forced convection over a flat plate coupled with unsteady one-dimensional diffusion and sorption within the porous solid through the concentration and the flux continuities at the material-air interface. The simulation results indicate that the assumptions can lead to a significant overestimation of the wall concentration especially in the early transfer phase. When the effect on the VOC source/sink behavior is quantified by the total transfer time, which is the time required to emit/absorb 99% of the maximum transferable VOC mass, the analytical model results in less than 5% error in the predicted value when VOC transfer is controlled by internal diffusion, i.e., Biot number larger than 9 for (ε + K) 100.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".