A New Hybrid Algorithm for Numerical Simulation of VOC Emissions Using Single-Layer and Multilayer Approaches
Bibliographic record
Abstract
A new hybrid algorithm based on the lattice Boltzmann method (LBM) and the finite-volume method (FVM) is proposed for numerically calculating the emissions of volatile organic compounds (VOCs) from building materials and predicting their space distribution. Most building envelopes are comprised of single as well as multilayer materials, with some of them being porous and others nonporous. First, only the LBM is used to calculate the VOC concentration in airtight and ventilated chambers with constant as well as variable ventilation. For multilayer materials, including both porous and nonporous, half-lattice division methodology in the LBM is used, which ensures flux continuity at the interfaces. Good agreement is found between computed results and experimental data available in the literature. The effect of variable ventilation is also studied for both types of sequences of porous/nonporous layers. Then the LBM coupled with the FVM is used to investigate the VOC concentration distribution in the room emitted from styrene-butadiene rubber (SBR) plate, and good agreement is found between obtained results and those already published. The hybrid algorithm with multilayer approach is also used to conduct a detailed study of the effect of different ventilation organizations on the concentration in the room air, and the best one is found by the simulation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".