Numerical Assessment of an Air Cleaner Device under Different Working Conditions in an Indoor Environment
Bibliographic record
Abstract
Transmission and spread of exhaled contaminants in the air may cause many airborne infectious diseases. In addition to appropriate ventilation, air cleaner devices are used as one of the most common ways to improve the indoor air quality. Therefore, it is necessary to understand the performance of an air cleaner under different operating conditions. This study mainly concerns investigating the effect of presence or absence of furniture and its displacement on the removal rate of the particles leaving a person’s mouth while coughing in an isolated room. Moreover, the effect of air exit angle of the device on removal rate of contaminated particles and the pattern of their dispersion within a room was studied. To this aim, computational fluid dynamics were employed to examine the mentioned effects by using the Eulerian− Lagrangian method. As the results indicated, when the furniture was placed farther away from the device, more particles were removed by the device. Additionally, the air ejection angle of the air cleaner device significantly affects the removal of particles. Results of the present study could improve use of air cleaner devices for maximum reduction of particles in the indoor environment.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".