CFD ANALYSIS OF PASSIVE SMOKING BY MEANS OF MICRO CLIMATE AROUND THE HUMAN BODY
Bibliographic record
Abstract
The purpose of the paper is to analyze passive-smoking in a room with 3-dimensional CFD analysis. We examine how much the breathing/inhalation air region is polluted by the smoke blown from a smoker's mouth or by the smoke arising from a lit cigarette. We evaluate the degree of pollution around the nose and mouth considering the indices of ventilation effectiveness. Displacement ventilation and mixing ventilation are tested as a ventilation system of the room. In the case of displacement ventilation, we examine two cases with the different speed of exhalation velocity. We observe that the thermal plume caused by the metabolic heat generation of the smoking person effectively transports both the cigarette smoke and blown smoke from the mouth upward and thereby the opposite-side person (passive smoking person) is not affected so much by the smoke. High-exposure case occurs only for the case with the high speed exhalation velocity and with the close distance between smoking and passive smoking person. On the other hand, in the case of mixing ventilation, the smoke tends to diffuse uniformly within the room regardless of the distance of the two persons.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".