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Record W2583269471 · doi:10.3130/aija.65.17_4

CFD ANALYSIS OF PASSIVE SMOKING BY MEANS OF MICRO CLIMATE AROUND THE HUMAN BODY

2000· article· en· W2583269471 on OpenAlexaff
Tatsuya Hayashi, Shinsuke Kato, Shuzo Murakami, Jie Zeng

Bibliographic record

VenueJournal of Architecture and Planning (Transactions of AIJ) · 2000
Typearticle
Languageen
FieldEngineering
TopicBuilding Energy and Comfort Optimization
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsDisplacement ventilationExhalationVentilation (architecture)SmokeEnvironmental scienceMixing (physics)Room air distributionPlumeIndoor air qualityDisplacement (psychology)MeteorologyMarine engineeringMedicineEngineeringEnvironmental engineeringAnesthesiaPhysics

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.535
Threshold uncertainty score0.293

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.005
GPT teacher head0.215
Teacher spread0.210 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2000
Admission routes1
Has abstractyes

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