Experimental validation of CFD simulations of bioaerosol movement in a mechanically ventilated airspace
Bibliographic record
Abstract
A CFD (computational fluid dynamics) model was developed to simulate the movement of bioaerosols in mechanically-ventilated chambers and the results were validated with experiments. Liquid aerosols containing Porcine Reproductive and Respiratory Syndrome Virus (PRRSV) were artificially generated in the chambers. Bioaerosol concentration was monitored with an optical particle counter until steady-state conditions were achieved (aerosols containing viruses are referred to as bioaerosols in this paper). Four treatments with two ventilation rates and two bioaerosol generation rates were tested. The standard k-ɛ turbulence model and a discrete phase model with unsteady tracking was used in an ANSYS Fluent CFD model to simulate the airflow and bioaerosol movement until steady-state was reached. A mesh refinement test was performed to select an optimal mesh size for simulations. The CFD simulations showed good agreement with the measured bioaerosol concentrations at steady-state with differences of 2% to 8%, normalized mean square error of 0.01 to 0.19, and fractional bias of 0.02 to 0.08. Simulations and validation during the transient phase could not be verified because of limited measurement locations.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".