Investigation of aerosol dispersion and air purifier performance in a hospital patient room using CFD and measurements
Bibliographic record
Abstract
Transmission of airborne disease is a concern in many indoor spaces. Recent studies have identified correlations between poor indoor air quality (IAQ) and COVID-19 vulnerability and mortality. Studying the role building design and ventilation play in both the spread and mitigation of airborne viruses in high-density spaces is thus imperative. However, guidance for IAQ improvement and COVID-19 risk mitigation is general and insufficient for specific application in at-risk spaces like British Columbia’s (BC) patient settings and long-term care homes. What remains underdefined is a workflow for translating site specific data on indoor aerosol spread into actionable tools health officials can use towards building retrofit and intervention planning. The objective of this project was thus to develop a library of ‘digital twin’ models of at-risk indoor spaces that can provide accurate and rapid investigations of indoor air quality improvement measures using computation fluid dynamics (CFD) software. To calibrate these models, 41 repeated controlled experiments of aerosol dispersion and removal were conducted to assess the ventilation patterns of a 4-bed hospital room. From these experiments, a 3D CFD model of the room was created using the RhinoCFD modelling package, calibrated with measured IAQ sensor data, and validated against the results of the live study. This paper presents the methodology and in-progress results of this CFD modelling process.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".