COVID-19 DISSEMINATION ASSESSMENT THROUGH NATURAL VENTILATION IN HOSPITAL PATIENT ROOM BY CFD ANALYSIS
Bibliographic record
Abstract
This paper studies the effect of natural ventilation on the spread of the COVID-19 virus from a patient room to an adjacent room with the help of airflow. The importance of this study is since COVID-19 virus contamination can easily transfer with the airflow from one room to the next room or adjacent corridor. This paper aims to determine the effect of natural ventilation on the contamination of the spaces next to the COVID-19 patients’ room.For this evaluation, we have used mechanical modelling and CFD simulation to evaluate the effect of natural ventilation on the transmission of COVID-19 with the airflow from a contaminated space to a clean space. During this study, we have calibrated the CFD model using one actual case, that was studied in a wind tunnel, and verified the modified model with the actual existing case. The simulated CFD model showed a reasonable accuracy for the prediction of ventilation in indoor spaces.Results showing the room geometries with air inlet/outlet that positioned at either bottom or top of the room will result in less COVID contamination dissemination through natural ventilation. In addition, in case of having the inlet/outlet in middle and positioning face to face and as well in the case of having max natural air velocity, the maximum contamination will exhaust from the space.
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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".