A numerical modelling investigation of the development of a human cough jet
Bibliographic record
Abstract
Purpose This study aims (1) to numerically investigate the characteristics of a human cough jet in a quiescent environment, such as the variation with time of the velocity field, streamwise jet penetration and maximum jet width. Two different turbulence modelling approaches, the unsteady Reynolds-averaged Navier–Stokes (URANS) and large eddy simulation (LES), are used for comparison purposes. (2) To validate the numerical results with the experimental data. Design/methodology/approach Two different approaches, the URANS and LES, are used to simulate a human cough jet flow. The numerical results for the velocity magnitude contours and the spatial average of the two-dimensional velocity magnitude over the corresponding particle image velocimetry (PIV) field of view are compared with the relevant PIV measurements. Similarly, the numerical results for the streamwise velocity component at the hot-wire probe location are compared with the hot-wire anemometry (HWA) measurements. Furthermore, the numerical results for the streamwise jet penetration are compared with the data from the previous experimental work. Findings Based on the comparison with the URANS approach and the experimental data, the LES approach can predict the temporal development of a human cough jet reasonably well. In addition, the maximum width of the cough jet is found to grow practically linearly with time in the far-field, interrupted-jet stage, while the corresponding axial distance from the mouth of the jet front increases with time in an approximately quadratic manner. Originality/value Currently, no numerical study of human cough flow has been conducted using the LES approach due to the following challenges: (1) the computational cost is much higher than that of the URANS approach; (2) it is difficult to specify the turbulent fluctuations at the mouth for the cough jet properly; (3) it is necessary to define the appropriate conditions for the droplets to obtain statistically valid results. Therefore, this work fills this research gap.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".