Two-dimensional mathematical framework for evaporation dynamics of respiratory droplets
Bibliographic record
Abstract
In majority of pandemics in human history, respiratory bio-aerosol is the most common route of transmission of diseases. These tiny droplets ejected through mouth and nose from an infected person during exhalation process like coughing, sneezing, speaking, and breathing consist of pathogens and a complex mixture of volatile and nonvolatile substances. A cloud of droplets ejected in such an event gets transmitted in the air, causing a series of coupled thermo-physical processes. Contemplating an individual airborne droplet in the cloud, boundary layers and wakes develop due to relative motion between the droplet and the ambient air. The complex phenomenon of the droplet's dynamics, such as shear-driven internal circulation of the liquid phase and Stefan flow due to vaporization or condensation, comes into effect. In this study, we present a mathematical description of the coupled subprocesses, including droplet aerodynamics, heat, and mass transfer, which were identified and subsequently solved. The presented two-dimensional model gives a complete analysis encompassing the gas phase coupled with the liquid phase responsible for the airborne droplet kinetics in the ambient environment. The transient inhomogeneity of temperature and concentration distribution in the liquid phase caused due to the convective and diffusive transports are captured in the 2D model. The evaporation time and distance traveled by droplets prior to nuclei or aerosol formation are computed for major geographical locations around the globe for nominal-windy conditions. The model presented can be used for determining the evaporation timescale of any viral or bacterial laden respiratory droplets across any geographical location.
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 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".