Enhanced Evaporation of Microscale Droplets With an Infrared Laser
Bibliographic record
Abstract
Enhancement of water droplet evaporation by added infrared radiation was modeled and studied experimentally in a vertical laminar flow channel. Experiments were conducted on droplets with nominal initial diameters of 50 μm in air with relative humidities ranging from 0% to 90% RH. A 2800 nm laser was used with radiant flux densities as high as 4 × 105 W/m2. Droplet size as a function of time was measured by a shadowgraph technique. The model assumed quasi-steady behavior, a low Biot number liquid phase, and constant gas–vapor phase material properties, while the experimental results were required for model validation and calibration. For radiant flux densities less than 104 W/m2, droplet evaporation rates remained essentially constant over their full evaporation, but at rates up to 10% higher than for the no radiation case. At higher radiant flux density, the surface-area change with time became progressively more nonlinear, indicating that the radiation had diminished effects on evaporation as the size of the droplets decreased. The drying time for a 50 μm water droplet was an order of magnitude faster when comparing the 106 W/m2 case to the no radiation case. The model was used to estimate the droplet temperature. Between 104 and 5 × 105 W/m2, the droplet temperature changed from being below to above the environment temperature. Thus, the direction of conduction between the droplet and the environment also changed. The proposed model was able to predict the changing evaporation rates for droplets exposed to radiation for ambient conditions varying from dry air to 90% relative humidity.
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 | 0.000 | 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 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".