Point source modelling approach for sessile droplet evaporation
Bibliographic record
Abstract
Evaporation of sessile droplets from unheated solid surfaces is a ubiquitous process in many practical applications. A reduced order, analytical point source model (PSM) for the axisymmetric diffusion-dominated evaporation of an isolated sessile droplet surrounded by non-saturated, quiescent air was developed. The droplet is modeled as a dynamic point mass source in the limit of an isothermal system. The model also incorporates the spatial variation in the evaporative flux across the droplet free surface. The model is capable of considering the mode of evaporation, i.e., constant contact angle or contract radius. The PSM was simulated using the finite difference method in MATLAB R2020a. The model determines the vapor concentration distribution in the surrounding environment, the instantaneous evaporative flux averaged across the droplet surface and the overall evaporation rate. Calculating the evaporation rate assuming a spatially uniform evaporative flux under-predicts the evaporation rate by up to an order of magnitude. The model results agreed with experimental data in literature and sufficiently captures the evaporation process phenomena. The versatility and accurate predictive power of the PSM allows it to be a robust and computationally inexpensive modeling tool for studying sessile droplet evaporation in a wide range of technical applications.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".