Verification and Validation of Droplet Freezing for Convective Boundary Condition Using Matched Asymptotic Perturbation Method and Computational Fluid Dynamics
Bibliographic record
Abstract
Abstract Droplet freezing has been a subject of several studies encompassing numerous applications such as food processing, spray freeze drying, ice accretion on the aircrafts and low temperature biological applications. The literature is rife with a comprehensive analytical and numerical treatment of the classical Stefan problem. There is a need, however, to address the development of accurate solutions for the two-phase Stefan problem where the initial droplet temperature is different than the freezing temperature of the droplet. A solution of such kind will allow for quick and convenient calculations for important geometric and operating parameters over a wide range. This study presents a computational fluid dynamics model along with a perturbation series solution approximation developed for the temperature field and the interface motion. The semi-analytical results are compared with experimental results in the literature and are further complemented by a computational fluid dynamics study of a freezing droplet using enthalpy-porosity method. Preliminary results indicate a good agreement between experimental, numerical and perturbation series solutions.
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".