Exploring heat transfer in freezing supercooled water droplet through high-speed infrared thermography
Bibliographic record
Abstract
This study explores the intricate heat transfer dynamics and thermographic patterns during the phase change from supercooled liquid water to ice. Using high-resolution, high-speed infrared thermography, real-time temperature data were captured during the freezing process. The resulting temperature profiles reveal critical insights into the freezing dynamics, particularly highlighting the rapid phenomena of recalescence in supercooled conditions. Notably, this study represents the first time recalescence, a rapid and previously elusive phenomenon, captured and documented in the scientific literature. Additionally, a mathematical model is developed to describe the recalescence phase on macro scale. These findings have practical relevance for various industries, aiding in the design of more efficient anti−/de-icing technologies, refrigeration systems, weather prediction models, and cryopreservation techniques. The study also opens new avenues for further exploration in understanding phase transitions in supercooled water. • The study presents the first-time high-speed infrared thermography capture of recalescence , a rapid and previously elusive phenomenon occurring during the freezing of supercooled water droplets. • The high-speed infrared thermography data provides crucial insights into the heat transfer mechanisms with a specific focus on the latent heat release and temperature profiles. • A mathematical model based on a sigmoid function is introduced to describe the temperature evolution during the recalescence phase, offering a macro-scale explanation of the process. • The findings have significant implications for improving anti−/de-icing technologies, refrigeration systems, weather prediction models, and cryopreservation techniques , making the results highly relevant for various industries.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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 teacher head, 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".