Spray freezing: An overview of applications and modeling
Bibliographic record
Abstract
Spray freezing technology finds broad applications across various industries such as food and pharmaceutical, mining, and water treatment. The significance of spray freezing is to offer a clean and renewable mechanism to generate heating and cooling potentials, frozen particles, or purified liquids. While several studies on spray freezing has been reported in the literature, no compilation of the findings is available, hindering further development of this technology. This paper reviews the diverse applications of spray freezing, emphasizing its potential to address engineering problems. The multi-scale multi-physics nature of the process is illuminated by shedding light on the significant physical mechanisms, including the droplet freezing and spray physics. The modeling advancements related to these phenomena are reviewed, showing the strengths and deficiencies of the current mathematical frameworks for spray freezing. It is underscored that further development of spray freezing requires high resolution frameworks, incorporating the droplet freezing and dynamics models while considering two-way coupling effects of the thermal and flow models of the droplet and cold medium. Additionally, the importance of studying the methods to mitigate the nucleation process of water in an industrially-relevant manner is highlighted.
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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".