Numerical Modeling of the Freezing of a Porous Humid Food inside a Cavity due to Natural Convection
Bibliographic record
Abstract
The conjugate heat transfer problem of food freezing inside a cavity was numerically investigated. A vegetable sponge has been considered as a food block freezing inside a square freezing chamber due to natural convection. The need for specifying the block surface convective heat transfer coefficients was eliminated by solving the surrounding cooling fluid and, therefore, a comprehensive understanding on heat transfer and airflow during freezing can be achieved. The 2-D unsteady Navier-Stokes and energy equations were solved using a finite volume method with the semi-implicit SIMPLE algorithm. Thermophysical properties of food block components were considered to be dependent on temperature, as well as moisture and ice content, which has been rarely considered in food freezing studies. The specific heat capacity method was employed to model the freezing process. The Krischer model was adopted for predicting the thermal conductivity of the food, which indicates that the model works with reasonable accuracy for humid porous foods. The mechanism of natural convection in the cavity was carefully studied and the effects of different parameters on the freezing time were examined. The food freezing curves were investigated for various Rayleigh numbers in the range of 104 ≤ Ra ≤ 106, with different area ratios of A = 1/16, 1/9, and 1/4, and food initial water contents of X tw = 0.48, 0.58, and 0.68. It was concluded that increasing the Rayleigh number reduces the freezing time. On the other hand, area ratio and initial water content of the food were proved to extend the freezing time.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".