Modeling the Influence of Temperature-dependent Thermal Properties on the Freezing Front
Bibliographic record
Abstract
Although numerical methods enable comprehensive analyses of food freezing, a thorough quantification is lacking the effects on the process introduced by uncertainties in variable thermal properties. Analytical models are, however, more suitable tools to perform such calculations. We aim to quantify these effects by developing a solution to the freezing front (FF) problem subject to temperature-dependent thermal properties and one-dimensional convective cooling. The heat integral balance method, Kirchhoff's transformation, and Plank's cooled-surface temperature equation (as a seed function) enabled us to obtain an approximate solution to the FF penetration time. To optimize model accuracy, two adjustable parameters were correlated with the inputs via nonlinear regression referenced to numerical simulation FF data. The mapped sensitivities, generated by perturbations in the temperature-dependent thermal conductivity and effective heat capacity, undergo rapid nonlinear changes for Biot numbers below 6. Above this level, these sensitivities stabilize depending on the cooling medium temperature and a thermal conductivity parameter. The median thermal conductivity-driven sensitivity is 0.348 and its interquartile range (IQR) is 0.220 to 0.425, whereas the median latent heat-driven sensitivity is 0.967 (IQR: 0.877 to 0.985). Statistical error measures and a ten-split K-fold validation support the model accuracy and reliability of the parameter estimates. Together, the model allows for gaining insights into the nonlinear behavior and magnitude of the influence of variable properties on the FF for a wide range of conditions. Nonlinear methods and prior information enable practical modeling of transport phenomena in foods.
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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 | 0.003 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".