Numerical Simulation of Salmon Freezing Using Pulsating Airflow in a Model Tunnel
Bibliographic record
Abstract
Food freezing is an energy-intensive thermal process that has required exploring new technologies to enhance productivity and efficiency. This work provides a detailed insight into the energy analysis for the improved cooling of solid food during the freezing process, which originated by imposing a pulsating airflow at the entrance of a convective freezer tunnel. Continuity, linear momentum, and energy equations described simultaneously the conjugate transient heat conduction with liquid-to-solid phase change of the water content of a square salmon piece and the unsteady heat transfer by mixed convection in the surrounding airflow. The Finite Volume Method and a recently developed fast-accurate pressure-correction algorithm allowed an accurate prediction for the effects of imposing an inlet pulsating cooling airflow on the evolution of vortex-shedding, food freezing, cooling rate, heat flow, and energy savings. The variation in the values of the local heat fluxes at the food surface was reported, analyzed, and discussed by the evolution of the local Nusselt number around the square salmon piece. The study found that using an inlet pulsed airflow during salmon freezing improved temperature distribution and reduced energy consumption by 21% compared to using an inlet constant velocity airflow. The findings conclude that using pulsed airflow can improve temperature distribution in the food and significantly reduce energy consumption. Future investigations should consider a three-dimensional analysis, real salmon shape, turbulent conjugate convective freezing, an ensemble of salmon pieces, and exergy analysis to improve freezing tunnel design.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".