Accelerating the charging process in a shell and dual coil ice storage unit equipped with connecting plates
Bibliographic record
Abstract
Frequent power outage in developing countries has created many problems for the people living in these regions, one of the most important of which is food spoilage due to the rise of refrigerator temperature. Ice storage systems are one of the promising techniques for handling this difficulty. Computational simulations are done here to influence the effects of dimensionless parameters on the charging rate of a shell and dual coil ice storage unit equipped with connecting plates as heat transfer enhancers. The ice storage unit is intended to be used as a backup cooling source for refrigerators in these regions. The studied parameters include the helical pitch length/storage height ratio (α1), the helical coil distance/storage diameter ratio (α2), the helical coil diameter/storage diameter ratio (α3), the connecting plate length/storage height ratio (α4), the connecting plate thickness/tube diameter ratio (α5), the modified Stefan number of the refrigerant flow (Ste*), and refrigerant flow Reynolds number (Re). The results suggest that the geometrical optimization of the proposed ice storage with α1, α2, and α3 parameters can improve the charging process up to 16.69%, 7.25%, and 18.84%, respectively. Also, the presence of full-length connecting plates can enhance the charging rate by up to 12%. While the influence of the Ste* on the charging rate is considerably high (25.56%), the Re does not exhibit a noticeable effect (0.95%). Moreover, the influence of natural convection on the process was considered, however, it was found that it does not have a considerable effect on the ice formation.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".