Numerical Modeling of the Melting Process in a Shell and Coil Tube Ice Storage System for Air-Conditioning Application
Bibliographic record
Abstract
Cold thermal energy storage, as a promising way of peak-shifting, can store energy by using cheap electricity during off-peak hours and regenerate electricity during peak times to reduce energy consumption. The most common form of cold storage air conditioning technology is ice on the coil energy storage system. Most of the previous studies so far about ice on coil cold storage system have been done experimentally. Numerical modeling appears as a valuable tool to first better understand the melting process then to improve the thermal performance of such systems by efficient design. Hence, this study aims to simulate the melting process of phase change materials in an internal melt ice-on-coil thermal storage system equipped with a coil tube. A three-dimensional numerical model is developed using ANSYS Fluent 18.2.0 to evaluate the dynamic characteristics of the melting process. The effects of operating parameters such as the inlet temperature and flowrate of the heat transfer fluid are investigated. Also, the effects of the coil geometrical parameters—including coil pitch, diameter, and height—are also considered. Results indicate that conduction is the dominant heat transfer mechanism at the initial stage of the melting process. Increasing either the inlet temperature or the flowrate shortens the melting time. It is also shown that the coil diameter shows the most pronounced effect on the melting rate compared to the other investigated geometrical parameters.
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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 | 0.000 |
| 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".