Numerical Study of a Horizontal and Vertical Shell and Tube Ice Storage Systems Considering Three Types of Tube
Bibliographic record
Abstract
There is a growing interest in sustainable energy sources for energy demand growth of power industries. To align the demand and the consumption of electrical energy, thermal energy storage appears as an efficient method. In the summer days, by using a cold storage system like ice storage, peaks of the energy usage shift to low-load hours of midnights. Here, we investigate the charging process (namely solidification) numerically in an ice-on-coil thermal energy storage configuration, where ice is formed around the coil or tube to store the chilled energy. The considered ice storage system is a shell and tube configuration, with three kinds of tubes including a U-shaped tube, a coil tube with an inner return line, and a coil tube with an outer return line. Advanced 3D unsteady simulations are achieved to determine the effects of tube type and position of the ice storage (horizontal or vertical) on the solidification process. Results indicate that using a coil tube speeds up the ice formation, as compared with the simple U-shaped tube. The coil tube with an outer return line exhibits a better performance (more produced ice), as compared with the coil tube with an inner return line. After 16 h of solidification, the coil tube with the outer return line has about 1.057% and 1.32% lower liquid fraction in comparison with the coil tube with the inner return line and U-shaped tube, respectively, for both positions (vertical and horizontal).
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".