Heat Transfer and Thermodynamic Analyses of Some Typical Encapsulated Ice Geometries During Discharging Process
Bibliographic record
Abstract
This study deals with the process of melting in some typical encapsulated ice thermal energy storage (TES) geometries. Cylindrical and slab capsules are compared with spherical capsules when subjected to a flowing heat transfer fluid (HTF). The effect of inlet HTF temperature and flow rate as well as the reference temperatures are investigated, and the resulting solidification and melting times, energy efficiencies, and exergy efficiencies are documented. Using ANSYS GAMBIT and FLUENT 6.0 softwares, all geometries are created, and the appropriate boundary and initial conditions are selected for the finite volume solver to proceed. Sufficient flow parameters are monitored during transient solutions to enable the calculation of all energy and exergy efficiencies. The energetically most efficient geometric scenario is obtained for the slab geometry, while the spherical geometry exergetically achieves the highest efficiencies. The difference between the two results is mainly through the accounting of entropy generation and exergy destroyed, and the largest mode of thermal exergy loss is found to be through entropy generation resulting from heat transfer accompanying phase change, although viscous dissipation is included in the analysis. All efficiency values tend to increase with decreasing HTF flow rate, but exergetically the best scenario appears to be for the spherical capsules with low inlet HTF temperature. Energy efficiency values are all well over 99%, while the exergy efficiency values range from around 72% to 84%, respectively. The results indicate that energy analyses, while able to predict viscous dissipation losses effectively, cannot correctly quantify losses inherent in cold TES systems, and in some instances predict higher than normal efficiencies and inaccurate optimal parameters when compared with exergy analyses.
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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 | 0.000 |
| 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) | 0.001 | 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".