Thermodynamic Analysis of Freezing and Melting Processes in a Bed of Spherical PCM Capsules
Bibliographic record
Abstract
The solidification and melting processes in a spherical geometry are investigated in this study. The capsules considered are filled with de-ionized water, so that a network of spheres can be thought of as being the storage medium for an encapsulated ice storage module. ANSYS GAMBIT and FLUENT 6.0 packages are used to employ the present model for heat transfer fluid (HTF) past a row of such capsules, while varying the HTF inlet temperature and flow rate, as well as the reference temperatures. The present model agrees well with experimental data taken from literature and was also put through rigorous time and grid independence tests. Sufficient flow parameters are studied so that the resulting solidification and melting times, exergy and energy efficiencies, and exergy destruction could be calculated. All energy efficiencies are found to be over 99%, though viscous dissipation was included. Using exergy analysis, the exergetic efficiencies are determined to be about 75% to over 92%, depending on the HTF scenario. When the HTF flow rate is increased, all efficiencies decrease, due mainly to increasing heat losses and exergy dissipation. The HTF temperatures, which stray farther from the solidification temperature of water, are found to be most optimal exergetically, but least optimal energetically. The main reason for this, as well as the main mode of loss exergetically, is due to entropy generation accompanying heat transfer, which is responsible for over 99.5% of exergy destroyed in all cases. The results indicate that viewing the heat transfer and fluid flow phenomena in a bed of encapsulated spheres, it is of utmost importance to assess the major modes of entropy generation; in this case from heat transfer accompanying phase change.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".