Geometrical optimization of shell and tube latent heat thermal energy storage reinforced by thermal fins- sintered copper wicks packages
Bibliographic record
Abstract
The current study aims to improve the thermal performance of a shell and tube latent heat thermal energy storage (LHTES) using packages of the thermal disk fins and the sintered Copper wicks. In an axis symmetric vertical model, the Paraffin wax, placed inside the shell, affected by the cold/ hot Water flow crossing the central Copper tube, gets the phase change. The main focus is on the thermal disk fins covered by the sintered Copper wicks, where the best and the worst geometrical combinations can be found, leading to the maximum/ minimum thermal performance of the LHTES, respectively. Once, the sets of partial differential equations (PDEs) within the Copper tube including the clear area and the sintered Copper wicks were declared. Once again, the PDEs covered the clear area and the sintered Copper wicks inside the shell. Intriguingly, the local thermal non- equilibrium (LTNE) conditions between the Water/PCM and the solid matrix of the sintered Copper wicks was also assumed. The calculated power and the needed time to complete melting/solidification modes were the substantial criteria to measure each case. The findings describe that the case including the Copper wicks spread on the Copper tube and the narrowest as well as the longest fins gets an improvement percentage in the power, over 38%, in both the charging and the discharging modes against the initial designation. Moreover, using the thermal fins spread on the Copper tube can be equal a LHTES without any thermal fins. A latent heat thermal energy storage unit with fins and copper wicks
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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".