Effects of segmentation in composite phase change material on melting/solidification performance of triplex‐tube thermal energy storage systems
Bibliographic record
Abstract
Abstract In this work, a numerical evaluation of the melting/solidification performance of phase change material (PCM) filled inside a triplex‐tube latent heat storage unit has been carried out. To enhance the melting/solidification performance, the porous Cu metal foam (MF) was embedded inside PCM (termed as composite PCM). Alternative segments of pure PCM and composite PCM have been allocated in such a way that both the pure PCM and composite PCM occupy the equal annular area (i.e., equal volumes). Influence of increasing number of segments was delineated on the melting/solidification rate, complete melting time, and thermal energy storage/recovery enhancement. The comparisons were drawn with reference to the model having two segments of PCM and composite PCM. The results show that the model containing 64 segments with alternate allocations of PCM and composite PCM has a faster melting/solidification rate than other models. With 32 alternate segments of MF, the full melting/solidification time reduced by 23%/77% with respect to the case with one segment of MF only. The melting/solidification performance gets saturated beyond 32 segments (M‐5) and negligible variation (only ~1%) in the thermal performance was noticed upon further segmentation. Finally, the model M‐5 proved as the best model considering the aspects of augmented melting/solidification rate and associated complexities. Moreover, the heterogeneity of MF applied in 32‐segment model confirmed that the anisotropic MF results in an increased melting rate and leads over other random isotropic distributions of MF.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".