Revolutionizing the latent heat storage: Boosting discharge performance with innovative undulated phase change material containers in a vertical shell-and-tube system
Bibliographic record
Abstract
Abstract This paper examines the impact of various parameters, including frames, zigzag number, and enclosure shape, on the solidification process and thermal energy storage rate of a vertical phase change material (PCM) container. The study also assesses the effects of the flow rate of the heat transfer fluid as well as changing the materials of the PCM between RT35 and RT35HC. In addition, the study compares the framed versus unframed systems and, subsequently, the best case was tested with various zigzag pitch numbers before changing the zigzag-shaped structure to arc and reversed-arc. The findings are examined by contrasting the different scenarios’ liquid fractions, temperature distributions, solidification rates, and heat storage rates. The results show that the framed geometry is 66% faster to reach the target temperature compared with the unframed geometry and employing a zigzag enclosure in a PCM can significantly improve the solidification time and heat recovery rate. As the number of pitches in the zigzag enclosure increases, the improvement rate decreases but still improves the solidification time and heat recovery rate. The reversed-arc-shaped structure has the best performance compared with the other undulated surfaces. For the system with RT35HC, the discharge time is 55% higher compared with that of the system with RT35, while the discharge rate is 8.2% higher for the former during the first 3000 s of the discharging process.
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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 | 0.001 |
| Open science | 0.001 | 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".