Melting phase change heat transfer in a quasi-petal tube thermal energy storage unit
Bibliographic record
Abstract
In the present study, the thermal energy storage of a hot petal tube inside a shell-tube type Thermal Energy Storage (TES) unit was addressed. The shell is filled with the capric acid Phase Change Material (PCM) and absorbs the heat from a hot U-tube petal. The governing equations for the natural convection flow of molten PCM and phase change heat transfer were introduced by using the enthalpy-porosity approach. An automatic adaptive mesh scheme was used to track the melting interface. The accuracy and convergence of numerical computations were also controlled by a free step Backward Differentiation Formula. The modeling results were compared with previous experimental data. It was found that the present adaptive mesh approach can adequately the melting heat transfer, and an excellent agreement was found with available literature. The effect of geometrical designs of the petal tube was investigated on the melting response of the thermal energy storage unit. The phase change behavior was analyzed by using temperature distribution contours. The results showed that petal tubes could notably increase the melting rate in the TES unit compared to a typical circular tube. Besides, the more the petal numbers, the better the heat transfer. Using a petal tube could increase the charging power by 44% compared to a circular tube. The placement angle of the tubes is another important design factor which should be selected carefully. For instance, vertical placement of tubes could improve the charging power by 300% compared to a case with the tubes' horizontal placement.
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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".