Axial bent fins for the phase-change heat transfer enhancement in triplex-tube ice storage systems
Bibliographic record
Abstract
Fins are known as effective tools to compensate for the low thermal conductivity of phase change materials (PCMs) and increase the phase change rate in latent thermal energy storage devices. Numerous innovative fins have been designed and introduced in previous studies; however, fabrication complexity usually hinders these fins from entering the industry. The current work introduces a practical yet effective axial bent fin configuration to improve the rate of ice formation, saving time and operational costs in a triplex-tube ice storage system without imposing any complicated fabrication process for the fins. Through transient computational simulations, the influence of various fin parameters, such as the bend angle (30°, 60°, and 90°), direction (unidirectional and bidirectional), and location (near the roots, in the middle, and near the tips), on solidification is studied. It is essential to note that during these examinations, not only the PCM volume but also the heat transfer surface is kept constant. Based on the results, bending the fins with an angle of 60° in a bidirectional configuration and with a bend formed near the roots yields the best solidification rate. The findings reveal that the bend fins can offer up to 45.03% acceleration in the solidification rate compared to the finless case and up to 7.98% improvement compared to the case with conventional straight fins with the same heat transfer surface area and PCM volume. Considering that the fins are not complex, this approach can be a practical solution for industrial and commercial applications in thermal energy storage.
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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".