Charging and discharging heat transfer enhancement in a latent thermal energy storage array using petal-shaped tubes and fins
Bibliographic record
Abstract
This work presents a comprehensive numerical investigation into the enhancement of heat transfer in latent heat thermal energy storage (LHTES) arrays using petal-shaped tubes combined with copper fins. A series of 19 configurations, varying the number of petals from 3 to 6, and fin arrangements were analyzed. The results show that increasing the number of petals from 3 to 6 reduces the melting time to reach a 0.9 melting volume fraction from 102 minutes to 60 minutes and solidification time to a 0.1 melting fraction from 200 minutes to 100 minutes. The optimal configuration (six-petal tube with horizontal fins) achieved a melting fraction of 94% after just 40 minutes, compared to only 91% for the best symmetrical fin case and 57% for the baseline circular tube, demonstrating a reduction in melting and solidification times by more than 50%. Further, the use of petal-shaped tubes with asymmetrical horizontal fins reduced total solidification time by nearly 78% relative to the baseline. This study provides clear design guidelines and quantitative benchmarks for optimizing LHTES units, showing that carefully engineered petal-shaped tubes and fin geometries can significantly advance thermal performance for practical applications in solar energy, waste heat recovery, and building heating systems.
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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".