Enhancement of Melting Process Inside Toroidal Tube Heat Exchanger With Different Cross-Sectional Geometries
Bibliographic record
Abstract
Abstract Utilizing phase change material (PCM) in concentric tube and shell-and-tube latent heat exchangers known as latent heat thermal energy storage (LHTES) have been extensively studied due to the high ability and density in storing energy during the melting (charging) process. Inadequate melting in these systems reduces the thermal performance of LHTES systems. To facilitate and accelerate the melting process, the innovative design of such systems is a key. The present study proposes novel designs of toroidal tubes embedded in the LHTES system as a latent heat exchanger. The effect of the cross-sectional geometry of the tube on the thermal performance of the system is investigated through simulation and comparison of different cross-sectional geometric shapes. A mathematical model based on the enthalpy-porosity approach is developed and numerically solved by the finite volume method to simulate the energy transport processes inside the system. Several transient heat transfer characteristics, e.g., thermal filed, melt fraction, Nusselt number, and energy storage during phase change, are determined and compared for all cases to evaluate their thermal performance and find the optimal geometry. The results indicate that downward triangular geometry for the cross-sectional shape of the tube shows the best performance as it significantly enhances the melting process, resulting in a faster energy storage rate during the charging process. Compared with the circular toroidal tube as the base geometry, the downward triangular shape design for the toroidal tube can improve the charging power of the system by 21%.
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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".