Application of partial wire mesh and particle image velocimetry for rectangular phase change material containers as efficient thermal energy storage systems
Bibliographic record
Abstract
Abstract An experimental setup is built to improve the charging process of a bio‐based phase change material inside a rectangular latent heat thermal energy storage (LHTES) system. In addition to experimenting with different porosities of full‐coverage wire mesh, a novel method of partial wire mesh placement at a proper location is used for improving conduction and maintaining natural convection, proven by the particle image velocimetry (PIV) method. Although acquiring PIV images from coconut oil is challenging because of the laser reflection, the image capturing and processing have been completed successfully. Temperatures inside the enclosure were measured using four K‐type thermocouples, and the melt fraction was calculated using captured images. The full‐size mesh results in a more uniform temperature distribution, and its charging time for porosities of 88% and 82% is decreased by 41% and 52%, respectively. The results of partial wire mesh placements indicate that a top‐only approach leads to a time reduction of only 7%. However, the bottom‐only approach yields a time reduction of 34% and the value of 3.8 for the charging time reduction over the porosity reduction parameter, which is the highest among all experiments. Additionally, this placement eliminates sudden temperature spikes, which improves the performance of the LHTES system and results in an economical and practical configuration. Moreover, the PIV test shows sustained convective velocities at the top half of the enclosure with a partial mesh at the bottom, demonstrating improved natural convection compared to that of a full‐size mesh implementation which weakens convection. Therefore, contrary to full‐size meshes used in previous works, a bottom‐only mesh placement can enhance conduction and convection simultaneously, which can be utilized in practical applications.
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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".