Experimental and Numerical Study on the Thermal Performance of a Vertical PCM Panel
Bibliographic record
Abstract
Due to an increased awareness of climate change and other environmental issues, methods to reduce the energy consumption of buildings has become of great importance. One way to improve the efficiency of a building is to use thermal storage material. A recent thermal storage strategy is to use phase change material (PCM) which allows for the storage and release of thermal energy. One of the main advantages of using PCM over traditional thermal storage (like concrete) is that PCM can achieve the same level of thermal storage as concrete while using less material. Using PCM can also reduce and delay peak load, improve the thermal comfort, and reduce the overall energy consumption of a building. One of the main parameters that affect the performance and effectiveness of PCM in buildings is the convective heat transfer between a PCM wall and room air. Current convective heat transfer coefficients used in whole building simulation and in building codes (such as ASHRAE) may not be adequate for PCM applications. The present study investigates thermal performance of a vertical PCM panel. The investigation includes experiments using laser MachZehnder Interferometry (MZI) and a comparative numerical study using computational fluid dynamics (CFD). The study focuses on a vertical flat plate filled with PCM (soy wax) undergoing transient convective heat transfer by natural convection while the PCM solidifies. A novel method was developed to make interferometric surface temperature measurements using partial fringes as a reference temperature. The experimental results show a deviation from predicted heat transfer coefficients from established correlation and significant subcooling was observed as a temperature jump. The subcooling effect was reproduced using CFD by implementing the speed of crystallization within the PCM cavity.
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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) | 0.002 | 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".