Optimization of PVT-PCM Systems Through Heatsink Geometry: An Experimental Investigation
Bibliographic record
Abstract
The advancement of hybrid photovoltaic thermal-phase change material (PVT-PCM) systems has attracted considerable scholarly interest owing to their augmented dependability and operational efficiency.This research endeavor scrutinizes the efficacy of non-active thermal regulation methodologies for solar energy collectors, with a particular focus on employing paraffin-based phase change materials.These materials are distinguished by their inherent capacity to absorb and sequester thermal energy in the form of latent heat as they undergo phase transitions.The research was conducted experimentally, focusing on evaluating three different heatsink geometries: a basic model, a square model, and a hexagonal model integrated with PCM.Our results show that the hexagonal cooling model performs the best, capable of maintaining the solar panel's operating temperature at 56.2℃.Additionally, this model achieved the highest power output of approximately 38.11 W with an electrical efficiency of 12.82%.Overall, the hexagonal design produced the highest total daily power output of 256.63 Wh.The improvement in PVT-PCM system performance correlated with the increased heat transfer rate in the hexagonal model.This research emphasizes the significance of PCM heatsink design in optimizing the efficiency and thermal regulation of PVT-PCM systems, providing valuable insights for developing more effective and practical passive cooling solutions.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".