Numerical investigation of heat transfer mechanism in an embedded PCM-TEG for better overall performance under the fluctuating heat source
Bibliographic record
Abstract
Using phase change materials (PCM) can effectively ensure the stable operation of semiconductor thermoelectric generators (TEGs). Conventional PCM-TEG structures exhibit relatively poor output performance due to energy barriers caused by low thermal conductivity. The embedded PCM-TEGs proposed in this study can address this issue well by adopting variable structure composite forms of PCM and TEG, resulting in a more complex heat transfer mode than traditional systems. To evaluate embedded systems' characteristics and performance enhancement methods, this paper first establishes a multiphysics mathematical model for embedded PCM-TEGs. Then, employing the TOPSIS method, an optimal embedded structure that considers system output and stability is obtained. Finally, we investigate the effects of key parameters on system performance, including PCM volume, latent heat, melting point, and metal-added composite materials. The CCC PCM-TEG (embedded PCM-TEG with PCM closer to the cold side) exhibits superior performance with a 28.6 % increase compared to conventional structures. For a given PCM volume, embedded structures with equal heights show better performance due to weak influence from asymmetric configurations on internal heat flow direction. Unlike traditional PCM-TEGs, metal-added composite PCMs in embedded structures reduce the temperature difference range across TEGs and thus deteriorate system performance.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".