Experimental measurements and numerical computation of nanofluid and microencapsulated phase change material in porous material
Bibliographic record
Abstract
The electronic industry is increasingly investigating different approaches for the cooling of electronic equipment. The use of bulk phase change materials is also a promising approach for energy storage. The introduction of microencapsulated phase change materials combined with nanofluids can be beneficial. The combined use of a nanofluid and a metallic porous material can be used to mitigate problems resulting from small thermal conductivity. This study investigated a ternary mixture of water with a nanofluid and a microencapsulated phase change material in a porous medium. The model was previously validated with experimental data using a 0.5%vol concentration nanofluid in water. The results revealed that heat storage capability can be achieved as long as the microencapsulated phase change materials, which consists of encapsulated eicosane, is at a concentration of 20%. Because the melting temperature of microencapsulated phase change materials is approximately 36°C, energy storage at a low flow rate and heat flux is recommended.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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 teacher head, 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".