Influence of Pore Density and Porosity on the Melting Process of Bio-Based Nano-Phase Change Materials Inside Open-Cell Metal Foam
Bibliographic record
Abstract
In this paper, experimental investigations were carried out to observe the melting process of a bio-based nano-phase change materials (PCM) inside open-cell metal foams. Copper oxide nanoparticles with five different weight fractions (i.e., 0.00%, 0.08%, 0.10%, 0.12%, and 0.30%) were dispersed into bio-based PCM (i.e., coconut oil) to synthesize nano-PCMs. Open-cell aluminum foams of different porosities (i.e., 0.96, 0.92, and 0.88) and pore densities (i.e., 5, 10, and 20 pores per inch (PPI)) were considered. An experimental setup was constructed to monitor the progression of the melting process and to measure transient temperatures variations at different selected locations. Average thermal energy storage rate (TESR) was calculated, alongside the melting time was recorded. The effects of various nanoparticles concentration, metal foam pore densities, porosities, and isothermal surface temperature on the melting time, TESR, thermal energy distribution, and the melting behavior were studied. It was observed that the melting time significantly reduced by using metal foam and increasing the isothermal surface temperature. It was concluded that the effect of adding nanoparticles on the TESR depends on the characteristics of metal foam, as well as, the weight fractions of nanoparticles. The change in TESR varied from −1% to 8.6% upon addition of 0.10 wt % nanoparticles compared to pure PCM, whereas the increase in the nanoparticles concentration from 0.10% to 0.30% changed TESR by −10.6% to 4.5%. The results provide an insight into the interdependencies of parameters such as pore density and porosity of metal foam and nanoparticles concentration on the melting process of nano-PCM in metal foam.
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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 | 0.000 | 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 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".