Performance Assessment of Nano-enhanced Phase Change Material for Thermal Storage
Bibliographic record
Abstract
The use of nano phase change material in thermal energy storage applications appears promising, but the often-poor performance and the lack of understanding of the heat transfer mechanisms interconnectedness remains a challenge and hinders their widespread integration. The existing numerical work has unveiled numerous impediments in predicting the actual melting behaviour. They rarely combine the effects of conduction enhancement, convection degradation, and latent heat reduction, due to inaccurate characterization of the thermophysical properties and the limitations of their model assumptions. In the present study, an enhanced numerical approach was developed to investigate the melting performance of xGnP-octadecane filled in a vertical cylindrical enclosure at different weight concentrations. The model results for the pure phase change material were compared and validated against the experimental data. The progression of the melting front, temperature probes, energy storage capacity and heat transfer rate of the nano phase change material were thoroughly evaluated. The current numerical observations demonstrate that the addition of nanoparticles improves, up to a critical concentration of 0.5wt%, the melting rate. The results showed that by adding 0.5wt% of xGnP in the base phase change material (octadecane), the melting rate decreases by 9.7% and the heat storage rate increases by 12.6%. However, at higher loadings, the heat transfer rate is deteriorated due to worsening of other thermophysical properties provoking the prevalence of viscous forces over natural convection and latent capacity. The system overall efficacy was found to be dependent on the net effects of relative changes of all thermophysical properties with nanoparticle concentration and temperature in the solid, so called mushy, and liquid zones. Finally, when characterizing nano phase change material, the thermal conductivity cannot be considered alone as a criterion for nano phase change material selection. A high thermal conductivity is needed for maximum heat absorption in thermal transport applications. Nevertheless, low viscosity, high latent heat and specific heat capacities are also essential to ensure a better thermal energy storage efficiency in terms of capacity and heat extraction/release rate.
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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) | 0.001 | 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".