Simulation of transport phenomena in nanoparticle enhanced phase change materials for better performance
Bibliographic record
Abstract
• This article uses multiphase flow based CFD simulation to model Nanoparticles enhanced phase change materials. • The model is developed using OpenFoam for the purpose of studying the cyclic performance of NPCM based TES systems. • The nanoparticles volume fraction and the size of nanoparticles is studied parametrically. • The study suggest that the percentage enhancement of nanoparticles diminishes greatly after the first charging / discharging cycle. This study presents a computational fluid dynamics (CFD) simulation to evaluate sedimentation effects on thermal energy storage (TES) performance in nanoparticle-enhanced phase change materials (NPCMs). Utilizing a Volume of Fluid (VoF) multiphase flow model, the simulation examines the dynamic distribution, transport, and sedimentation of nanoparticles within the phase-changing thermal storage medium over consecutive charging and discharging cycles. This multiphase approach is essential for accurately capturing sedimentation effects, as single-phase models cannot simulate the distinct behaviors of nanoparticles and PCM during phase transitions. The study considers sedimentation, thermophoretic diffusion, and Brownian motion in the transport of nanoparticles. Results reveal that initial thermal conductivity enhancements from 5% and 10% NPCM concentrations reduce charging times by 17% and 22%, respectively, in the first cycle. However, nanoparticle sedimentation, primarily due to density differences, leads to performance declines in subsequent cycles. By the third cycle, the charging time increases to match that of pure PCM, with over 60% of larger nanoparticles sedimented at the bottom. These findings underscore the limited long-term benefits of nanoparticles in TES systems under these conditions, emphasizing the need for multiphase flow simulations to accurately assess cyclic 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 | 0.000 |
| 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 | 0.001 | 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".