Numerical studies on <scp>PCM</scp> 's contribution for thermal energy storage in continuous operation
Bibliographic record
Abstract
Abstract The utilization of phase change materials (PCMs) holds immense promise for thermal energy storage due to their high latent heat capacity. However, the design of advanced continuous operation systems is currently hampered by the lack of a systematic framework for selecting the optimal PCM, leading to risks of inefficiency and unpredictable performance. This study directly confronts this challenge by establishing clear selection criteria based on a numerical investigation of simultaneous melting and solidification. We analyzed three paraffin‐based PCMs (RT 50, RT 35, and RT 27) under constant source (348 K) and sink (273 K) temperatures. The results reveal that achieving a stable, continuous melt fraction depends not on a single property like latent heat, but on the complex interplay between thermal diffusivity, driving forces, and convection, as quantified by Rayleigh and Prandtl numbers. RT 50 exhibited the most stable performance, attaining a steady melt fraction of 0.21 after 180 s and an average PCM temperature of 304.9 K, with Rayleigh numbers of 425 × 10 8 (charging) and 756 × 10 8 (discharging). In contrast, RT 27 and RT 35 showed continuously increasing melt fractions of 0.36 and 0.34 at 200 s, driven by higher thermal gradients (46 and 40 K, respectively) and Prandtl numbers (45.6 and 230), indicating unstable long‐term operation. This work provides a crucial, evidence‐based methodology for designing next‐generation, high‐efficiency thermal storage systems.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 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".