Effect of fin properties and positioning on phase change material (PCM) thermal behavior: A numerical study
Bibliographic record
Abstract
This study presents a comprehensive numerical investigation into the melting behavior of a Phase Change Material (PCM), specifically Lauric Acid, within latent thermal energy storage systems . To accomplish this, the Enthalpy-based method was employed to model the phase change process , ensuring accurate tracking of the solid-liquid interface and latent heat effects. To enhance numerical stability and accuracy, the convection terms in the momentum and energy equations were discretized using a third-order TVD flux-limiter scheme, while the pressure-velocity coupling was managed via the hybrid unsteady PISOR algorithm. Following extensive validation, the code was used to systematically study the effects of key design parameters, including the position and material of conductive fins and the configuration of embedded heated pipes. The results revealed that placing a fin at the lower portion of the heated wall significantly accelerates the melting process by enhancing buoyancy-driven convection and eliminating heat-trapping zones. Among the fin materials examined, Copper exhibited the highest thermal performance, followed closely by iron, while Nichrome demonstrated poor heat transfer characteristics due to its low conductivity. The simulations also showed that Rayleigh-Bénard convection plays a critical role in shaping the solid-liquid interface, resulting in wavy interfacial patterns and oscillatory Nusselt number behavior, whereas conduction-dominated regions exhibited thicker, slower-evolving interfaces due to limited convective motion.
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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 | 0.000 | 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) | 0.000 | 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".