An analytical prediction for charging–discharging cycles of metal foam composite phase change materials thermal energy storage
Why this work is in the frame
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Bibliographic record
Abstract
The main drawback of phase change materials is their low thermal conductivity, resulting in poor thermal performance. Recent research has attempted to enhance heat transfer and increase the thermal conductivity of phase change materials, including the addition of metal foams. However, modeling metal foam composite phase change materials using conventional methods, such as numerical simulations, can be computationally expensive due to their complex structure and non-linear phase transition. This paper proposes a unified mathematical framework based on a two-phase Stefan problem subject to a time-dependent convective boundary in an annulus, capable of predicting both solidification and melting processes for charging and discharging metal foam composite phase change materials. Three physical stages, along with four temporal regimes and five spatial layers, are considered to forge asymptotic solutions around a small Stefan number. The effective thermal conductivity is calculated by a three-dimensional structured tetrakaidecahedron model, while other thermophysical properties are obtained through the method of volume averaging. The analytical results are compared with numerical solutions and validated against experimental data in the literature. The computational time is found to be up to 2 orders of magnitude faster than the enthalpy method for each cycle. Effects of porosity and Biot number on the solution are investigated, utilizing dimensionless temperature, interface motion, and solid fraction. Reducing porosity by 2% alone could decrease cycling times by over 25%. The novel analytical model provides an accurate yet computationally efficient prediction of the charging–discharging cycles of metal foam composite phase change materials through a unified mathematical framework.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 it