An analytical prediction for charging–discharging cycles of metal foam composite phase change materials thermal energy storage
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".