An efficient method for modelling thermal energy storage in packed beds of spherically encapsulated phase change material
Bibliographic record
Abstract
An approach for modelling melting (and solidification) in packed beds of encapsulated spherical PCM is presented. The approach includes a calibration step based on comparisons of simulations with experimental results of PCM melting in a cylindrical geometry, followed by detailed simulations of melting in an isolated encapsulated PCM sphere and a PCM sphere in a representative elemental volume of packed PCM spheres to study the impact of external flow conditions on PCM heating and melting. The detailed simulations show that the enthalpy-porosity model provides a reasonable estimate of the overall melting time and a good approximation of the evolution of the liquid/solid melt front during the melting process. The simulations also confirm the dominance of convection during most of the melting process. The temporal integral results of overall heat transfer coefficient and overall energy fraction from the detailed simulations are combined to yield a relationship between overall heat transfer coefficient and overall energy fraction such that the process can be modelled in terms of the condition inside the encapsulated PCM sphere, wherein the condition can also be obtained from the fraction of heat absorbed compared to the energy capacity of the PCM sphere. This relationship is then used as an input to a finite-volume model for packed beds which can be used to predict the charging time of entire packed beds of encapsulated spherical PCM. Results from the simple model show correct trends for the temporal evolution of liquid fraction and PCM temperature, and for overall charging times for the cases considered. This simple approach can substantially reduce the simulation time required to model beds of different shaped encapsulated PCMs and different dimensions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".