An investigation into computational modelling of phase change materials using the enthalpy-porosity approach
Bibliographic record
Abstract
Purpose This study aims to examine the use of the enthalpy–porosity approach for simulating melting and solidification of paraffin-based phase change materials in the context of thermal energy storage. Design/methodology/approach This study describes a calibration exercise on a truncated cylindrical geometry that systematically investigates the sensitivity of the computational model to the mushy zone coefficient, the solidus/liquidus temperature separation and the convective intensity coefficient ci, which is introduced to account for the aggregate effect of remaining influences that are not individually modeled. Findings It was found that increases in the mushy zone coefficient and decreases in ci result in reduced intensity of convection. Moreover, it was observed that achieving a calibrated model requires consideration of all parameters collectively, rather than separately. The calibrated model was validated against experimental data for other heating conditions in the cylindrical cavity and in the prediction of melting in a rectangular cavity. The model is shown commute poorly across other heating conditions in terms of overall melting time but perform very well in other geometries subjected to the same heating conditions. When applied to solidification, significant discrepancies arise in terms of overall solidification time and in the temporal evolution of the interface separating the solid and liquid regions of the domain. Research limitations/implications When applied to solidification, significant discrepancies arise in terms of overall solidification time and in the temporal evolution of the interface separating the solid and liquid regions of the domain. It is thought that supercooling, differences in the solid–liquid interface and property dependencies on temperature may need to be considered for improved freezing process modeling. Practical implications This suggests that a calibrated model is well-suited for the development of heat transfer elements in applications like thermal storage where shape-optimization is paramount. Originality/value This suggests that a calibrated model is well-suited for the development of heat transfer elements in applications like thermal storage where shape-optimization is paramount. It is thought that supercooling, differences in the solid–liquid interface and property dependencies on temperature may need to be considered for improved solidification modeling.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".