THE EFFECT OF THE MUSHY-ZONE CONSTANT ON SIMULATED PHASE CHANGE HEAT TRANSFER
Bibliographic record
Abstract
This paper presents a numerical study aimed at understanding the impact of the mushy zone constant, Amush, on simulated phase change heat transfer. This parameter is found in the Carman-Koseny equation which is used in the enthalpy-porosity formulation for modeling phase change; this approach models fluid flow within the mushy region as flow through a porous medium. The melting of lauric acid inside a rectangular thermal storage unit was simulated in COMSOL 4.4 and FLUENT 15.0; with Amush and the melting temperature range, ΔT, being varied per study. The simulated melt front positions were directly compared to experimental results presented by Shokouhmand and Kamkari [2013]. Results showed that Amush is an important parameter for accurately modelling phase change heat transfer; in particular, high Amush values corresponded to slower melting rates and the smallest Amush values resulted in unphysical predictions of the melt front development. Additionally, it was concluded that Amush and ΔT are not independent of one another in their roles of accurately modeling the melting rate; different values of ΔT would require different values of Amush to achieve the same melt front development. Further efforts are required to identify ideal values for these parameters, as well as to determine the extent to which these parameters hold for different materials and physical setups. It is anticipated that this paper will lead to further discussion on the significance of the mushy zone as a numerical technique for accurately modelling phase change heat transfer.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".