Experimental and Numerical Analysis of Phase Change Material (PCM) Heat Sinks and Determination of Mushy Zone Constant
Bibliographic record
Abstract
Electronic packages with increased performance and process capability and decreased in size have been developed daily. High-performance chips include more circuits and this causes an increase in heat dissipation. To remove this heat from component and reduce the risk of overheating and damage, heat sinks filled with phase change material (PCM) can be used as passive cooling device. PCMs can absorb and store large amounts of heat energy and thanks to this, temperature of electronic devices can be regulated at the melting point of PCM. Since performing experiments and testing procedure of prototypes are costly, difficult and time consuming, numerical modelling saves time and reduces cost of prototyping. During phase transition of PCM, mushy zone, which is a two-phase mixed region between solid and liquid regions, is observed. Numerical modelling of this region is critical for PCMs because this region affects whole flow characteristics and heat transfer of PCM. To model this region, mushy zone constant is used in ANSYS Fluent Solidification&Melting option. This constant is accepted as 10 5 for all PCMs in ANSYS Fluent, however, each PCM has a specific mushy zone constant, which should be determined before flow analysis. In this paper, a 4-chambered PCM heatsink with different PCMs are investigated experimentally and numerically. Mushy zone constants are determined from experiments and then applied as an input to numerical analyses. Different trademarked phase change materials with codes A58H, RT-65 and S72 PCMs are used in the experiments. Experimental results are used for the validation of numerical studies. CFD results show good agreement with experiments, the highest error is obtained as 11%. In addition, from experimental results, it is observed that PCM provides a 30 o C temperature drop and therefore increases the operational time of IC package.
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How this classification was reachedexpand
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 | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".