Pore-scale analysis of regular and irregular sintered wick structures in heat pipes
Bibliographic record
Abstract
This study employs pore-scale simulations to investigate permeability (k), maximum velocity (Umax), and average velocity (Uave) across three sintered porous structural configurations: irregular, regular, and uniform. The irregular structure, characterized by random particle sizes and arrangements, exhibited the highest permeability and maximum velocity, due to enhanced pore connectivity and size variability. In contrast, due to compact and evenly distributed pores, the uniform structure (fixed particle sizes and arrangements) exhibited the lowest values. The regular structure, with fixed particle sizes and random arrangements, achieved intermediate results, balancing the advantages of randomness with the constraints of uniform particle sizes. Pore-scale geometries were generated using MATLAB, and fluid flow properties were simulated in COMSOL Multiphysics. Clustering analysis of the irregular structure was performed to assess permeability, maximum velocity, and pore size distributions. The analysis identified three distinct clusters for all parameters: high-performance clusters associated with large interconnected pores, moderate clusters corresponding to transition zones, and low-performance clusters linked to smaller, isolated pores. For permeability, the high-performance cluster showed a significant increase, while smaller pores exhibited much lower permeability. Similar trends were observed for velocity and mean inter-particle distance, with the high-performance cluster consistently outperforming others. Statistical analysis using analysis of variance (ANOVA) confirmed significant differences among the configurations (p < 0.01 for k; p < 0.05 for Umax). The irregular structure consistently outperformed the other configurations across all evaluated parameters. Additionally, increasing porosity across all configurations improved permeability and velocity, stressing the pivotal role of pore connectivity in optimizing fluid flow pathways.
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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) | 0.000 | 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".