Effect of Meniscus Recession on the Effective Pore Radius and Capillary Pumping of Copper Metal Foams
Bibliographic record
Abstract
An experimental study is performed to characterize the effect of meniscus recession on the effective pore radius and capillary pumping of copper metal foams which are to be used as wicks in heat pipes for electronic cooling. Knowledge of the effective pore radius is critical in defining the capillary pumping of a wicking material, but is rarely measured under operating conditions. It is known that the meniscus of a liquid recedes when evaporating from a porous media, which could impact the effective pore radius and therefore the capillary pumping capabilities of the foam. To elucidate this impact, the evaporation rate is measured from foam strips wicking ethanol from a reservoir while applying heat fluxes to the foam. Using thermocouple and IR camera measurements, the measured evaporation rates are corrected to account for different thermal losses, including natural convection, direct thermal conduction to the liquid, and evaporation from the container. An analytical model is then developed to relate the evaporated mass to the maximum capillary pressure (minimum effective pore radius) provided by the foam. It is shown for the first time, that just before the onset of dryout, the recessed meniscus will lead to 15%, 28% and 52% decrease in effective pore radius for samples with 68%, 75% and 82% porosities respectively. The capillary pumping therefore increases during evaporation. This can have significant impact on the prediction of the capillary limits in two phase capillary driven devices.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".