Effect of Moisture Transfer Through a Semipermeable Membrane on Condensation/Frosting Limit
Bibliographic record
Abstract
This paper investigates frost formation on a flat horizontal surface, with humid air flowing over the surface and a cold liquid desiccant flowing below the surface. Two different surfaces, a semipermeable membrane and an impermeable plate, are tested. The condensation/frosting limit, that is, the lowest air humidity ratio, Wair, at a constant liquid temperature, Tliq, or the highest Tliq at a constant Wair that leads to condensation/frosting, is determined for each surface. The main aim of this study is to find the effect of moisture transfer through the semipermeable membrane on the condensation/frosting limit. It is found that the semipermeable membrane has a lower condensation/frosting limit, due to the moisture transfer through the semipermeable membrane, which dehumidifies the air flow. For a given Wair, the surface temperature can be approximately 5 to 8 °C lower when using a semipermeable membrane, compared to an impermeable plate, before condensation/frosting occurs. Furthermore, it is shown that at some operating conditions, frost appears on the semipermeable membrane only at the air flow entrance of the test section, while the impermeable plate was fully covered with frost at the same operating conditions. Moreover, it is shown that increasing the moisture transfer rate through the semipermeable membrane decreases the frosting limit and delays frost formation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".