Frosting on porous membranes in energy exchangers
Bibliographic record
Abstract
A liquid-to-air membrane energy exchanger (LAMEE) is a device that uses a semi-permeable membrane to transfer heat and moisture between an air stream and a liquid stream. In cold climates, they can be used to dehumidify an air stream and reduce or even prevent frost formation inside the exchanger. Understanding the mechanisms of frosting and the frost limits on membranes is essential for advancing the applications of LAMEEs in cold conditions. This paper combines a review of previously published research on the growth of frost on a membrane surface, compared to an impermeable surface, along with new experimental results that extend the applications of the frost research to cover more air temperature and relative humidity (RH) conditions. Frost limit maps are created for various operating conditions using an analytical model and verified with experimental results. These maps indicate the liquid temperature and air RH values that will result in frost conditions on the membrane surface. It was found that when the air temperature is 23°C, the liquid temperature could be lowered by 2°C-3°C at a constant RH level without frost appearing on the surface as compared to an impermeable surface, and when the air temperature is 0°C, the liquid temperature could be lowered by approximately 5°C compared to the impermeable surface. The results show that a porous membrane has great potential to create frost-free energy exchangers.This article is part of the theme issue 'Heat and mass transfer in frost and ice'.
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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 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".