Measurement of Heat Transfer Enhancement and Pressure Drop Across Eddy Promoter Air Screens in a Liquid-to-Air-Membrane Energy Exchanger (LAMEE)
Bibliographic record
Abstract
In liquid-to-air membrane energy exchangers (LAMEEs), the heat and mass transfer resistances in the air channel are dominant. An eddy promoter air screen can effectively enhance the heat and mass transfers in the air channel. In this study, the heat transfer enhancement and pressure drop across three different eddy promoter air screens in an air channel are experimentally investigated. Eddy promoter air screens are comprised of plastic ribs in the stream-wise direction and aluminum cross-bars normal to the air flow direction. A low speed wind tunnel test facility, which simulates the air channel of a LAMEE is designed to measure the friction factor and enhanced convective heat transfer coefficient in the air channel with an eddy promoter air screen. Tests were conducted at Reynolds numbers of 920, 1550, and 2160. In this paper, the effects of the spacing of the cylindrical bars and plastic ribs on the heat transfer performance are studied experimentally. Also, the performance of eddy promoter air screens as a function of enhanced heat transfer coefficient and increased pressure drop is investigated. Results show that the eddy promoter air screens have the highest efficiencies at Reynolds of 1550 and double the convective heat transfer coefficient of the air with respect to a smooth channel.
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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 | 0.001 | 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) | 0.000 | 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".