Extension of the Concepts of Heat Capacity Rate Ratio and Effectiveness-Number of Transfer Units Model to the Coupled Heat and Moisture Exchange in Liquid-to-Air Membrane Energy Exchangers
Bibliographic record
Abstract
The underlying concept of the standard effectiveness-number of transfer units (NTU) model is that the effectiveness of an exchanger can be correlated to two dimensionless parameters, namely, heat capacity ratio (Cr) and NTU. However, a limitation of this model is that it cannot account for the changes in effectiveness due to changes in operating temperature and humidity of simultaneous heat and moisture exchangers, specifically liquid-to-air membrane energy exchangers (LAMEEs). The purpose of this paper is to explain the reason for this limitation and also to explore the extension of the aforementioned concept of the effectiveness-NTU model to LAMEEs. The first contribution of this paper is to demonstrate that the reason for this limitation is that one of the simplifying assumptions of the standard effectiveness-NTU model, i.e., that Cr represents the ratio between the changes in the temperatures of the two fluid streams across an exchanger, is not applicable to LAMEEs. Further analysis in this paper yields two new fundamental dimensionless parameters that are analogous to Cr, termed effective Cr and effective m*, which represent the actual ratios between the changes in the temperatures and humidity ratios of the fluid streams. Then, it is shown that models analogous to the standard effectiveness-NTU model can be used to correlate the dependency of the effectiveness of LAMEEs on the operating temperature and humidity to effective Cr and effective m*.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".