Coupled ϵ-NTU Method to Design and Evaluate the Performance of Energy Exchangers With Coupled Heat and Mass Transfer
Bibliographic record
Abstract
Abstract The classical ϵ-number of transfer units (NTU) method is widely used to design and evaluate the performance of heat and mass energy exchangers. In energy exchangers, where the heat and mass transfer are coupled, i.e., the magnitude of heat transfer impacts the magnitude of mass transfer and vice-versa, the classical ϵ-NTU method fails to capture the outlet fluid conditions of the energy exchanger accurately. It cannot be used for designing/evaluating the performance of energy exchangers where heat and mass transfer are coupled. The coupled ϵ-NTU model uses modified heat and mass capacity ratios to capture the effects of coupled heat and mass transfer. The use of the coupled ϵ-NTU model to design and evaluate the performance of energy exchangers is illustrated, specifically on a liquid-air-membrane energy exchanger (LAMEE), but the model can be extended to other coupled energy exchangers. The coupled ϵ-NTU model is validated using a numerical model of a LAMEE in counterflow and crossflow configurations. The validation is completed for over 14,500 test points representing a wide range of operating conditions. The average error in estimating sensible and moisture transfer effectiveness using the coupled ϵ-NTU method is less than ±1.5% for both configurations, compared to the numerical model illustrating the robustness of the coupled ϵ-NTU model. Of the 14,500 tested points, the error in estimating sensible or moisture transfer effectiveness is greater than 4% for less than 5% of the test points.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".