Modeling of an air-to-air exchanger with dual-core in cascade connection
Bibliographic record
Abstract
With the current leaning towards finding effective solutions to maintain a good indoor air quality (IAQ) inside houses and buildings and to simultaneously reduce the energy consumption, air-to-air exchangers with heat/energy recovery have emerged as one of the promising technologies to provide a healthy and comfortable indoor environment. To deeply evaluate these systems performances, the present investigation focuses on modeling a combined ERV-HRV exchanger using the effectiveness-NTU (ε−NTU) method. To this end, a detailed mathematical model introducing heat and mass exchange mechanisms was developed and applied to predict the system energy recovery efficiency. To assess its suitability, the developed model was validated and compared to real measurements which carried out under the Atlantic Canada weather. The comparison findings disclosed that the developed model can predict the system performance with a maximum relative discrepancy less than 10%. • The detailed mathematical model including heat and mass exchange mechanisms was clearly developed using the ε−NTU approach in order to carefully predict the dual-core system performance in terms of sensible and latent recovery potential. • The developed model was validated against real data to evaluate its suitability and accuracy. Obtained results show that it could be satisfactory for predicting the dual-core system performances. • The ε−NTU approach adopted in this study could be a convenient method for modeling single or/and dual-core air-to-air heat/energy recovery systems.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".