Run-Around Energy Recovery System for Air-to-Air Applications Using Cross-Flow Exchangers Coupled with a Porous Solid Desiccant—Part II: Results and Performance Sensitivity
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The model for the run-around energy recovery system was developed and verified for sensible and latent energy transfer in Part I (Li et al. 2009). In this paper, the sensible, latent, and overall effectiveness are investigated in each exchanger as well as the run-around exchanger. The overall effectiveness of the run-around energy recovery system is dependent on the airflow rate, the solid desiccant flow rate, the desiccant properties, specific surface area, the size of each exchanger, and the inlet air operating conditions. The run-around system can achieve a high overall effectiveness when the flow rates and exchanger's properties are properly chosen. Comparisons between the solid desiccant and salt solution run-around system effectiveness (Fan et al. 2006) show they are in agreement. In a sensitivity study, the thickness of desiccant on the fiber is investigated in the solid run-around system. It was found that a good performance is obtained with very thin desiccant coatings (1 or 2 μm). During the practical use of this system, a desiccant-coated fiber could be inserted into very porous balls or cages, protecting the desiccant-coated fiber from mechanical wear. The performance sensitivity for this kind of run-around system is demonstrated.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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 it