Effects of Heat Loss/Gain on the Transient Testing of Heat Wheels
Bibliographic record
Abstract
Heat wheels are used in ventilation systems to provide indoor thermal comfort by recovering considerable amount of sensible energy from exhaust airstream. The transient single step test is a new testing method developed to determine the sensible effectiveness of heat wheels. In practice, heat loss/gain may create large uncertainty in the sensible effectiveness obtained through the transient testing. In this study, the transient analytical model in the literature is extended to account for heat loss/gain effects in the transient testing. The results state that in particular operating conditions, the sensible effectiveness can be affected by more than 10% due to heat loss/gain. A new testing facility is developed to investigate the effects of heat loss/gain on the sensible effectiveness through transient testing of a small-scale heat exchanger. After decoupling heat loss/gain effects from transient test data, less than 2% difference was observed in the sensible effectiveness while supply and exhaust flow rate was small (Re < 209) and the temperature difference between them was ΔTst < 7.0 °C. However, the sensible effectiveness decreased more than 9% while ΔTst > 37.5 °C or Re > 600. An empirical correlation was proposed based on the transient test data that correlates the sensible effectiveness with the heat capacity rate ratio. Comparing the results of proposed correlation with literature, less than 2% difference was observed at the heat capacity rate ratio of greater than 0.5 after the heat loss/gain effects were decoupled from transient test data.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".