Bibliographic record
Abstract
An experimental investigation was carried out for frost growth in a desiccant-coated regenerative wheel. The test facility was set up following ASHRAE Standard 84-1991R. Temperature, relative humidity, mass flow rate, and pressure drops were measured at each measuring station. Photos of frost within energy wheel flow channels show frost accumulation. The problem of frost growth within the narrow parallel flow passages of a regenerative heat or energy rotary wheel is formulated for a very cold-temperature ventilation application. Frost growth is assumed to grow as a porous media while the wheel is exposed to warm humid airflow on the exhaust side. While the wheel is exposed to cold dry airflow on the supply side, the frost is cooled but no frost grows. This cyclic frost growth and cooling process is continued with each wheel rotation. An analytical/numerical model is developed to simulate these frost properties over the depth of the wheel and as a function of time. Simulation results are used to interpret experimental data for the early stage of frost growth on a typical energy wheel with a cold supply air temperature of −40°C, a warm exhaust temperature of 20 °C and 40% relative humidity. Pressure drop measurements across a wheel taken for constant mass flow conditions revealed some very significant fluctuations of up to 100% of original pressure drop with a period ranging from 2 to 4 min for a wheel speed of 20 rpm. Each fluctuation in pressure drop is interpreted to imply a catastrophic failure of the outer frost layer sequenced over 1–2 min throughout the wheel followed by another frost growth period on top of a slightly thicker frost base.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".