A Numerical Study of a Wavy Fin and Tube CO<sub>2</sub> Evaporator Coil
Bibliographic record
Abstract
Carbon dioxide is among the promising natural refrigerant alternatives to HCFCs and HFCs for refrigeration. In the perspective of this development, a numerical model for dry evaporator coil design and simulation, based on correlations for carbon dioxide, is presented, along with typical cooling coil simulations. The model uses the NIST database for refrigerant properties and provides adequate flexibility for local parameter calculations across the coil. The heat transfer and pressure drop data used to validate this model originate both from a dedicated test bench built in our laboratories and from other sources. These data were predicted satisfactorily over the operating range corresponding to refrigeration applications. Apart from the air side pressure drop, which is predicted with a maximum uncertainty of 25%, a comparison between experiments and calculation are within 1°C for air and CO2 outlet temperatures and within 13.5% for capacity and CO2 pressure drop. Simulations at low and moderate temperatures were performed on coil configurations typically used in supermarket applications. Key parameter distributions, including temperatures, pressures, and relative humidity, were tracked inside and outside the tube coil. Tube relative positions in the coil largely influenced phase repartition and overall operation. The resulting air temperature distribution gradients were also affected.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".