An automatic refrigerant circuit generation method for finned‐tube heat exchangers
Bibliographic record
Abstract
Abstract Design of finned‐tube heat exchangers or coils, and in particular their refrigerant circuits, depends primarily on experimental data and the associated development cost is in general rather high. Taking into consideration the high number of possible refrigerant circuits, the input of each circuit into a particular design computational tool would be a very time‐consuming operation. In the present work, we present a model to automatically generate refrigerant circuits for finned‐tube heat exchangers based on a recursive algorithm. The model assumes that a tube is only connected to those that are in its neighbourhood. In this way the total number of connecting bend tubes is a minimum and, as a consequence, material usage is at its lowest level. In addition, a finned‐tube heat exchanger performance simulation model is developed to evaluate the performance of a finned‐tube heat exchanger with the automatically generated circuit. This simulation model can handle arbitrary refrigerant circuits and it is sufficiently user‐friendly to allow the easy inclusion of new features. In addition, it can simulate coil performance by analyzing the coil into uneven length elements and by considering non‐uniform air distribution at minimal computational cost. The simulation results of the model have a maximum ±10 % error compared with experimental data, most within ±5 % deviation of the experimental data. The automatically generated circuits are evaluated on the basis of the heat exchanger performance simulation results. This combined feature has the potential of being a powerful tool in designing finned‐tube heat exchangers.
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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 | 0.001 |
| 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) | 0.003 | 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".