PREDICTION OF THE USE OF REFRIGERANTS IN LOW-TEMPERATURE EQUIPMENT
Bibliographic record
Abstract
Objectives Determination of prospects for the use of various refrigerants, as well as the potential for their interchangeability in low-temperature equipment in accordance with the conditions of the Kigali Amendment to the Montreal Protocol on substances that deplete the ozone layer. Method A computer simulation of heat exchange processes based on generally accepted dependencies was carried out and data for the construction of refrigeration machine elements obtained. Results R717 and R410A are recommended for use in medium- and low-temperature machines. R32 refrigerant is used in high-temperature refrigeration machines, especially in units with finned copper tubes. The low vapour content of R32 refrigerant prevents steaming of the upper layers of the tube bundle, leading to an increase in the level of the refrigerant in the evaporator and in the working area of the evaporator tube bundle. For R32, it is necessary to conduct additional research to find an alternative refrigerant. The highest values of the heat transfer coefficient are obtained when working on refrigerants R410A and R717. Conclusion The implemented algorithms can be helpful for obtaining the characteristics of the steam-compressor refrigerator elements across a wide range of boiling and condensing temperatures taking various factors and the percentage composition of the mixed working substance into account. This is a highly important consideration when converting the machines to run on alternative refrigerants.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".