Thermodynamic Evaluation of Low-GWP and Environmentally Friendly Alternative Refrigerants
Bibliographic record
Abstract
Refrigerant systems, crucial for modern life, are increasingly important due to their environmental impact and rising energy costs, with their advancement influenced by social life evolution and widespread use in homes and buildings. A systematic search using thermodynamic models identified 48 possible ternary mixtures and 5 pure refrigerants. These combinations, based on thermodynamics, could provide energy savings, paving the way for real-world testing and definitive conclusions, not yet studied in literature. REFPROP refers to the reference fluid properties program, developed by NIST version 9.0 for 2010, is a program for calculating the thermodynamic and transport properties of industrially important fluids and their mixtures. This program was used to evaluate the refrigerant properties in different mixing ratios. Then, using the MATLAB version of 2020 apparatus to arrange and solve all the variables to generate the results under set boundary conditions, all the characteristics were incorporated into thermodynamic equations. when compared to R134a, the results demonstrated that mixtures of natural refrigerants usually have acceptable thermal performance; these mixtures may be recommended as suitable replacements for refrigeration and air conditioning systems because they are environmentally harmless and have a low GWP.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".