Analysis of a simple vapor compression and ejector refrigeration systems working with eco-friendly refrigerants
Bibliographic record
Abstract
Transitioning to alternative refrigerants with low Global Warming Potential (GWP) in both vapor compression and ejector refrigeration systems emerges as a viable strategy to address the environmental impact associated with refrigeration technologies. This shift necessitates a thorough examination of factors such as thermodynamic performance, safety considerations, and optimization of system design. The outcomes of this study contribute to the advancement of sustainable refrigeration systems, aligning with global initiatives to curb greenhouse gas emissions and preserve the environment. The study adopts a thermodynamic approach to numerically investigate several eco-friendly refrigerants with GWP below 150, including R1234yf, R1234ze, R1270, R152a, R290, and R600a, as potential alternatives for vapor compression and ejector refrigeration systems. Thermodynamic models, developed in MATLAB using refrigerant properties, reveal that R600a and R290 exhibit promising potential as replacements for R134a in vapor compression refrigeration systems. These alternatives demonstrate a noteworthy improvement in the thermodynamic coefficient of performance, with percentages of 2.47% and 2.12%, respectively, under similar working conditions. For ejector refrigeration systems, R152a, R717, and R1270 exhibit enhanced coefficients of performance, contributing to significant savings in generator heat load. The results highlight the ability of these refrigerants to improve both the efficiency and sustainability of refrigeration systems in diverse applications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".