A thermodynamic investigation and optimization of an ejector refrigeration system using R1233zd(E) as a working fluid
Bibliographic record
Abstract
The increasing contribution of heating, ventilation, air conditioning and refrigeration systems to energy consumption and global warming is driving the search for technologies that are energy efficient, clean and renewable. The ejector refrigeration system is one such system with the potential to reduce energy consumption and CO 2 emissions. It is simple, low cost, has no moving parts, and can use low grade heat sources such as solar or waste heat. However, the coefficients of performance (COP) of these systems are still low. Moreover, most studies on ejector refrigeration systems have used refrigerants that are not environmentally benign. In this paper, the performance of an ejector refrigeration system working with R1233zd(E) is numerically investigated. R1233zd(E) is a newly introduced Hydroflouroolefin refrigerant with no ozone depletion potential and very low global warming potential. No studies on the performance of this refrigerant in ejector systems have been conducted. A novel mathematical model that uses ejector coefficients which are dependent on the evaporator and generator pressures to accurately determine performance was used in the present study. In the analysis, area ratios between 6.44 and 10.94, evaporator temperatures between 0 and 16°C, and generator temperatures between 70 and 110°C were considered. Results show that system performance in the critical mode of operation increases as the generator temperature reduces and as the evaporator temperature increases. Furthermore, there is an optimal generator temperature at each condensing and evaporator temperature with the highest COP. Correlations for the optimal generator temperature and optimal COP have been derived and presented.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".