Performance Analysis and Optimization of an Ejector Refrigeration System Using Alternative Working Fluids under Critical and Subcritical Operation Modes
Bibliographic record
Abstract
Ejector systems are receiving considerable attention due to their simplicity, lower maintenance requirements, use of low grade heat, longer lifespan and low cost. In this paper an improved model to predict the performance of an ejector refrigeration system under both the critical and subcritical modes of operation was developed and validated. The model predicts ejector performance more precisely compared to studies following the same modelling approach in the literature. Using the developed model, performances with environmentally benign refrigerants, including R1233zd(E), HFO1336mzz(Z), R1234ze(Z), R600, RE245fa2, and RE245fa2 as alternatives to R141b and R245fa were investigated. For ejector area ratios between 4.45 to 12.98, evaporator temperatures between 0oC and 16oC and condenser temperatures between 20 and 40oC, the optimal performance of the ejector system was determined. Results show that for each refrigerant, higher area ratios give higher coefficients of performance, but require higher generator temperatures for better critical condensing temperatures. R600 showed the best performance followed by R1234Ze(Z) and R1233Zd(E) for the entire range of parameters considered. Results further show that there is an optimum generator temperature at each area ratio that maximizes performance. The optimal generator temperature increases as the area ratio and the condensing temperature increase. An alternative and more convenient approach to optimize ejector performance has been suggested in this work.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".