Energy and exergy efficiencies of different configurations of the ejector-based co <sub>2</sub> refrigeration systems
Bibliographic record
Abstract
Carbon dioxide (CO 2 ) is an appropriate replacement for conventional refrigerants due to its low global warming effects. However, its application within a traditional refrigeration compression cycle leads to low thermodynamic performance due to the large expansion losses in a throttling process. The application of ejectors allows reducing these losses. Many scenarios of ejector-based cycles have been proposed. Among them four different configurations may be distinguished: an expansion work recovery cycle (EERC), a liquid recirculation cycle (LRC), an increasing compressor discharge pressure cycle (CDPC) and a vapor jet refrigeration cycle (VJRC). This study deals with the comparative analysis of these cycles. In order to study the performance of the cycles, the numerical simulations are developed using EES software. Two performance criteria, energy efficiency (COP) and exergy efficiency are evaluated for each cycle. The highest values of these criteria point to the most thermodynamically efficient cycle. The results show that the EERC has the highest COP and exergy efficiency compared to other cycles. For example, the COP of the EERC is 3.618 and the exergy efficiency is 9.68%. The COP (resp. exergy efficiency) is approximately 23.3% (resp. 23.3%), 24.9% (resp. 25.5%) and 5.6 times (resp. 56.2%) higher than the corresponding energy and exergy efficiencies of LRC, CDPC and VJRC. Moreover, in comparison with a basic throttling valve cycle, the COP and exergy efficiency in EERC are higher up to 23% and 24% correspondingly. The detailed exergy analysis of EERC cycle has pinpointed the equipment where the major exergy losses take place. The largest losses occur in the evaporator (about 33% of the total exergy destruction of the cycle) followed by the compressor (25.5%) and the ejector (24.4%).
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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".