Transcritical CO2 refrigeration systems enhanced by ejector technology: state-of-the-art review
Bibliographic record
Abstract
• Comprehensive state-of-the-art review of ejector-integrated transcritical CO 2 systems. • Ejector integration improves COP by 10–25%, reaching up to 40–60% in hybrid cycles. • CFD–ML surrogate models reduce entrainment-ratio and pressure-lift errors to < 3 %. • Multi-ejector and VGE systems sustain COP under large ambient-temperature variations. • Key research gaps identified: standardization, long-term reliability, and smart control. The demand for sustainable and environmentally benign refrigeration technologies has accelerated the adoption of carbon dioxide (CO 2 ) as a natural refrigerant. Despite its thermodynamic benefits and negligible global warming potential, the use of CO 2 in transcritical refrigeration cycles is constrained by significant inefficiencies, particularly related to throttling losses and high discharge pressures. Ejector technology has emerged as a potential addition mechanism that could enhance the overall cycle performance by recuperating the expansion work and distributing the pressure to optimal points. This review paper gives an in-depth and critical description of ejector-integrated transcritical CO 2 refrigeration systems. It explores the basics of ejectors, including ejector-based system configurations, their performance enhancement, control strategies, and industrial applications. Quantitative analyses from recent studies indicate that ejector integration can improve the system Coefficient of Performance (COP) by 10 to 25 % compared with conventional throttling cycles, while hybrid designs employing internal heat exchangers or parallel compression achieve gains up to 40 %. In addition, recent developments such as Computational Fluid Dynamics (CFD) and machine learning, are also discussed. The integration of CFD and ML frameworks has reduced prediction errors in the entrainment ratio and pressure lift to below 3%. Critical gaps are found in standardization, long-term reliability, and smart system integration. The review outlines preliminary directions including the establishment of unified testing protocols, the development of long-duration reliability studies, and the design of adaptive, sensor-integrated ejector systems for intelligent control. This review is cross-disciplinary and systematic in its scope to the critical role ejector technology has played in enhancing the development of high-efficiency and low-emission refrigeration technology.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".