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Record W7117659700 · doi:10.1016/j.ecmx.2025.101500

Transcritical CO2 refrigeration systems enhanced by ejector technology: state-of-the-art review

2025· article· en· W7117659700 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueEnergy Conversion and Management X · 2025
Typearticle
Languageen
FieldEngineering
TopicRefrigeration and Air Conditioning Technologies
Canadian institutionsSAIT Polytechnic
Fundersnot available
KeywordsTranscritical cycleBandwidth throttlingRefrigerationInjectorCoefficient of performanceWork (physics)RefrigerantHeat exchangerCooling capacity

Abstract

fetched live from OpenAlex

• 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.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.912
Threshold uncertainty score0.297

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.003
GPT teacher head0.193
Teacher spread0.190 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it