Experimental Performance Study of a Transcritical CO2 Heat Pump Equipped with a Passive Ejector
Bibliographic record
Abstract
This study is dedicated to an experimental investigation of a passive two-phase ejector used as an expander in a transcritical CO2 heat pump. The investigation focused on the impact of the evaporating temperature (Tevap) and the CO2 gas cooler outlet temperature (Tgc-out) on the ejector and the overall cycle performance. The basic cycle without an ejector was also tested as a baseline for comparison. Two ejectors designed with different modeling approaches were tested and compared. The ejector with an enlarged mixing section diameter was selected for subsequent testing due to its improved pressure lift. The optimum primary nozzle position was found to be 4 times the mixing section diameter (Dmix). Although the ejector was designed for specific conditions, the results demonstrate its ability to remain operational under varying conditions with some changes in performance. The ejector’s performance was observed to be dependent on the Tevap, and particularly on the Tgc-out. The pressure lift recorded was in the range of 3.7-6.5 bar, and the lowest value was obtained with the low Tgc-out value (29 °C). Under the tested conditions, the integration of the ejector enhances the performance and the capacity of the heat pump. The ejector cycle improvement is primarily based on improved mass flow rates due to increased compressor suction pressure, reduced compression ratio, and consequently, improved compressor operating conditions. Improvements of up to 18% in heating COP and 20.5% in heating capacity were observed. The study provides valuable insights into enhancing the performance of transcritical CO2 heat pump system by refining ejector design. It explores the behavior of the system across varying conditions, highlighting the significant impact of the ejector-compressor interaction on overall performance.
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.
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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".