A Simple Design Approach of Two-Phase Ejectors for CO2 Transcritical Heat Pumps
Bibliographic record
Abstract
Integrating a two-phase ejector in mechanical vapor compression heat pumps is a practical and low-cost solution for improving performance and reducing energy consumption. Typically, using an ejector to recover part of the important pressure expansion losses in CO2 systems may improve the operating conditions of the compressor. One of the prerequisites for the success of such an application is the proper design of the ejector. This study is mainly dedicated to developing a simple approach for CO2 ejector design. The advantage of using the ejector as an expander in a transcritical CO2 heat pump is first introduced. Compressor operation is particularly improved. The development of an ejector design model for CO2 expanding from transcritical to two-phase conditions is presented. Validation of the thermodynamic model with experimental results from the literature shows the predictions to be within an acceptable range of discrepancy. The primary nozzle throat diameter calculations do not exceed ±8% of error for transcritical conditions. The error of the predicted pressure at the outlet of the ejector is in the limit of -15% to +3%. A practical design example for estimating the transcritical CO2 ejectors geometry integrated in a heat pump is presented. The results show the important decrease of primary nozzle diameters with the drop of Tevap, especially for the throat. A decrease of Dmix also occurs with Tevap and an optimal diameter is obtained for each condition considered. The design of the diffuser is based on a compromise between the outlet velocity and the length of the diffuser. The detailed design procedure with the proposed model, complemented with data from the literature, is a valuable tool for rapidly generating useful results and obtaining preliminary designs transcritical CO2 ejector.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".