Analysis of the Critical Conditions and the Effect of Slip in Two-Phase Ejectors
Bibliographic record
Abstract
In this study, it is proposed to lift the no-slip constraint imposed in the Homogenous equilibrium Model (HEM) for two-phase ejector design and analyse its effects on performance. Two models accounting for slip are used: the first, currently available in the literature is due to Moody and the second, developed by the authors is proposed as an alternative. Firstly, in order to avoid the direct computation of the velocity of sound in two-phase flow close to critical conditions, it is proposed to maximise the mass flow rate in the nozzle without recourse to the Mach number, since the computation of this latter in two-phase conditions has not yet gained consensus. Secondly, the introduction of a slip factor accounting for the velocity difference between vapour and liquid phases has allowed achieving remarkable improvements of critical flow computations, especially when using the newly developed approach by the authors. Thirdly a test facility for two-phase ejectors using R134a as refrigerant has been built for further studies. First results have allowed to validate the models predictions of the critical flow over a large interval of operating conditions. Lastly, analysis indicates that neglecting interphase slip may have a significant impact on two-phase ejector design. In this way and under some ejector inlet conditions, the prediction gap between HEM and the new model falls in the range of 13 to 23% in terms of compression ratio and in the range of 33 to 39% for the nozzle throat diameter.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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 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".