Numerical and Experimental Investigation of Supersonic Binary Fluid Ejector Performance
Bibliographic record
Abstract
Ejectors are simple mechanical devices with no moving parts which convert the pressure energy of a motive fluid to kinetic energy and generate suction of the secondary fluid. The ability to recover waste heat, to operate using solar power and the ability to use geothermal energy make ejector-based systems attractive in different industrial applications. The main challenge of ejector-based refrigeration systems is their relatively low coefficient of performance (COP). In order to increase the ejector performance, two chemically distinct fluids can be used in the refrigeration cycle. It is suggested that a higher molecular mass be used for the motive fluid to improve the entrainment ratio of the binary fluid ejector (BFE) and thus the system COP. Inert gas combinations of argon–helium and krypton–air are studied in this paper using computational fluid dynamics (CFDs) and experimental measurements. All CFD cases were axisymmetric and the appropriate turbulence model was selected based on experimental validation. Specifically, the entrainment ratio and the static pressure along the ejector wall were measured to validate the CFD predictions. It was found that the molar entrainment ratio was significantly higher in argon–helium compared to krypton–air. The static pressure measurements along the wall, in addition, exhibited good agreement with the results obtained via computational fluid dynamics (CFDs).
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".