CFD Study of a Variable Flow Geometry Radial Ejector
Bibliographic record
Abstract
To achieve higher performance from ejectors at some working conditions, implementations of variable geometry might be possible.While axisymmetric ejectors with axial flow paths have limitations that make practical implementation of variable geometry difficult, radial ejector configurations have a flow path that is conducive to changes in nozzle and ejector throat area during operation.The geometric adjustment of the radial ejector could be made by simply changing the separation of the radial ejector duct walls and/or the separation of the nozzle walls in order to optimize performance over a range of different conditions.The effects of such changes on the performance of a radial ejector have been investigated using a Computational Fluid Dynamic (CFD) analysis with ANSYS FLUENT software.Axisymmetric CFD models were generated to assess performance for a primary nozzle throat area of 8.792 mm 2 and for ejector throat separations of 2.2 mm, 2.4 mm and 3.0 mm, corresponding to ejector throat areas of 497, 543 and 678 mm 2 , respectively.The CFD analysis reveals that changes ejector performance can be achieved by changing the ejector duct's separation.An increase of 34% in entrainment ratio can be achieved by increasing the ejector throat separation from 2.2 mm to 3.0 mm at fixed primary and secondary pressures of 160 kPa and 1.8 kPa, respectively.If an increase in the ejector malfunction pressure is needed, it could be achieved by decreasing the ejector duct separation.An overall malfunction pressure increase of 18% can be achieved by decreasing the ejector throat separation from 3.0 mm to 2.2 mm at primary and secondary pressures of 250 and 1.8 kPa, respectively.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| 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".