Investigation of Annular Jet Flows in a Subsonic Air-Air Ejector With Multi-Ring Entraining Diffuser
Bibliographic record
Abstract
The current paper presents a cold flow simulation study of a low Mach number air-air ejector with a four ring entraining diffuser that is used in a variety of applications including infrared (IR) suppression of exhaust from helicopters and fixed wing aircraft. The main objectives of this investigation were to identify key issues that must be addressed in successful CFD modelling of such devices, and recognize opportunities to improve the performance of these devices. Two-dimensional CFD simulations were carried out using commercial software, Ansys14. Studies of mesh and domain size sensitivity were made to ensure the CFD results were independent of both factors. A turbulence model independence study using k-ε, k-ω and RSM turbulence models was performed to figure out the appropriate turbulence model that produced the best agreement with the experimental data for several of ejector performance criteria. The measured flow properties in the annulus were used as input boundary conditions for the CFD simulations. However, in the comprehensive turbulence model study, the measured flow parameters at the nozzle exit were also applied as inlet boundary conditions for the CFD simulations. The measured flow velocity at the nozzle exit, at one centerline section inside the mixing tube and at the diffuser exit and the system pressure recovery were compared with the CFD predictions. The ejector pumping ratios, back pressure coefficient and diffuser gap velocities were also compared. It was found that the RANS-based CFD predictions were sensitive to the changes in the ejector domain size, mesh refinement and inlet boundary condition locations. With the annulus inlet boundary conditions, the tested turbulence models under predicted the size of the core separation downstream of the system, back pressure, pumping ratio and pressure recovery in the mixing tube and diffuser. However, the ability of the RNG turbulence model to predict the ejector performance parameters was better than that of the other turbulence models at all inlet flow conditions. Nevertheless, applying the inlet boundary conditions at the nozzle exit enhanced the capability of the RANS-based turbulence model particularly in predicting the ejector pumping ratios, pressure recovery and the size of the core separation. Finally, the acceptable agreement between the experimental data and the CFD predictions provides a valid tool to continue improving these devices using CFD techniques.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".