CFD Simulations of Full Surface Passive Effusion Mass Injections in a Rectangular S-Bend Diffuser
Bibliographic record
Abstract
A coarse grid CFD methodology was employed to simulate internal flow passages with full coverage effusion cooling by imposing momentum sinks on effusion cooled surfaces based on a perforated plate pressure loss analogy. The methodology was implemented by specifying 1D Porous Jump boundary conditions (available in ANSYS FLUENT) on the effusion cooled surfaces. Numerical simulations were conducted based on the experimental data of an S-duct diffusing passage where ambient air was passively drawn into the sub-atmospheric passage along the different effusion surfaces with 1 mm diameter holes spaced 4 mm apart. The porous wall simulations were also compared to an alternative CFD approach with mass inlet boundary specified on the effusion surfaces. The proposed porous wall model is promising for practical design applications with the reasonable simulations of the S-duct flow fields with effusion injections. A reasonable accuracy in the results of injection mass flow rates was also obtained for the different effusion configurations. Discrepancies in the simulations of flow momentum components were mainly contributed to the diminishing effects of discrete injections at the aft-section of cooling surface due to the development of a shear layer across the free surfaces (porous jump boundaries) between the main flow and coolant flow.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".