Inlet CFD Results: Comparison of Solver, Turbulence Model, Grid Density, and Topology
Bibliographic record
Abstract
AIAA’s first Propulsion Aerodynamics Workshop (PAW) was held in 2012 at the Joint Propulsion Conference (JPC) in Atlanta. Representatives from academia, industry and commercial CFD tool vendors were encouraged to participate and simulate a serpentine inlet duct (S-duct), and a family of convergent nozzles for a blind comparison of CFD results with test data. Most of the test data was kept private until the conclusion of the workshop. Lockheed Martin Aeronautics Company (LM Aero) was among the participants from industry, and provided results for the S-duct and the nozzles. LM Aero’s results for the S-duct are summarized in this paper. LM Aero’s comparison included results from a commercial code as well as a code developed internally. The commercial code was CFD++ from Metacomp Technologies, Inc., and the in-house code was LM Aero’s Falcon CFD tool. Both CFD codes were applied to the same family of grids, which included three structured hexahedral grids at different mesh densities, and three tet-prism hybrid grids at different mesh densities. Two turbulence models were compared with each solver on each grid. This resulted in 24 cases in the primary solution set (6 grids with 4 solutions per grid). The final comparisons showed that both codes performed well and predicted the stagnation pressure recovery at the Aerodynamics Interface Plane (AIP) to within 1% in general, and to within 0.1% in certain cases (beyond the precision of the test data). The predicted distortions matched test data slightly less favorably, but compared well with other participants. Also summarized are the guidelines used to reliably obtain a converged result. Figure 1 depicts the S-duct and nozzle geometry.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".