Results from the Fifth AIAA Drag Prediction Workshop obtained with a parallel Newton-Krylov-Schur flow solver discretized using summation-by-parts operators
Bibliographic record
Abstract
We present the solution of the test cases from the Fifth AIAA Drag PredictionWorkshop computed with a novel Newton-Krylov-Schur parallel solution algorithm for the Reynolds- Averaged Navier-Stokes equations coupled with the Spalart-Allmaras one-equation turbu- lence model. The algorithm employs summation-by-parts operators on multi-block struc- tured grids, while simultaneous approximation terms are used to enforce boundary condi- tions and coupling at block interfaces. Two-dimensional verification and validation cases highlight the correspondence of the current algorithm to established flow solvers as well as experimental data. The common grid study, using grids with up to 150 million nodes around the NASA Common Research Model wing-body configuration, demonstrates the parallel computation capabilities of the current algorithm, while the buffet study demon- strates the ability of the solver to compute flow with substantial recirculation regions and flow separation. The use of quadratic constitutive relations to modify the Boussinesq ap- proximation is shown to have a significant impact on the recirculation patterns observed at higher angles of attack. The algorithm is capable of efficiently and accurately calculating complex three-dimensional flows over a range of flow conditions, with results consistent with those of well-established flow solvers using the same turbulence model.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".