Lessons Learned by the Fixed-Grid RANS TFG for HLPW-4 / GMGW-3
Bibliographic record
Abstract
View Video Presentation: https://doi.org/10.2514/6.2022-3211.vid The current state-of-the-practice technology for high-lift aerodynamic simulations is to solve the Reynolds-Averaged Navier-Stokes (RANS) equations on a fixed grid, or a refinement sequence of fixed grids. The Fixed-Grid Reynolds-Averaged Navier-Stokes Technology Focus Group set out to determine meshing requirements and best practices; whether RANS can accurately predict the change in aerodynamic performance with changes in flap deflection; whether RANS modeling can produce accurate results near CLmax ; and the effects of underconvergence and solution strategy on computed results. Eighteen groups of participants submitted over 100 datasets. Challenges with grid convergence and iterative convergence made it impossible to definitively answer all the questions we had posed. Despite this, we can conclude that meshes with at least half a billion cells (more than a billion degrees of freedom) are required for grid convergence away from stall; that RANS simulations cannot currently be reliably used to predict aerodynamic coefficients near stall, nor changes in coefficients with changes in flap angle; that iterative underconvergence remains a significant source of uncertainty in outputs; and that solution initialization can have an important effect on solution behavior, including flow separation patterns.
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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 | 0.014 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.034 | 0.017 |
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".