Predicting the High-Angle-of-Attack Characteristics of a Delta Wing at Low Speed
Bibliographic record
Abstract
Ensuring the safe operation of new supersonic transport aircraft requires understanding their stability during takeoff and landing. These phases involve flying at subsonic speeds and high angles of attack, where the aerodynamics are characterized by unsteady vortical flow. This work assesses the accuracy of Reynolds-averaged Navier–Stokes (RANS) and delayed detached eddy simulations (DDES) at predicting the vortex-dominated flow over a delta wing for angles of attack up to and past stall. The delta wing has an aspect ratio of 2 and is a simplified representation of a supersonic transport wing. The predicted aerodynamic coefficients are compared with experimental data, focusing on the shape of the pitching moment curve. In addition, a steadiness metric is formulated to distinguish between steady and unsteady angles of attack. It is found that RANS accurately predicts vortex effects in the steady regime but is inaccurate at high angles of attack where the flow is unsteady. DDES is more reliable in the unsteady regime, but the computational cost is at least 100 times that of RANS. Predicting the pitching moment at the highest angles of attack is difficult even with DDES on a 69-million-cell mesh. These results provide guidelines for choosing the appropriate fidelity depending on the flow characteristics, the required accuracy, and the computational budget.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".