Higher Order Two Dimensional Aerodynamic Optimization Using Unstructured Grids and Adjoint Sensitivity Computations
Bibliographic record
Abstract
*† We present early results from an aerodynamic optimization scheme based on a high-order accuracy finitevolume solver. The flow solution sensitivity is calculated using the adjoint approach; the higher order method is shown to be more accurate in calculating sensitivity values than traditional second order accurate computations, when each is compared to the finite difference sensitivity for a flow solver of the same order of accuracy. We take advantage of the exact Jacobian matrix to simplify this process. To avoid re-generating the grid around the airfoil for each optimization iteration, we instead deform the mesh when the geometry is change. We use the semi-torsional mesh movement scheme because of its simplicity and robustness. We use The Quasi-Newton optimization line search method with BFGS approximation of the Hessian matrix as an optimization scheme. We present two unconstrained optimization test cases: one with angle of attack as the sole design variable, and the other an inverse design shape optimization problem. Both the 2 nd and 4 th order schemes reach their corresponding optimal solutions with identical optimization convergence rates. The 2 nd and 4 th order schemes produces similar airfoil shapes for the inverse design test case in subsonic conditions.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".