Aerodynamic Shape Optimization for Unsteady Flows With Application to Laminar Flows
Bibliographic record
Abstract
View Video Presentation: https://doi.org/10.2514/6.2021-3028.vid An aerodynamic shape optimization framework for unsteady flow is applied to a range of two- and three-dimensional laminar flows. The shape optimization framework uses free-form deformation for geometry control with an underlying B-spline surface parameterization integrated with an efficient mesh deformation method. The mesh deformation is based on the linear elasticity method applied to a B-spline control volume parameterization of the mesh. A parallel implicit Newton-Krylov algorithm is used to solve the discretized flow equations and the discrete adjoint methodology is applied to both the flow and the mesh-movement algorithms to compute the gradient. For the two-dimensional studies, we consider three objectives based on the mean aerodynamic quantities: lift-constrained drag minimization, lift-to-drag ratio maximization, and lift maximization. For the drag minimization and lift-to-drag ratio maximization problems, the optimizer improved the performance of the baseline airfoil primarily by keeping the flow on the upper surface attached as long as possible and also pushing the camber towards the trailing edge to increase or maintain the lift coefficient. The optimizer improved the drag minimization objective by more than 20% and the lift-to-drag ratio maximization objective by about 50% for roughly the same initial drag. We also investigate the impact of design variable scaling on the convergence of the lift-maximization problem. For the three-dimensional studies, we consider a minimization of mean drag at a fixed mean lift, and we allow section shape, aerodynamic twist about the quarter-chord, and the chord length to vary along the span of the wing. The optimizer exploits all of the geometric freedom given to improve the design objective while satisfying the constraints imposed and produces some non-intuitive geometric changes, especially with respect to the wing planform.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".