Comparison of Optimization Algorithms Applied to Aerodynamic Design
Why this work is in the frame
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Bibliographic record
Abstract
This thesis investigates the performance and robustness of three gradient-based optimization algorithms. The algorithms are the BFGS quasi-Newton algorithm, which uses the quadratic penalty approach for constraints, the KSOPT algorithm, which uses the Kreisselmeier-Steinhauser function to combine the objective function and constraints, and the SNOPT algorithm, which implements a sequential quadratic programming method for solving constrained optimization problems. These approaches are applied to the two-dimensional Navier-Stokes aerodynamic shape optimization problem. Accurate gradients are computed using the discrete adjoint method. The performance of the optimizers is demonstrated for several design examples, including inverse design, maximization of lift-to-drag ratio, maximization of endurance factor, lift-constrained drag-minimization, and multi-point optimization. The SNOPT optimizer is more robust and efficient than the BFGS and KSOPT optimizers for the majority of test cases presented.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it