Robust and Reliability-Based Design Optimization Framework for Wing Design
Bibliographic record
Abstract
This paper outlines an architecture for simultaneous analysis, robustness, and reliability calculations in aircraft wing design optimization. Robust design optimization and reliability-based design optimization are unified in a mixed formulation, which streamlines the setup of optimization problems and aims at preventing foreseeable implementation issues in uncertainty-based design while ensuring that the performance hit of robustness/reliability assessments is kept to a minimum. To avoid the extra computation time that would be the result of a direct evaluation approach to nondeterministic optimization, Kriging surrogate models are employed, and an alternative implementation of the reliability subproblem is also proposed. The sigma point method is used to compute statistical moments in the robust objective function. The computational effort of reliability analysis is further reduced through the implementation of a coordinate change in the respective optimization subproblem to solve for the distance from the current iterate to the most probable point of failure. Robustness and reliability-based optimization is tested on both simple analytic problems and more complex wing design problems, across a range of statistical variation, revealing that performance benefits can still be achieved while obeying precise probabilistic constraints.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".