Toward High-Fidelity Aerostructural Optimization Using a Coupled ADjoint Approach
Bibliographic record
Abstract
The tools required to perform high-fidelity aerostructural optimization are developed. Given the highly coupled nature of the aerostructural problem, a multidisciplinary feasible (MDF) approach is used for the framework. This approach is facilitated by a lagged-coupled adjoint, implemented using the ADjoint technique to sensitivity analysis. The ADjoint technique allows for the generation of very accurate and efficient adjoint sensitivities. The lagged-coupled adjoint system, which is equivalent to using a block-Jacobi method on the full coupled adjoint system, is solved using a pair of linear solvers. The structural portion of the system is solved using FEAP’s linear solver, while the CFD portion of the system is solved using PETSc. To demonstrate the accuracy of the coupled ADjoint, the sensitivities computed using the lagged-coupled approach are verified against complex-step sensitivities.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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".