Efficient Monolithic Solution Algorithm for High-Fidelity Aerostructural Analysis and Optimization
Bibliographic record
Abstract
An efficient and robust solution algorithm for the aerostructural analysis and coupled adjoint problems is crucial to the success of high-fidelity aerostructural optimization. The objective of the present paper is to investigate ways to maximize the efficiency of a monolithic solution method and further quantify its benefits in the context of aerostructural optimization. A Newton–Krylov method is used for the aerostructural analysis, and a preconditioned Krylov subspace method is used for the coupled adjoint solution. Several aspects of the monolithic solution method have been investigated. These include appropriate strategies for scaling and matrix–vector product evaluations as well as block Jacobi and block Gauss–Seidel preconditioning techniques that preserve the modularity between subproblems. The monolithic solution method is applied to problems with varying degrees of fluid–structure coupling as well as a wing-span optimization study. In most cases, the monolithic solution algorithm requires 20–70% less computing time than its partitioned counterpart. This advantage increases with increasing wing flexibility. Robustness of the monolithic solution method is shown via its reduced sensitivity to the choice of problem-dependent solution parameters as well as its ability to converge when the partitioned method fails.
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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 | 0.000 |
| 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) | 0.005 | 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".