Efficient Flutter Prediction Using Reduced-Order Modeling
Bibliographic record
Abstract
Typical aerodynamic shape optimization and multidisciplinary optimization algorithms omit high-fidelity flutter predictions due to the associated computational costs. This paper presents a model order reduction framework as a step toward flutter-constrained aircraft optimization. The Euler equations linearized about a steady-state solution of the nonlinear Euler equations are used as the governing unsteady flow equations. Using a proper orthogonal decomposition approach, a reduced basis is constructed onto which the governing equations are projected. The result is a linear reduced-order model (ROM) with significantly fewer degrees of freedom capable of rapidly approximating aerodynamic forces. This ROM is coupled to a linear structural model to create a single monolithic aeroelastic system. The eigenvalues of the resulting system are analyzed for various flow conditions to determine the onset of flutter in the system. To ensure the stability of the ROM, the use of a stabilizing inner product is demonstrated. The flutter boundaries obtained for both a two-degree-of-freedom airfoil structure and the AGARD 445.6 wing model show good agreement with the full-order model and with the literature.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".