Nonlinear Aeroelasticity Computations in Transonic Flows Using Tightly Coupling Algorithms
Bibliographic record
Abstract
A nonlinear computational aeroelasticity model based on the Euler equations of compressible flows and the linear elastodynamic equations for structures is developed. The Euler equations are solved on dynamic meshes using the ALE kinematic description. Thus, the mesh constitutes another field governed by pseudo-elatodynamic equations. The three fields are discretized using proper finite element formulations which satisfy the geometric conservation law. A matcher module is incorporated for the purpose of pairing the grids on the fluid-structure interface and for transferring the loads and displacements between the fluid and structure solvers. Two solutions strategies (Gauss Seidel and Schur-Complement) for solving the nonlinear aeroelastic system are discussed. Using second order time discretization schemes allows us to use large time steps in the computations. The numerical results on the AGARD 445.6 aeroelastic wing compare well with the experimental ones and show that the Schur-complement coupling algorithm is more robust than the Gauss-Seidel algorithm for relatively large oscillation amplitudes.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".