Dynamic Aeroelastic Performance Optimization of Adaptive Aerospace Structures Employing Structural Geometric Nonlinearities
Why this work is in the frame
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Bibliographic record
Abstract
This thesis proposes a framework for the design optimization of geometric nonlinearities developed by active elements embedded in truss-like aerospace structures for the purpose of attenuating their dynamic aeroelastic response under turbulent aerodynamic gust conditions. Dynamic aeroelastic responses are analyzed considering random Power Spectral Density (PSD) and Tuned Discrete Gust (TDG) excitation profiles. MSC NASTRAN is employed for the development of the dynamic aeroelastic models where the random PSD with a continuous Davenport spectrum (DS) and the TDG with a One-minus cosine (OMC) wind gust excitation profiles are developed. This work presents a multi-objective genetic optimization algorithm (MOGA) utilized to determine optimal prestress values through active element actuations for the purpose of tuning the geometric stiffness and therefore modal response of the structure when exposed to gust excitations. Additionally, this work contributes a new simplified control metric for comparing active member locations. Two case studies are presented to minimize the pointing error of both a simplified and high-fidelity (HF) Earth-based very-long baseline interferometry (VLBI) antenna structure. The pointing error is calculated as the spatial displacement of the secondary reflector using time-consistent displacements (TCD) imparted by time consistent loads (TCL).
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it