Dynamic Aeroelastic Performance Optimization of Adaptive Aerospace Structures Employing Structural Geometric Nonlinearities
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).To increase the computational efficiency of the design optimization process of the HF model, model order reduction is conducted using the Craig-Bampton method which resulted in the computation time decreasing from 39.21 minutes to only 50 seconds while maintaining a 99.9% Modal Assurance Criterion (MAC) correlation in the first 20 mode shapes of interest of the structure.With the reduced model, the framework used multi-objective genetic optimization with iii the dual objectives of decreasing total pointing error while minimizing the total strain energy in the system as a result of both the applied aerodynamic and inertia loads as well as the applied actuations.The yield strength of the elements and their maximum displacements were used as design constraints to ensure integrity of the structure.Pareto fronts are presented containing optimal responses for 16, 32, and 44 active members of the structure.The utopian point method was employed to calculate the best configuration of active members to be considered.A reduction of 82.6% with a total strain energy increase of 292.5% was obtained for the primary operating case under PSD gust excitation.On the other hand, at the increased mean wind speeds of the secondary operating case, the developed design algorithm was able to reduce the total pointing error by 80.9% but with a total strain energy increase of 825.3%.Similarly, for TDG analysis with the OMC excitation profile the optimization algorithm reduced the total pointing error by 51.6% with a TSE increase of 2098.1% and 80.5% with a TSE increase of 48.7% for the primary and secondary operating conditions, respectively, when compared to the uncontrolled response.The adaptive nature of the presented methodology allows a single actuator layout to mitigate structural response for a variety of load cases, which is a large benefit over many traditionally passive techniques.This thesis expands the existing usage of geometric nonlinearities to determine optimal active element location and actuations for given optimization objectives under realistic environmental loading conditions.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 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".