Multi-Objective Design Optimization of Multiple Tuned Mass Dampers for Attenuation of Dynamic Aeroelastic Response of Aerospace Structures
Bibliographic record
Abstract
This paper proposes a design procedure to determine the optimal configuration of multi-degrees of freedom (MDOF) multiple tuned mass dampers (MTMD) to mitigate the global dynamic aeroelastic response of aerospace structures. The computation of the aerodynamic excitations is performed considering two models of atmospheric disturbances, namely, the Power Spectral Density (PSD) modelled with the Davenport Spectrum (DS) and the Tuned Discrete Gust (TDG) with the one-minus cosine profile. In order to determine the optimum sets of MTMD, a Multi-objective design Optimization considering Genetic Algorithm (MOGA) is implemented, where the selected fitness functions for the analysis are the minimization of the total mass of the resonators as well as the concurrent minimization of the peak displacements of a specified structural node in all translational degrees of freedom. A case study is presented to demonstrate the proposed methodology, where the optimal sets of MTMD are determined for the concurrent minimization of the pointing error of a truss-like antenna structure as well as the mass of the considered MTMD. It is found that the placement of the MTMD in the primary reflector of the antenna structure provided a maximum reduction in the pointing error of 62.0% and 39.2%, considering the PSD and the TDG models, respectively. Finally, this paper presents an advanced framework to estimate optimal parameters of MTMD control devices under convoluted loading cases as an initial step towards the use of such passive systems in applications that commonly employ active or semi-active solutions.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".