Passive-Tuned Mass Dampers for the Pointing Accuracy Mitigation of VLBI Earth-Based Antennae Subject to Aerodynamic Gust
Bibliographic record
Abstract
This paper proposes an optimization procedure to achieve the best configuration of multiple degrees of freedom Tuned Mass Dampers (TMDs) to mitigate the pointing error of Very-Long-Baseline Interferometry (VLBI) Earth-based radio antennae operating under aerodynamic gust conditions. In order to determine the optimum sets of TMDs, a Multi-Objective design optimization employing a genetic algorithm is implemented. A case study is presented where fourteen operational scenarios of wind gust are considered, employing two models of atmospheric disturbances, namely the Power Spectral Density (PSD) function with a statistical profile presented by the Davenport Spectrum (DS) and a Tuned Discrete Gust (TDG) modeled as a one-minus cosine signal. It is found that the optimal configurations of TMDs are capable of reducing the pointing error of the antenna by an average of 66% and 50% for the PSD and TDG gust excitation scenarios, respectively, with a mass inclusion of 1% of the total mass of the antenna structure. The optimal TMD parameters determined herein can be utilized for design and field implementation in antenna systems, such that their structural efficiency can be enhanced for radio astronomy applications.
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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) | 0.001 | 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".