Mass and Stiffness Effects of Harnessing Cables on Structural Dynamics: Continuum Modeling
Bibliographic record
Abstract
Dynamic analysis of satellite structures comprises an important part of their design. One such example is an inflatable deployable structure. Although prized for their small volume, mass, and subsequent launch costs, these structures are quite susceptible to disturbances in a space environment that can jitter their mission accuracy. Therefore, it is important to have models to accurately predict their vibrations response to these disturbances. One important aspect to include in these models is the effect of signal and power cables surrounding the host structure that has been traditionally ignored or accounted for using ad hoc models. Obtaining simple analytical solutions that can predict the dynamic behavior of these structures has numerous advantages for their vibrations control and modeling before their launch. Although damping plays an important role in the dynamics of these structures, the presented paper pertains only to the mass and stiffness effects of these cables. The structures are modeled as beam structures harnessed with cables and the governing partial differential equations of motion for different coordinates of vibrations, such as bending, longitudinal, and torsional modes, are derived for the harnessed structure. Two wrapping patterns for the cables are considered. Natural frequencies and the resultant frequency response functions are presented, and the results are compared to a finite element solution.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".