Dynamic Modeling and Analysis of Electrodynamic Multi-tether System
Bibliographic record
Abstract
Electrodynamic tether (EDT) is a key prospective technique for space-debris removal without the use of propellant. However, there are 2 main shortcomings of the classical EDT system. One is that the conductive tether with a single wire should exceed several kilometers to produce the expected force, which increases the risk of collision and damage. The other is the heavy current or high voltage caused by the overlong tether, which may even melt itself. Therefore, a novel electrodynamic multi-tether (EMT) system has been proposed here to overcome the above disadvantages of the classical EDT system. The EMT system has multi tethers connecting the 2 end bodies, which has more complex dynamic behaviors than the EDT system. In order to promote the application of the EMT system, this paper will pay attention to the dynamic modeling and analysis of the EMT system, to reveal the primary dynamic characteristics. Firstly, the dynamic equation of the novel EMT system was established. Secondly, the linear assumption and vibration theory were utilized to illustrate its primary dynamic characteristic. Finally, the expressions of the vibration period and critical current of tether were given and numerical simulations were conducted to verify these analyses. The results showed that the tether libration equation can be simplified when the size of main satellite is much smaller than that of tether. Besides, the tether will go tumbling immediately when the practical current exceeds the critical current.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".