Optimization of gimbaled thruster configurations for asteroid detumbling
Bibliographic record
Abstract
Gimbaled thrusters for the purpose of asteroid detumbling hold the potential to vastly decrease detumbling time for asteroid redirection and asteroid mining missions. As interest in asteroid missions have increased, so have investigations into efficient detumbling methods. Controlling an asteroid’s attitude during a redirection or mining mission leads to much more controllable outcomes, and as such, developing a fast detumbling solution is advantageous. Much of the existing investigations have used fixed-orientation thrusters for optimizing asteroid detumbling, but these thrusters leave little room for error. The optimal solutions that are found for gimbaled thrusters are often uncontrollable for fixed-orientation thrusters, making gimbaled thrusters more versatile for detumbling purposes. This paper develops an approach that employs a genetic algorithm to find the optimal locations of gimbaled thrusters on asteroids for the purpose of rapid detumbling. A time-optimal controller was implemented to determine the orientation of the thrusters throughout the detumbling maneuver. This was investigated for four scenarios that may be the target of future asteroid detumbling missions—spheroid or elongated asteroids and slow or fast rotators. For each of these scenarios, the optimal landed configurations that maximize the torque generated to oppose the tumbling motion was first found, then detumbling was simulated for the best solution for each scenario. To enable comparison, detumbling simulations were also performed for each scenario with fixed-orientation thrusters with the same initial configuration. The results of the optimizations and detumbling simulations are discussed, and the viability of the method developed in this paper to optimize gimbaled thruster lander locations for asteroid detumbling is demonstrated.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".