Optimization of a Low-Thrust Salvage Mission from a Highly Inclined Geostationary Transfer Orbit to a Geostationary Orbit
Bibliographic record
Abstract
A direct optimization method was used to flnd several three-dimensional minimum fuel trajectories from a highly inclined (51 degree) geostationary transfer orbit (GTO) to a geostationary orbit (GEO). To obtain GEO, two strategies were examined: 1) using an Earth-orbiting transfer, and 2) using a lunar gravity assist to remove the excess inclination. The solutions consisted of an initial non-optimized impulsive high-thrust burn, followed by optimized low-thrust burns. A single-shooting optimization strategy was used to solve the various transfer problems. In order to accommodate the many orbit revolutions of the Earth-orbiting transfer, a multiple-orbit thrust parameterization strategy was used to reduce the problem size. This strategy allows near-optimal solutions to be found for very large transfer problems. For the lunar swingby trajectories, the transfer problems were divided into two subproblems due to complexity involved in the swingby. Additionally, the complex-step derivative approximation was used to obtain high accuracy derivative information for the objective function and nonlinear constraints. This high accuracy derivative information was found to resolve some of the inherent sensitivity and lack of robustness present in the single-shooting method.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".