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Record W4387741229 · doi:10.2514/6.2023-4781

Gateway Momentum Unloading Using a Solar Parasol

2023· article· en· W4387741229 on OpenAlexaboutno aff
Stephanie Thomas, Michael Paluszek, Aniesha Dyce

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
Topicsolar cell performance optimization
Canadian institutionsnot available
Fundersnot available
KeywordsAttitude controlOrbit (dynamics)SpacecraftTorqueAerospace engineeringReaction wheelSun-synchronous orbitMomentum (technical analysis)Robotic armDragGimbalComputer scienceControl theory (sociology)BackupSatelliteEngineeringPhysicsGeosynchronous orbitControl (management)Mechanical engineering

Abstract

fetched live from OpenAlex

This paper describes the use of a robotic arm for momentum unloading and orbit control. A panel is attached to the end of a robotic arm. It is positioned, in angle and position, to optimize unloading. The robot arm can move about the spacecraft giving additional degrees of freedom. The panel can be stowed when necessary. The work is based partially on a technology developed by the Canadian Space Agency. The parasol can be grabbed and deployed by the robot arm whenever it is needed, to remove momentum. When it is not needed the parasol is retracted and stowed. The system can be used in high orbits for both momentum and orbit control using solar pressure. In lower orbits, it can use atmospheric drag for the same purpose. This paper focuses on its use for momentum unloading for the NASA Gateway space station. The paper includes a complete GN&C design using single gimbal CMGs with thrusters for orbit control and backup attitude control. The dynamical equations are derived and simulation results are presented for all modes of operation. This includes an optimal attitude profile for minimizing solar and gravity gradient torques over the Gateway orbit.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.083
Threshold uncertainty score0.793

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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.

Opus teacher head0.021
GPT teacher head0.224
Teacher spread0.203 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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