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Record W2990107848 · doi:10.2514/1.g004608

Flight Dynamics and Control Strategy of Electric Solar Wind Sails

2019· article· en· W2990107848 on OpenAlexafffund
Gangqiang Li, Zheng Zhu, Chonggang Du

Bibliographic record

VenueJournal of Guidance Control and Dynamics · 2019
Typearticle
Languageen
FieldEngineering
TopicSpacecraft Dynamics and Control
Canadian institutionsYork University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSolar sailAerospace engineeringPhysicsAttitude controlDynamics (music)Electric fieldControl theory (sociology)Computer scienceEngineeringPropulsionControl (management)Acoustics

Abstract

fetched live from OpenAlex

This paper studies the flight dynamics and control strategy for electric solar wind sails based on the nodal position finite element method, where the coupling effects between tether dynamics and the electrical field are considered. A modified throttling control strategy is proposed to control the attitude of electric sails by modulating individual tether voltage synchronously with the spinning motion of the sails. The effects of four critical physical parameters (tether numbers, tether length, sail spin rate, and mass of remote units) are investigated. The results show that the effect of the relative velocity of the solar wind has a significant effect on the spin rate of the sail in attitude maneuvering, which in turn affects the attitude dynamics and orbit motion of the sail. Numerical results show that the proposed control strategy work successfully stabilizes the spin rate of sail when the new type sail is adopted.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

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)0.0010.000

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.

Opus teacher head0.002
GPT teacher head0.175
Teacher spread0.173 · 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 source (direct Gemma or distilled Codex), 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

Citations28
Published2019
Admission routes2
Has abstractyes

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