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Record W4407425941 · doi:10.2514/1.a36239

Propulsion Properties of Electric Sails

2025· article· en· W4407425941 on OpenAlexaff
Sergey Gimelshein, Kentaro Hara, Yusuke Yamashita, Anthony M. DeStefano

Bibliographic record

VenueJournal of Spacecraft and Rockets · 2025
Typearticle
Languageen
FieldEngineering
TopicPlasma Diagnostics and Applications
Canadian institutionsInstitute of Particle Physics
FundersNational Aeronautics and Space Administration
KeywordsAerospace engineeringPropulsionElectrically powered spacecraft propulsionIon thrusterArcjet rocketPropellantIn-space propulsion technologiesSpacecraft propulsionEnvironmental scienceAeronauticsEngineering

Abstract

fetched live from OpenAlex

Multiparametric study of a thrust force exerted on a model electric (E) sail spacecraft by the solar wind is conducted with a particle-in-cell method. Several effects relevant to the thrust performance are examined with the focus on the accuracy of the obtained results, reflected in the improved initial conditions and physically realistic boundary conditions set at the external boundaries and the near field of a tether. These effects include thrust sensitivity to the solar wind density, temperature, and relative velocity between the solar wind and the spacecraft as well as the influence of the tether potential on the resulting thrust force. The dependence of thrust [Formula: see text] on the distance between the spacecraft and the sun [Formula: see text] is found to have an exponential form approximated by [Formula: see text]. Numerical estimates of the electric current to the E-sail tether mesh give values that are on average about 50% higher than those predicted by the orbital motion limited theory. In addition to single tethers, tether arrays are also studied and shown to have lower thrust per tether that that of a single tether.

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)0.001
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: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.005
GPT teacher head0.188
Teacher spread0.184 · 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

Citations1
Published2025
Admission routes1
Has abstractyes

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