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Record W4246155320 · doi:10.1504/ijasse.2021.114118

Design and implementation of low-power low-cost quasi steady-state magnetoplasmadynamic propulsion using Ar-He and N<SUB align="right">2-He gas mixtures

2021· article· en· W4246155320 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueInternational Journal of Aerospace System Science and Engineering · 2021
Typearticle
Languageen
FieldPhysics and Astronomy
TopicSolar and Space Plasma Dynamics
Canadian institutionsYork University
FundersYork University
KeywordsPropulsionSteady state (chemistry)Power (physics)Nuclear engineeringChemistryState (computer science)Materials sciencePhysicsThermodynamicsEngineeringComputer sciencePhysical chemistry

Abstract

fetched live from OpenAlex

Current miniature plasma propulsion technologies use ion or Hall propulsion to provide thrust for miniature satellites. The problem with ion and Hall thrusters is the low thrust-to-power ratio (30 mN/kW-50 mN/kW), and it is not enough for high-speed manoeuvres in deep-space missions. Alternatively, magnetoplasmadynamic propulsion provides higher thrust to miniature satellites than ion thrusters without the increase in mass. Magnetoplasmadynamic propulsion is a technology that the plasma is accelerated electromagnetically. This research investigates the design and performance of a low-cost magnetoplasmadynamic thruster built for micro and nanosatellites. Gas mixtures are tested in this research to observe any improvement in the overall performance. The gases used in the thruster are pure helium, nitrogen and argon; with gas mixtures of 50% helium -50% nitrogen and 50% helium -50% argon. The specific impulse, impulse bit, thrust efficiency and thrust-weight ratios of 50% helium -50% nitrogen are 801 seconds, 6.29 Ns, 19.8% and 15.72 mN/kg, respectively.

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.

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)0.001
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.669
Threshold uncertainty score0.506

Codex and Gemma teacher scores by category

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

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 head0.235
Teacher spread0.230 · 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