MétaCan
Menu
Back to cohort
Record W4306316764 · doi:10.2514/6.2022-4271

Design and Modeling of a Vectored Electrospray Thruster

2022· article· en· W4306316764 on OpenAlexaffabout
Ivan Savytskyy, Manish Jugroot

Bibliographic record

VenueASCEND 2022 · 2022
Typearticle
Languageen
FieldEngineering
TopicElectrohydrodynamics and Fluid Dynamics
Canadian institutionsRoyal Military College of Canada
Fundersnot available
KeywordsPropulsionThrustAttitude controlAerospace engineeringImpulse (physics)TorqueElectrically powered spacecraft propulsionSpecific impulseWedge (geometry)ElectrosprayEngineeringComputer sciencePhysicsOpticsMass spectrometry

Abstract

fetched live from OpenAlex

View Video Presentation: https://doi.org/10.2514/6.2022-4271.vid Electrospray thrusters employing multiplexed emission from arrays of linear porous wedge emitters have recently gained prevalence in the nanosatellite propulsion community. While these devices naturally offer high-performance thrust profiles, their capabilities may be enhanced further via the implementation of thrust vector control, which is conducive for optimized nanosatellite attitude and trajectory control. A Vectored Electrospray Thruster (VET) that provides multi-axis attitude actuation via thrust vector control is currently being investigated at the Royal Military College of Canada Advanced Propulsion and Plasma Exploration Laboratory (RAPPEL). The VET employs porous wedge emitters and uses pulse-width modulation to generate stepped emission differentials across the propulsion plane, producing precise linear impulse and torque bits. The design and modeling of a two-emitter VET prototype that enables single-axis attitude actuation is presented and discussed.

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 score0.005
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.002

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 head0.008
GPT teacher head0.194
Teacher spread0.186 · 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

Citations4
Published2022
Admission routes2
Has abstractyes

Explore more

Same venueASCEND 2022Same topicElectrohydrodynamics and Fluid DynamicsFrench-language works237,207