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Record W1974627890 · doi:10.13182/fst05-a637

Improvement of Flow Characteristics for an Advanced Plasma Thruster

2005· article· en· W1974627890 on OpenAlexaboutno aff
M. Inutake, Y. Hosokawa, Ryo Sato, Akira Ando, H. Tobari, Katsumi Hattori

Bibliographic record

VenueFusion Science & Technology · 2005
Typearticle
Languageen
FieldEngineering
TopicPlasma Diagnostics and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsSpecific impulseArcjet rocketNozzleAerospace engineeringThrustPlasmaPropulsionPhysicsMuzzleMaterials scienceMechanicsNuclear physicsEngineering

Abstract

fetched live from OpenAlex

A higher specific impulse and a larger thrust are required for a manned interplanetary space thruster. Until the realization of a fusion-plasma thruster, a magneto-plasma-dynamic arcjet (MPDA) powered by a fission reactor is one of the promising candidates for a manned Mars space thruster. The MPDA plasma is accelerated axially by a self-induced j × B force. Thrust performance of the MPDA is expected to increase by applying a magnetic nozzle instead of a solid nozzle. In order to get a much higher thruster performance, two methods have been investigated in the HITOP device, Tohoku University. One is to use a magnetic Laval nozzle in the vicinity of the MPDA muzzle for converting the high ion thermal energy to the axial flow energy. The other is to heat ions by use of an ICRF antenna in the divergent magnetic nozzle. It is found that by use of a small-sized Laval-type magnetic nozzle, the subsonic flow near the muzzle is converted to be supersonic through the magnetic Laval nozzle. A fast-flowing plasma is successfully heated by use of an ICRF antenna in the magnetic beach configuration.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.010

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 communication0.0000.001
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.010
GPT teacher head0.251
Teacher spread0.242 · 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 designBench or experimental
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

Citations3
Published2005
Admission routes1
Has abstractyes

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