Development of supersonic plasma flows by use of a magnetic nozzle and an ICRF heating
Bibliographic record
Abstract
A high-beta, supersonic plasma flow plays a crucial role in MHD phenomena in space and fusion plasmas. There are a few experimental researches on production and control of a fast flowing plasma in spite of a growing significance in the magnetized-plasma flow dynamics. A magneto-plasma-dynamic arcjet (MPDA) is one of promising devices to produce a supersonic plasma flow and has been utilized as an electric propulsion device with a higher specific impulse and a relatively larger thrust. We have improved the performance of an MPDA to produce a quasi-steady plasma flow with a transonic and supersonic Mach number in a highly-ionized state. There are two methods in order to control an ion-acoustic Mach number of the plasma flow exhausted from an MPDA: one is to use a magnetic Laval nozzle to convert a thermal energy to a flow energy and the other is a combined system of an ion heating and a divergent magnetic nozzle. The former is an analogous method to a compressible air flow and the latter is the method proposed in an advanced thruster for a manned interplanetary space mission. We have clarified the plasma flow characteristics in various shapes of a magnetic field configuration. It was demonstrated that the Mach number of the plasma flow could increase up to almost 3 in a divergent magnetic nozzle field. This paper reports recent results on the flow field improvements: one is on a magnetic-Laval-nozzle effects observed at the muzzle region of the MPDA, and the other is on ICRF (ion-cyclotron-range of frequency) heating of a supersonic plasma by use of a helical antenna.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".