Supersonic Gas Injector for Plasma Fueling in the National Spherical Torus Experiment
Bibliographic record
Abstract
A supersonic gas injector (SGI) has been developed for fueling and diagnostic applications on the National Spherical Torus Experiment (NSTX). It is comprised of a graphite converging-diverging Laval nozzle and a commercial piezoelectric gas valve mounted on a movable probe at a low-field-side midplane port location. Also mounted on the probe is a diagnostic package: a Langmuir probe, two thermocouples, and five pick-up coils for measuring toroidal, radial, vertical magnetic field components and magnetic fluctuations at the location of the SGI tip. The SGI flow rate is up to 33.25 Pa m3/ (1.75 × 1022 euterium particles/s), comparable to conventional NSTX gas injectors. The nozzle operates in a pulsed regime at room temperature and a reservoir gas pressure up to 665 kPa (5000 Torr). The deuterium jet Mach number of about 4 and the divergence half-angle of 5 to 25 deg have been measured in laboratory experiments simulating the NSTX environment. Reliable operation of the SGI and all mounted diagnostics at distances 0.01 to 0.20 m from the plasma separatrix has been demonstrated in NSTX experiments. The SGI has been used for fueling of ohmic and 2- to 4-MW neutral beam injection–heated L- and H-mode plasmas. Fueling efficiency in the range 0.1 to 0.3 has been obtained from the plasma electron inventory analysis. The SGI-fueling–based plasma discharge scenarios enabling better density control have been developed.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".