Plasma Density Control by Molecular Beam Iniection in HT-7 Tokamak
Bibliographic record
Abstract
Laval nozzle, which produces supersonic molecular beam, is proven to be an effective fuelling tool for magnetic confinement devices. Details of its design are given in the first part of this paper. Results with supersonic beam injection in HT-7 tokamak show higher efficiency and deeper penetration compared to normal gas puffing. The peaking factor is almost the same with the off-axis pellet injection. Experiments demonstrate that it is a suitable fuelling method of steady-state operation with high density for super-conducting tokamak. Keywords: HT-7, Laval nozzle, supersonic molecular beam, gas puffing the experiments is to optimize its parameter for the high-density steady-state fueling, deeper penetration and reduce the high edge recycling made by strong gas puffing. The detail of the Laval nozzle design was described in the first part of the paper. The experimental results were given in the following part. 2. Gas Injector Design As we know, in order to obtain a supersonic, parallel, uniform molecular beam at the exit section of an injector, a Laval-shaped nozzle must be used to achieve this goal. The gas in a Laval nozzle will flow from subsonic (convergent section) through transonic (throat section) to supersonic (divergent section) shown in Fig. l. Owing to the different flow properties in each flow section, the design method should be different.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".