Effects of vacuum magnetic field region on the compact torus trajectory in a tokamak plasma
Bibliographic record
Abstract
Abstract The trajectory of the compact torus (CT) within a tokamak discharge is crucial to fueling. In this study, we developed a penetration model with a vacuum magnetic field region to accurately determine CT trajectories in tokamak discharges. This model was used to calculate the trajectory and penetration parameters of CT injections by applying both perpendicular and tangential injection schemes in both HL-2A and ITER tokamaks. For perpendicular injection along the tokamak’s major radius direction from the outboard, CTs with the same injection parameters exhibited a 0.08 reduction in relative penetration depth when injected into HL-2A and a 0.13 reduction when injected into ITER geometry when considering the vacuum magnetic field region compared with cases where this region was not considered. In addition, we proposed an optimization method for determining the CT’s initial injection velocity to accurately calculate the initial injection velocity of CTs for central fueling in tokamaks. Furthermore, this paper discusses schemes for the tangential injection of CT into tokamak discharges. The optimal injection angle and CT magnetic moment direction for injection into both HL-2A and ITER were determined through numerical simulations. Finally, the kinetic energy loss occurring when the CT penetrated the vacuum magnetic field region in ITER was reduced by by optimizing the injection angle for the CT injected into ITER. These results provide valuable insights for optimizing injection angles in fusion experiments. Our model closely represents actual experimental scenarios and can assist the design of CT parameters.
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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 | 0.001 |
| 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 | 0.001 | 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".