Effects of vacuum magnetic field region on the compact torus trajectory in a tokamak plasma
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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) | 0.001 | 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 it