Refueling of field-reversed configuration core via axial plasmoids injection
Bibliographic record
Abstract
Abstract This study successfully developed a refueling technique for a field-reversed configuration (FRC) via axial plasmoid injection and demonstrated it on the FAT-CM device at Nihon University. The target FRC is generated using the collisional-merging formation technique combined with conical theta-pinch formation. Plasmoids with an FRC-like configuration are coaxially injected from both ends of the FAT-CM device toward the preexisting target FRC. Postinjection, the system achieves equilibrium, resulting in increases by factors of 1.8 and 2.4 in the total inventory and plasma energy, respectively, compared to cases without injection. This method effectively accomplishes FRC refueling while preserving the intrinsic characteristics of a simply connected, axisymmetric configuration and a high beta value approaching unity. Therefore, this approach offers potential for repetitive refueling in the reactor stage having a FRC plasma core. Experimental outcomes are compared with magnetohydrodynamic simulation results. In the collisional merging process, the characteristics of the pre-collision plasmoids, such as the strong toroidal rotation and coherent FRC-like magnetic field structures of the FRC, are not preserved. Experimental environments have been constructed to investigate such unique properties of the resulting FRCs.
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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) | 0.001 | 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".