Electromagnetic Lithium Ring Compression for Magnetized Target Fusion Application: Trajectories
Bibliographic record
Abstract
Abstract To achieve commercially relevant fusion conditions in a magnetized plasma, rapid and efficient heating must surpass heat loss. In Magnetized Target Fusion (MTF) experiments, which heat plasma by compression, a magnetic flux conserver made of metal is essential for plasma confinement, and an understanding of the compression trajectory of this plasma liner is crucial to the design and operation of the machine. In this work, lithium rings, 527 mm in diameter and 55 mm in height, were produced by centrifugal casting and electromagnetically compressed using a high voltage power supply with capacitor energy ranging from 100 kJ to 250 kJ. High speed cameras were used to track the trajectories of the inner and outer edges of the top surface of the ring, as well as its inner edge at the equator. Magnetic field sensors were positioned at a number of radial locations to measure the change in magnetic flux density during ring compression. Ring parameters, such as thickness and temperature, were explored to attain symmetric compression trajectories free of buckles. A 2D-axisymmetric numerical model was developed using the ANSYS LS-DYNA software package to predict compression trajectories and evaluate the energy efficiency of the compression. The aim of this work is to assess the use of concentric coil electromagnetic compression, also known as a theta-pinch, in magnetized target fusion using a solid lithium liner and to validate the LS-DYNA model. Close agreement between simulation and experiment was observed. This research contributes to advancing Magnetized Target Fusion technologies, with implications for future fusion energy applications.
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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".