Electromagnetic Lithium Ring Compression for Magnetized Target Fusion Application: Shell Buckling
Bibliographic record
Abstract
Abstract Magnetized target fusion (MTF) relies on rapidly compressing magnetized plasma within a flux conserver to attain fusion conditions. Maintaining the smoothness and symmetry of the plasma-facing surface during compression is crucial, as any asymmetric deformation or buckling of the solid metal shell can disrupt magnetic confinement, reducing plasma temperature, and lifetime. To establish the criteria for preventing buckling in solid ring implosions, we assess the effectiveness of the dynamic plastic flow buckling model. This assessment involves analyzing buckling phenomena in magnetically driven, imploding lithium rings. This investigation includes a total of 5 experiments with variations in collapse velocities, radius-to-thickness ratios, and initial ring temperatures. We capture and analyze the collapse trajectories and deformations of the rings during implosion using computer vision techniques. To establish material properties required for the theory, we utilize the Johnson-Cook model for the lithium ring with strain and strain rate extracted from the experiments. Using the extracted material properties and ring dimensions and employing the analytical model, we predict critical buckling velocities and dominant modes for each experimental scenario. We observe qualitative agreement between the experiment and analytical model within measurement uncertainty, indicating the potential to define design, manufacturing, and quality requirements for larger-scale Magnetized Target Fusion experiments using solid shells to achieve fusion conditions.
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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 | 0.001 |
| 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".