On the efficacy of imploding plasma liners for magnetized fusion target compression
Bibliographic record
Abstract
A new theoretical model is formulated to study the idea of merging a spherical array of converging plasma jets to form a “plasma liner” that further converges to compress a magnetized plasma target to fusion conditions [Y. C. F. Thio et al., “Magnetized target fusion in a spheroidal geometry with standoff drivers,” Current Trends in International Fusion Research II, edited by E. Panarella (National Research Council Canada, Ottawa, Canada, 1999)]. For a spherically imploding plasma liner shell with high initial Mach number (M=liner speed/sound speed) the rise in liner density with decreasing radius r goes as ρ∼1∕r2, for any constant adiabatic index γ=dlnp∕dlnρ. Accordingly, spherical convergence amplifies the ram pressure of the liner on target by the factor A∼C2, indicating strong coupling to its radial convergence C=rm∕R, where rm(R)=jet merging radius (compressed target radius), and A=compressed target pressure/initial liner ram pressure. Deuterium-tritium (DT) plasma liners with initial velocity ∼100km∕s and γ=5∕3, need to be hypersonic M∼60 and thus cold in order to realize values of A∼104 necessary for target ignition. For optically thick DT liners, T<2eV, n>1019–1020cm−3, blackbody radiative cooling is appreciable and may counteract compressional heating during the later stages of the implosion. The fluid then behaves as if the adiabatic index were depressed below 5∕3, which in turn means that the same amplification A=1.6×104 can be accomplished with a reduced initial Mach number M≈12.7(γ−0.3)4.86, valid in the range (10
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".