Direct drive double shell target implosion hydodynamics on OMEGA
Bibliographic record
Abstract
Imploding indirect-drive double shell targets may provide an alternative, non-cryogenic path to ignition on the National Ignition Facility (NIF). Experiments are being pursued at OMEGA to understand the hydrodynamics of these implosions and the possibility of scaling to NIF designs. We have used 40 beams from the OMEGA laser to directly drive the capsules, and we have used the remaining 20 beams to backlight the imploding shells from two different directions at multiple times. We will review the recent experiments to measure the hydrodynamics of the targets using two-view x-ray radiography of the capsules. We will present data on measured yields from the targets. We will present a measured time history of the hydrodynamics of the implosion. Experiments were pursued using direct drive in which the M-band effect, experienced in the indirect drive experiments, could be eliminated or controlled. It was learned in the direct drive experiments that the best performing capsules were those that had a thin outer layer of gold. This effectively causes M-band pre-heat effects giving implosion hydrodynamics and performance closer to the indirect drive case. We will review the method used to radiograph the targets and the techniques used to extract useful information to compare with calculations. The effect of imperfections in the target construction will be shown to be minimal during the initial stage of the implosion. The yields from the targets were observed to be uniformly low compared to indirect-drive.
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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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".