Investigation of heat transport using directly driven gold spheres
Bibliographic record
Abstract
Recently, heat transport was investigated using a directly driven beryllium sphere [Farmer et al., Phys. Plasmas 27, 082701 (2020)]. Models that overly restrict heat transport were rejected. This paper extends work to directly driven gold spheres where radiation loss is more important. Here, gold coated spheres are directly driven at the OMEGA laser facility at intensities of 5×1014 W/cm2. Plasma conditions, laser coupling, and x-ray flux are all measured. Comparisons to 2D radiation-hydrodynamic simulations are performed. Simulations use three common heat transport models: local transport with flux limiters of f = 0.15 and f = 0.03, and the nonlocal Schurtz–Nicolai–Busquet (SNB) model. It is shown that both the SNB model and f = 0.15 match the measured plasma conditions with the SNB model better capturing the temporal evolution of electron temperature. The f = 0.03 model predicts too low of an electron density and too hot of a temperature. The measured scattered light is roughly 6% of the incident energy, the f = 0.15 and SNB models predict 0.5% uncoupled light, and f = 0.03, 38% uncoupled light. The x-ray fluxes in the f = 0.15 and SNB simulations rise too quickly and are just outside the measurement's error, while the x-ray flux in the f = 0.03 simulation is low by a factor of two-three. For these reasons, the f = 0.03 model is rejected.
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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 | 0.000 |
| 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".