Simulation of gross and net erosion of high-Z materials in the DIII-D divertor
Bibliographic record
Abstract
Abstract The three-dimensional Monte Carlo code ERO has been used to simulate dedicated DIII-D experiments in which Mo and W samples with different sizes were exposed to controlled and well-diagnosed divertor plasma conditions to measure the gross and net erosion rates. Experimentally, the net erosion rate is significantly reduced due to the high local redeposition probability of eroded high-Z materials, which according to the modelling is mainly controlled by the electric field and plasma density within the Chodura sheath. Similar redeposition ratios were obtained from ERO modelling with three different sheath models for small angles between the magnetic field and the material surface, mainly because of their similar mean ionization lengths. The modelled redeposition ratios are close to the measured value. Decreasing the potential drop across the sheath can suppress both gross and net erosion because sputtering yield is decreased due to lower incident energy while the redeposition ratio is not reduced owing to the higher electron density in the Chodura sheath. Taking into account material mixing in the ERO surface model, the net erosion rate of high-Z materials is shown to be strongly dependent on the carbon impurity concentration in the background plasma; higher carbon concentration can suppress net erosion. The principal experimental results such as net erosion rate and profile and redeposition ratio are well reproduced by the ERO simulations.
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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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".