Plasma Surface Interaction (PSI) studies at DIII-D
Bibliographic record
Abstract
Understanding of Plasma Surface Interactions (PSI) and the selection of suitable plasma facing materials are critical areas for current tokamak experiments and future D-T burning facilities including ITER and FNSF. In support of PSI studies, DIII-D uses the Divertor Materials Evaluation System (DiMES), which contains a removable probe where material samples can be exposed to as few as a single well-characterized plasma shot. Experiments, consisting of a carbon DiMES probe surface with metal coatings of Be, W, V, Mo or Al, have been exposed to the DIII-D lower divertor strike point plasma for cumulative discharge times of 4-20s. Extensive DIII-D divertor diagnostics provided well-characterized plasmas for modeling efforts. Experimental results were benchmarked with modeling codes to validate and extend the predictive capability of the codes. Reported in this paper are two recent experiments and results. The first is on the net and gross erosion of Mo coatings and the extension of these results to an extrapolated all Mo surface DIII-D machine. The second is on the exposure to vertical displacement discharges and X-point plasma discharges of W-fuzz buttons, which were prepared by the PISCES (UCSD) laboratory. The surprising results are the robustness of the W-fuzz and that W impurity was not detected in the plasma core at the conditions studied.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".