Characterizing Low-Z erosion and deposition in the DIII-D divertor using aluminum
Bibliographic record
Abstract
We present measurements and modeling of aluminum erosion and redeposition experiments in separate helium and deuterium low power, low density L-mode plasmas at the outer divertor strike point of DIII-D to provide a low-Z material benchmark dataset for tokamak erosion-deposition modeling codes. Coatings of Al ∼100 nm thick were applied to ideal (smooth) and realistic (rough) surfaces and exposed to repeat plasma discharges using the DiMES probe. Redeposition in all cases was primarily in the downstream toroidal field direction, evident from both in-situ spectroscopic and post-mortem non-spectroscopic measurements. The gross Al erosion yield was estimated from film thickness change measurements of small area samples, and was found to be ∼40–70% of the expected erosion yield based on theoretical physical sputtering yields after including sputtering by a 1–3% carbon impurity. The multi-step redeposition and re-erosion process, and hence the measured net erosion yield and material migration patterns, were found to be influenced by the surface roughness and/or porosity. A time-dependent model of material migration accounting for deposit accumulation in hidden areas was developed to reproduce the measurements in these experiments and determine a redeposition probability distribution function for sputtered atoms.
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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 | 0.000 |
| 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".