Impact of ELM control techniques on tungsten sputtering in the DIII-D divertor and extrapolations to ITER
Bibliographic record
Abstract
The free-streaming plus recycling model (FSRM) has recently been developed to understand and predict tungsten gross erosion rates from the divertor during edge localized modes (ELMs). In this work, the FSRM was tested against the experimental measurements of W sputtering during ELMs, conducted via fast neutral tungsten (WI) spectroscopy. Good agreement is observed using a variety of controlling techniques, including gas puffing, neutral beam heating, and plasma shaping to modify the pedestal stability boundary and, thus, the ELM behavior. ELM mitigation by pellet pacing was observed to strongly reduce W sputtering by flushing C impurities from the pedestal and reducing the divertor target electron temperature. No reduction of W sputtering was observed during the application of resonant magnetic perturbations (RMPs), in contrast to the prediction of the FSRM. Potential sources of this discrepancy are discussed. Finally, the framework of the FSRM is utilized to predict intra-ELM W sputtering rates in ITER. It is concluded that W erosion during ELMs in ITER will be caused mainly by free-streaming fuel ions, but free-streaming seeded impurities (N or Ne) may increase the erosion rate significantly if present in the pedestal at even the 1% level. Impurity recycling is not expected to cause significant W erosion in ITER due to the very low target electron temperature.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".