Experimental validation of a model for particle recycling and tungsten erosion during ELMs in the DIII-D divertor
Bibliographic record
Abstract
A refined version of the Fundamenksi-Moulton 'free-streaming' model (FSM) for the dynamics of divertor density, particle flux, and heat flux during edge localized modes (ELMs) is presented. This model depends only on inter-ELM pedestal and divertor conditions and, crucially, incorporates particle recycling: a FSM with recycling model, FSRM. The effective particle recycling coefficient, Reff, is the only empirical fitting parameter in the FSRM. The predictions of the FSRM are systematically tested against a DIII-D database of ELM ion and energy fluence measurements and are shown to be consistent with the model across a wide range of pedestal and divertor conditions using a constant value of 0.96 for Reff . Predictions for W sputtering during ELMs are developed based on the FSRM. It is concluded that energetic free-streaming D+ ions and C6+ impurities are the dominant contributors to the intra-ELM gross erosion of W in the DIII-D divertor, i.e., recycling ions and impurities have relatively little impact on the total W sputtering rate. These calculations are also shown to be consistent with spectroscopic measurements of W gross erosion for three different pedestal conditions after incorporating the strong electron density dependence of the WI 400.8 nm ionizations/photon (S/XB) coefficient.
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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 | 0.002 |
| 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) | 0.001 | 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".