OEDGE modeling for the planned tungsten ring experiment on DIII-D
Bibliographic record
Abstract
The OEDGE code is used to model tungsten erosion and transport for experiments with toroidal rings of high-Z metal tiles in the DIII-D tokamak. Such modeling is needed for both experimental and diagnostic design to have estimates of the expected core and edge tungsten density and to understand the various factors contributing to the uncertainties in these calculations. OEDGE simulations are performed using the planned experimental magnetic geometries and plasma conditions typical of both L-mode and inter-ELM H-mode discharges in DIII-D. OEDGE plasma reconstruction based on specific representative discharges for similar geometries is used to determine the plasma conditions applied to tungsten plasma impurity simulations. A new model for tungsten erosion in OEDGE was developed which imports charge-state resolved carbon impurity fluxes and impact energies from a separate OEDGE run which models the carbon production, transport and deposition for the same plasma conditions as the tungsten simulations. These values are then used to calculate the gross tungsten physical sputtering due to carbon plasma impurities which is then added to any sputtering by deuterium ions; tungsten self-sputtering is also included. The code results are found to be dependent on the following factors: divertor geometry and closure, the choice of cross-field anomalous transport coefficients, divertor plasma conditions (affecting both tungsten source strength and transport), the choice of tungsten atomic physics data used in the model (in particular ionization rate for W-atoms), and the model of the carbon flux and energy used for calculating the tungsten source due to sputtering. Core tungsten density is found to be of order 1015 m−3 (excluding effects of any core transport barrier and with significant variability depending on the other factors mentioned) with density decaying into the scrape off layer. For the typical core density in the plasma conditions examined of 2 to 4 × 1019 m−3, this represents a concentration on the order of 5 × 10−5.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".