IMPROVED MODELLING OF NEUTRALS AND CONSEQUENCES FOR THE DIVERTOR PERFORMANCE IN ITER
Bibliographic record
Abstract
In B2-EIRENE modelling of ITER, the usual, linear Monte-Carlo modelling of neutral transport is inadequate, since the large dimensions and high neutral density make the neutrals in the PFR collisional, providing bulk particle scattering, an effect which is important when removal of the dome is examined. We have developed and implemented [1] a non-linear Monte-Carlo model, including neutral-neutral and molecule-ion collisions, which renders possible for the first time meaningful comparisons among divertor geometries, including those without dome. Relative to the model introduced in [1], we have now introduced collisions of carbon atoms with other neutrals. The plasma consists of D, He, and C ions, whose energy and particle transport are described by constant cross-field diffusivities D = 0.3 m 2 s 1 and ! = 1 m 2 s 1 . For neutrals (D, He, and C atoms and D 2 molecules) a constant albedo A at the divertor bottom represents pumping. All the surfaces are covered by carbon. The power input from the core P in and the gas puffing are varied to explore the parameter space in P in and neutral pressure in the private flux region (PFR), p DT . The dome affects the compression of neutrals in the PFR to facilitate helium exhaust, reduces the neutral influx to the core plasma near the X-point, and provides neutron shielding (not treated here). Using the full model, we ha ve re-examined aspects of the dome design (transparency, [2]) and compared the plasma parameters with and without dome.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".