Fast ion power loads on ITER first wall structures in the presence of NTMs and microturbulence
Bibliographic record
Abstract
The level and distribution of the wall power flux of energetic ions in ITER have to be known accurately in order to ensure the integrity of the first wall. Until now, most quantitative estimates have been based on the assumption that fast ion transport is dictated by neoclassical effects only. However, in ITER, the fast ion distribution is likely to be affected by various MHD effects and probably also by microturbulence. We have now upgraded our orbit-following Monte Carlo code ASCOT so that it has simple, theory-based models for neoclassical tearing mode (NTM)-type islands as well as for turbulent diffusion. ASCOT also allows for full-orbit following, which is important close to the material surfaces and, possibly, also when strong toroidal inhomogeneities are present in the magnetic field. Here we introduce the new models, preliminary results obtained with them, and how these models could be made more realistic in the future. The simulations are carried out for thermonuclear alpha particles in ITER scenario 2 plasma, because we consider this combination to be most critical for the successful operation of ITER. Neither the turbulent transport nor NTM-type islands are found to introduce alarming changes in the wall loads. However, at this stage it was not possible to combine the island structures with the non-axisymmetric magnetic field of ITER, and it remains to be seen what the combined effect of drift islands together with the toroidal ripple and local field aberrations, such as those due to test blanket modules and resonant magnetic perturbations will be.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".