A simple analytic model of impurity leakage from the divertor and accumulation in the main scrape-off layer
Bibliographic record
Abstract
Edge codes such as SOLPS-ITER find distributions of impurity ions, e.g. of C, N, Ne and Ar, in the divertor and SOL which are quite non-uniform spatially, both poloidally and radially. Poloidally, impurity ion density distributions often have strong peaks near the targets as well as a peak on/near the separatrix in the main SOL near the outside midplane. A high density of low-Z impurities near the targets is quite desirable since cold, dense divertor plasma conditions there result in very efficient radiative dissipation of power. By contrast, impurity concentration near the outside midplane separatrix is often quite undesirable since the impurity density there is essentially the boundary value for impurity levels in the confined plasma. In order to better understand the poloidal distribution of impurities in the edge plasma, a simple analytic 1D impurity fluid model, 1DImpFM, has been developed for the transport along open field lines of impurity ions in a specified fuel-plasma background. Often, the strongest parallel forces acting on impurity ions in the edge plasma are (i) FiG , the (fuel) ion temperature parallel-gradient force (‘thermal force’), and (ii) FF , the friction force between fuel and impurity ions (‘friction force’). Recently, Senichenkov et al (2019 Plasma Phys. Control. Fusion 61 045013) reported the extremely useful and informative result that the impurity ion parallel velocity calculated by the SOLPS-ITER code can be remarkably well reproduced by assuming the simple force balance FF + FiG = 0. In the present paper the basis for, and a number of basic predictions of, the 1DImpFM are reported including an assessment of the circumstances under which FF + FiG = 0 can be expected to be a good approximation. The 1DImpFM is used to elucidate the competing roles of thermal and friction forces, as they control three key features of edge impurity behavior: (a) leakage of impurity ions from the divertor, (b) the peaking of impurity density near the targets, and (c) impurity ion accumulation near the midplane separatrix; the model provides simple analytic expressions for estimating the divertor leakage rate (ions/m 2 /s) and impurity density peaking/accumulation (ions/m 3 ). A subsequent paper will report comparisons of results from the 1DImpFM and from SOLPS-ITER modeling of some ITER cases with neon impurities.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".