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Record W3035989599 · doi:10.1088/1741-4326/ab9e16

A simple analytic model of impurity leakage from the divertor and accumulation in the main scrape-off layer

2020· article· en· W3035989599 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueNuclear Fusion · 2020
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMagnetic confinement fusion research
Canadian institutionsUniversity of Toronto
FundersFusion Energy SciencesUT-BattelleResearch Councils UKBattelleU.S. Department of Energy
KeywordsDivertorImpurityLeakage (economics)Materials scienceLayer (electronics)Simple (philosophy)Nuclear engineeringAtomic physicsPlasmaNuclear physicsTokamakPhysicsNanotechnology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.841
Threshold uncertainty score0.986

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0150.000

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.

Opus teacher head0.053
GPT teacher head0.287
Teacher spread0.235 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it