The relation between upstream density and temperature widths in the scrape-off layer and the power width in an attached divertor
Bibliographic record
Abstract
The target power width is one of the most critical practical quantities in the development of magnetic fusion energy. It is essential to know how to scale this quantity to future devices. At present the controlling physics is not adequately understood, making reliable prediction difficult. It seems likely that two important processes effecting are (a) cross-field transport, e.g. D ⊥, and (b) volumetric power loss processes in the edge plasma, with the latter tending to occur mainly in the divertor for attached divertor conditions. It is hypothesized that a simple relation exists between the ‘upstream’ radial profiles of n e and T e in the main scrape-off layer, , , and the parallel power flux density at the divertor entrance, . Such a simple relation is found here in 2D SOLPS edge code simulations of attached divertor conditions, which contain a wide range of more or less complex edge physics effects. It is found that , as can be expected on the basis of flux-limited parallel heat conduction, rather than Spitzer–Harm conduction for which is expected. For the relatively open divertor configuration considered, and for attached divertor conditions, it is found that the flux-limited relationship also holds for the SOLPS power flux density deposited on the target , even including the radiation load; this despite the fact that up to half the power into the SOL is dissipated radiatively. Comparing with experimentally measured target power widths for H-mode discharges, better agreement is found assuming flux limited rather than Spitzer–Harm transport although definitive conclusions will require analysis of specific discharges in specific tokamaks. This study is a necessary preliminary work to an equivalent treatment of the case where volumetric losses in the divertor are stronger, including the detached, strongly radiating divertor case with momentum loss.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".