Obtaining reactor-relevant divertor conditions in tokamaks
Bibliographic record
Abstract
It is argued that the paramount boundary plasma issue for DT reactors is likely to be the erosion wear of the plasma facing components, PFCs, and that a number of potential solutions all require the achievement of not only low temperature (≲10 eV) but also high density (≳10 21 m −3 ) in the divertor. Estimates are made of the minimum heating power, P heat , required to achieve a divertor target temperature of T t = 5 eV and density n t > 10 21 m −3 , based on four recent hypotheses or scalings for the width of the power footprint on the target, λ q t . Each of these result in predictions of how the required minimum P heat depends on device size, namely as R , R 3/2 or R 2 . The absolute magnitude for the required values of minimum P heat is found not to vary significantly among the four power scalings; for the most part a factor of order ∼2 for a significant range of R . The four hypotheses/scalings for λ q t are empirically based; however, they draw on measurements made in tokamaks that did not have divertors operating primarily under these conditions. In order to establish if any of these power scalings are applicable, they are compared with measurements from a set of DIII-D discharges with high n t ∼ 0.35 × 10 21 at 5 eV. It is found that all four power scalings match the experimental measurements to within the uncertainties. The main objective is to determine what power is needed to achieve the required divertor conditions in future devices, for both reactor and simulator tokamaks, and therefore the approximate agreement of the four, strongly empirical, power scalings increases confidence that this may be possible.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".