Simulation study for divertor design to handle huge exhaust power in the SlimCS DEMO reactor
Bibliographic record
Abstract
By the SOLDOR/NEUT2D simulation for divertor design study on a compact DEMO reactor, SlimCS, we estimated the prospect of handling the huge exhaust power in the divertor. Assuming exhaust power of 500 MW and ion outflux of 0.5 × 10 23 s −1 into the scrape-off-layer, the peak heat load is estimated to be 70 MW m −2 on the outer target on the initial divertor design (vertical target) with the introduction of moderate gas puff flux and Ar fraction. This value significantly exceeds the allowable level of 10 MW m −2 which is an initial design target. By installing the ' V-shaped corner ' in the bottom of the outer divertor target, and using strong gas puffing or Ar impurity injection, the detached condition with high particle recycling and radiation loss conditions is formed, and the peak heat load is successfully reduced below 10 MW m −2 . It can also be demonstrated properly for the dependence of the exhaust power on the divertor heat load. Peak heat load is reduced exponentially with a decrease in the exhaust power and reaches 7 MW m −2 at Q total = 300 MW for moderate gas puff flux and Ar fraction.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".