Manipulation of E×B drifts in a slot divertor with advanced shaping to optimize detachment
Bibliographic record
Abstract
Abstract SOLPS-ITER edge code analysis including drifts shows that optimization of divertor target shaping in a small angle slot (SAS) can strongly influence E × B drift particle fluxes, potentially improving divertor detachment for both toroidal field directions. This is enabled by directing recycling neutrals toward the separatrix from both the common flux region (CFR) and the private flux region (PFR) walls of the slot with a V-shape target in the slot (SAS-V), leading to two separate reinforcing effects, each individually involving positive feed-back: (a) increase of neutral recycling at the PFR wall of the slot due to—and causing—strong radial E × B ion flux from the CFR to the PFR; (b) decrease of E × B loss of ions out of the outer divertor into the inner divertor via the PFR due to reduction of the radial gradient of electron temperature at the outer target caused by the increased particle retention in the outer divertor. This circumvents the general problem for divertor operation with ion B ×∇B toward the X-point: E × B loss of particles from the outer divertor CFR plasma tends to keep it hot and attached. This work identifies a strong interaction between divertor geometry and drifts, a potentially important effect for optimizing advanced divertors for power exhaust in fusion reactors.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".