Developing and validating advanced divertor solutions on DIII-D for next-step fusion devices
Bibliographic record
Abstract
Abstract A major challenge facing the design and operation of next-step high-power steady-state fusion devices is to develop a viable divertor solution with order-of-magnitude increases in power handling capability relative to present experience, while having acceptable divertor target plate erosion and being compatible with maintaining good core plasma confinement. A new initiative has been launched on DIII-D to develop the scientific basis for design, installation, and operation of an advanced divertor to evaluate boundary plasma solutions applicable to next step fusion experiments beyond ITER. Developing the scientific basis for fusion reactor divertor solutions must necessarily follow three lines of research, which we plan to pursue in DIII-D: (1) Advance scientific understanding and predictive capability through development and comparison between state-of-the art computational models and enhanced measurements using targeted parametric scans; (2) Develop and validate key divertor design concepts and codes through innovative variations in physical structure and magnetic geometry; (3) Assess candidate materials, determining the implications for core plasma operation and control, and develop mitigation techniques for any deleterious effects, incorporating development of plasma-material interaction models. These efforts will lead to design, installation, and evaluation of an advanced divertor for DIII-D to enable highly dissipative divertor operation at core density (n e/n GW), neutral fueling and impurity influx most compatible with high performance plasma scenarios and reactor relevant plasma facing components (PFCs). This paper highlights the current progress and near-term strategies of boundary/PMI research on DIII-D.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".