Developing solid-surface plasma facing components for pilot plants and reactors with replenishable wall claddings and continuous surface conditioning. Part B: required research in present tokamaks
Bibliographic record
Abstract
Abstract The companion part A paper (Stangeby et al 2022) reports a number of independent estimates indicating that high-duty-cycle DT tokamaks starting with pilot plants will likely experience rates of net erosion and deposition of solid PFC, plasma facing component, material in the range of 103 to 104 kg yr−1, regardless of the material used. The subsequent redeposition of such large quantities of material has the potential for major interference with tokamak operation. Similar levels and issues will be involved if ∼continuous low-Z powder dropping is used for surface conditioning of DT tokamaks, independent of the material used for the PFC armor. In Stangeby et al (2022) (part A) it is proposed that for high-duty-cycle DT tokamaks, non-metallic low-Z refractory materials such as ceramics (graphite, SiC, etc) used as in situ replenishable, relatively thin—of order mm—claddings on a substrate which is resistant to neutron damage could provide a potential solution for protecting the main walls, while reducing the risk of degrading the confined plasma. Assessment of whether such an approach is viable will require information, much of which is not available today. Section 6 of part A identifies a partial list of major physics questions that will need to be answered in order to make an informed assessment. This part B report describes R&D needed to be done in present tokamaks in order to answer many of these questions. Most of the required R&D is to establish better understanding of low-Z slag generation and to identify means to safely manage it. Powder droppers provide a unique opportunity to carry out controlled studies on the management of low-Z slag in current tokamaks, independent of whether their protection tiles use low-Z or high-Z material.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".