Bibliographic record
Abstract
Divertor tokamaks such as ITER also need limiters for startup and rampdown, as well as protection of the main wall from normal and off-normal plasma loads during the diverted phase. Analysis of the optimum shape with regard to power handling of the plasma-facing surface is typically carried out using numerical procedures. This can make it difficult to identify underlying shaping principles. It is also important to have methods for conveniently and quickly checking the correctness of numerical results, approximately, using readily available analytic expressions. This paper provides such expressions together with their derivations for the case of multiple limiters at the outer wall, low-field side of a tokamak, illustrated here for the case of ITER. Analytic expressions are developed for the near-optimal toroidal shaping of individual limiters which, while distributing the deposited power on the limiter face as uniformly as possible, also provides adequate protection of the limiter edges against over-heating—for a range of plasma shapes and conditions and for a specified number of limiters, limiter sizes, sizes of gaps between limiters and relative radial misalignment between adjacent limiters. The effect of shadowing of one limiter by another is included, for shadowing of both edges and faces. Analytic expressions are developed for the magnitude and location of the peak power flux density deposited on the face and edges of the limiters. There are sufficient important differences in the shaping required for wall regions on the low- and high-field sides and in the secondary divertor region at the top of the vessel to warrant separate treatments of each of the three cases. In subsequent papers analytic expressions will be developed for the inner wall and the top wall.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.005 | 0.003 |
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".