First wall power flux management during plasma current ramp-up on ITER
Bibliographic record
Abstract
Abstract On ITER, plasma start-up will be performed in limiter configuration on the inboard equatorial beryllium first wall panels (FWP). In contrast to most present tokamaks, however, this ramp-up phase will be comparatively long (∼10 s) and the use of actively cooled components means that power flux management is key if FWP lifetime is not to be compromised. Shaping of the FWPs is mandatory to ensure that leading edges do not appear between neighbouring units. For the ITER inboard panels, this has been optimized to account for the discovery in recent years on current devices of narrow scrape-off layer power flux channels for inner wall limited plasmas. However, the shaping results in power densities which are particularly sensitive to the overall ‘longwave’ (LW) alignment of the central column FWP ring with the structure of the toroidal magnetic field (TF), placing tight constraints on the target alignment. This target is currently based on a pure n = 1 LW alignment, but simulations of TF coil (TFC) locking upon energization show that, depending on the initial configuration of the gaps between the TFC inner legs, the field structure can be more complex. Although the TFC manufacture and machine assembly strategy is to make every effort possible to approach the ideal TF structure, an NMR sensor-based TF mapping diagnostic will be implemented to measure the field structure during the first plasma and engineering operation phase. An analytic framework has been developed and verified against numerical simulations to assess the capability for measurements from a set of discrete sensors located on the vacuum vessel inner column to be used to reconstruct the field structure at the FWP locations, a further ∼60 cm radially inward. In parallel with the alignment optimization and TF mapping strategies, modified ramp-up scenarios are also being designed which may be used to reduce inner wall limiter power fluxes if this proves to be necessary during operation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.647 | 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; both teacher heads agree on what is shown here.
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".