Analysis of fuelling requirements in ITER H-modes with SOLPS-EPED1 derived scalings
Bibliographic record
Abstract
Abstract Fuelling requirements for ITER are analysed in relation to pellet fuelling and ELM pacing, and a divertor power load control consistent with the ITER pumping and fuel throughput capabilities. The plasma parameters at the separatrix and the particle sources are derived from scalings based on SOLPS simulations. Effective transport coefficients in the H-mode pedestal are derived from EPED1 + SOLPS scalings for the pedestal height and width. 1.5D transport is simulated in the ASTRA framework. The operating window for ITER DT plasmas with the required fusion performance and level of ELM, and divertor power load control compatible with ITER fuelling and pumping capabilities, is determined. It is shown that the flexibility of the ITER fuelling systems, comprising pellet and gas injection systems, enables operation with Q = 10, which was found to be marginal in previous studies following a similar approach but with different assumptions. The present assessment shows that a reduction of 〈 n e 〉 by a factor ~2 (from 9 to 5 × 1019 m−3) in 15 MA H-mode plasmas leads to a reduction in the required pellet fuelling rate by a factor of four. Results of the analysis of the fuelling requirements for a range of ITER scenarios are found to be similar to those obtained with the JINTRAC code that included 2D modelling of the edge plasma.
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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 | 0.002 |
| 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) | 0.002 | 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".