Integrated modelling for prediction of optimized ITER performance
Bibliographic record
Abstract
ITER hybrid and target steady-state fusion burn scenarios are simulated using the PTRANSP integrated modelling code together with input from the TSC code. In the hybrid scenarios, the majority of the current is driven inductively; whereas, for the target steady-state scenarios, approximately 22% of the current (at 1000 s) is driven inductively with the remaining current driven by the bootstrap, neutral beam and radio frequency sources. Predictive simulations are carried out using either the new Multi-Mode or the GLF23 anomalous transport model. Momentum transport is used to compute the toroidal angular frequency profile which, in turn, is used to compute the self-consistent flow shear suppression of anomalous transport. The simulations of the hybrid scenario indicate that the fusion power production at 1000 s will be approximately 500 MW corresponding to a fusion Q = 9.4. The fusion power predicted in the simulations of the target steady-state scenarios is found to depend on the time dependence of the input heating and associated current drive. It is found that turning off some components of auxiliary heating causes the fusion power production to increase. The fusion power obtained in the target steady-state scenarios, depending on the transport model and input injected power, ranges from 168 MW up to 226 MW, corresponding to a fusion Q ranging from 2.0 to 6.8.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".