Predictive integrated modelling of the hybrid and baseline scenarios of JT-60SA in view of the second operational phase
Bibliographic record
Abstract
Abstract The integrated modelling of two plasma scenarios, hybrid and baseline, envisaged for the second operational phase (OP2) of the JT-60SA tokamak has been performed using the 1.5-dimensional JINTRAC suite of codes and the Bohm/gyro-Bohm (BgB) semi-empirical transport model. The decision to use the BgB model is driven not only by its widespread application in predicting scenarios for JET and JT-60U similar to those anticipated for JT-60SA, but also by its low computational cost. Two versions of the hybrid scenario—3.7 MA/2.28 T and 2.7 MA/1.70 T with P aux = 19 MW—were optimized with respect to the reference METIS simulation to maintain a safety factor with a low magnetic shear region, q min > 1 and low shine-through losses. The results suggest that a high- β N ( ∼ 3 ) regime with a high non-inductive current fraction ( ∼ 70 % ) could be achieved during the initial research phase at 2.7 MA/1.70 T and at a Greenwald density fraction n e / n GW = 0.4 . Hybrid-like q profiles are expected to be more easily obtained at higher Greenwald density fractions (0.6–0.8), while at lower densities, challenges such as hollow current density profiles and reversed q profiles were mitigated by adjusting the negative-neutral beam injection power and the injector configuration. The baseline scenario—4.6 MA/2.28 T with P aux = 17.5 MW—demonstrated potential for high confinementperformance, achieving values of β N ∼ 1.8 , H 98 ∼ 1.0 , and W th ∼ 10 MJ. A scan of the temperature pedestal height and its effect on plasma performance underscores the need to develop a physics-based model capable of accurately predicting the H-mode pedestal.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".