Integrated modelling of the ramp-up phase of JT-60SA baseline and hybrid scenarios in view of operations
Bibliographic record
Abstract
The JT-60SA superconducting tokamak, built and operated jointly by Europe and Japan, achieved its first plasma in October 2023. This will be the largest magnetically confined fusion experiment in the world for the coming years, before the start of ITER operations, supporting the exploitation of ITER and the investigation of key physics and engineering issues for future demonstration power plants [1, 2]. The parameters of the scenarios that will be studied by JT-60SA, reported in [1, p. 10], have been determined with the help of the equilibrium code ACCOME, then checked and improved with 0.5-D simulations using the METIS code [3] and finally confirmed by means of more sophisticated 1.5-D transport codes [4]. The ramp-up phase of the advanced inductive (hybrid) scenario has also been modelled with the JINTRAC [5] suite of codes, confirming the results of METIS [6], and with the CRONOS code [7]. However, the scenarios that will be developed in the next operational phase (OP2), expected to start in ~2026, will be limited by the heating and current drive availability of the machine, as well as by the heat handling capability of the first lower divertor, and will therefore differ from the target scenarios. Consequently, a great effort is being devoted to the initial development of integrated scenarios, including transport predictions, MHD stability and control, in order to maximise the scientific outcome of the Initial Research Phase within the capabilities of the machine. This work shows the results of the predictive integrated modelling of the baseline and hybrid scenarios in view of OP2, whose global parameters are reported in Table 1. The scenarios reported here are meant as a starting point for future optimizations and a first step for approaching their maximum parameters
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".