Integrated plasma scenario analysis for the HL-2M tokamak
Bibliographic record
Abstract
Abstract HL-2M is a new medium-sized tokamak under construction at the Southwestern Institute of Physics, dedicated to supporting the critical physics and engineering issues of ITER and CFETR. Analyzing integrated plasma scenarios is essential for assessing performance metrics and foreseeing physics as well as the envisaged experiments of HL-2M. This paper comprehensively presents the kind of expected discharge regimes (conventional inductive (baseline), hybrid and steady-state) of HL-2M based on the integrated suite of codes METIS. The simulation results show that the central electron temperature of the baseline regime can achieve more than 10 keV by injecting 27 MW of heating power with a plasma current of I p = 3 MA and Greenwald fraction f G = 0.65, with the thermal energy and β N reaching 5 MJ and 2.5, respectively. The hybrid regime with f ni = 80%–90% can be realized at I p = 1–1.4 MA with f G around 0.5, where β N is 2.3–2.5 with H 98(y ,2) = 1.1. Because of the effect of the on-axis NBCD, the hybrid steady state, at I p = 1.0 and 1.2, can be achieved more easily than the steady state regimes with reversed shear, corresponding to β N = 2.6 and 3.4. Such studies show that HL-2M is a flexible tokamak with a significant capacity for generating a broad variety of plasmas as a consequence of the different heating and current drive systems installed.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".