Particle transport modelling for D/T ratio control experiments in JET
Bibliographic record
Abstract
Abstract This study presents results from particle transport modelling for D/T ratio control experiments conducted during the JET DTE3 campaign. TRANSP interpretative and JETTO predictive simulations for D and T densities were performed and their results are discussed. Despite using simplified models based on Bohm-gyroBohm transport, the simulations incorporate self-consistent sources and impurities and cover the full radial range. The simplified models effectively reproduced the evolution of electron density and neutron rates. However, the predicted D/T ratio evolution responded to control requests faster than what was experimentally observed, suggesting that the employed models possess certain limitations. Specific cases involving swapped gas injection species were also studied, highlighting the potential applicability of the proposed methodology in future experimental scenarios. TRANSP interpretative analysis indicates that a Real-Time (RT) scheme employing simplified quasi-neutrality and Z eff estimations can be implemented with high degree of reliability. JETTO predictive analysis suggests that a simplified modelling approach for the behaviour of the future RT controllers of D/T mixture can be effective. Such an approach involves using measured temperatures, omitting explicit modelling of the SOL physics, and adopting simplified assumptions for the particle transport.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".