Modelling of charge-exchange induced NBI losses in the COMPASS upgrade tokamak
Bibliographic record
Abstract
Abstract The COMPASS upgrade tokamak (Panek et al 2017 Fusion Eng. Des. 123 11–16) will be a tokamak of major radius R 0 = 0.894 m with the possibility to reach high field (B t ∼ 5 T) and high current (I p ∼ 2 MA). The machine should see its first plasma in 2023 and H-mode plasma will be obtained from 2025. The main auxiliary heating system used to access H-mode will be 4 MW of neutral beam injection (NBI) power. The NBI will have a nominal injection energy of 80 keV, a maximum injection radius R tan = 0.65 m and will create a population of well-confined energetic D ions. In this contribution, our modelling studies the NBI deposition and losses when a significant edge background density of neutrals is assumed. We follow the fast ions in the 3D field generated by the 16 toroidal field (TF) coils using the upgraded EBdyna orbit solver (Jaulmes et al 2014 Nucl. Fusion 54 104013). We have implemented a Coulomb collision operator similar to that of NUBEAM (Goldston et al 1981 J. Comput. Phys. 43 61) and a charge-exchange operator that follows neutrals and allows for multiple re-ionizations. Detailed integrated modelling with the METIS code (Artaud et al 2018 Nucl. Fusion 58 105001) yields the pressure and current profiles for various sets of achievable engineering parameters. The FIESTA code (Cunningham 2013 Fusion Eng. Des. 88 3238–3247) calculates the equilibrium and a Biot–Savart solver is used to calculate the intensity of the perturbation induced by the TF coils. Initial distributions of the NBI born fast ions are obtained from the newly developed NUR code, based on Suzuki et al (1998 Plasma Phys. Control. Fusion 40 2097). We evolve the NBI ions during the complete thermalization process and we calculate the amount of NBI ions loss in the edge region due to neutralizations. Results indicate the NBI losses for various injection geometries, various engineering parameters and various assumptions on the magnitude of the background neutral densities.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".