Neoclassical electron transport calculation by using δf Monte Carlo method
Bibliographic record
Abstract
High electron temperature plasmas with steep temperature gradient in the core are obtained in recent experiments in the Large Helical Device [A. Komori et al., Fusion Sci. Technol. 58, 1 (2010)]. Such plasmas are called core electron-root confinement (CERC) and have attracted much attention. In typical CERC plasmas, the radial electric field shows a transition phenomenon from a small negative value (ion root) to a large positive value (electron root) and the radial electric field in helical plasmas are determined dominantly by the ambipolar condition of neoclassical particle flux. To investigate such plasmas’ neoclassical transport precisely, the numerical neoclassical transport code, FORTEC-3D [S. Satake et al., J. Plasma Fusion Res. 1, 002 (2006)], which solves drift kinetic equation based on δf Monte Carlo method and has been applied for ion species so far, is extended to treat electron neoclassical transport. To check the validity of our new FORTEC-3D code, benchmark calculations are carried out with GSRAKE [C. D. Beidler et al., Plasma Phys. Controlled Fusion 43, 1131 (2001)] and DCOM/NNW [A. Wakasa et al., Jpn. J. Appl. Phys. 46, 1157 (2007)] codes which calculate neoclassical transport using certain approximations. The benchmark calculation shows a good agreement among FORTEC-3D, GSRAKE and DCOM/NNW codes for a low temperature (Te(0)=1.0 keV) plasma. It is also confirmed that finite orbit width effect included in FORTEC-3D affects little neoclassical transport even for the low collisionality plasma if the plasma is at the low temperature. However, for a higher temperature (5 keV at the core) plasma, significant difference arises among FORTEC-3D, GSRAKE, and DCOM/NNW. These results show an importance to evaluate electron neoclassical transport by solving the kinetic equation rigorously including effect of finite radial drift for high electron temperature plasmas.
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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 | 0.001 | 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".