An assessment of full wave effects on the propagation and absorption of lower hybrid waves
Bibliographic record
Abstract
Lower hybrid (LH) waves (Ωci⪡ω⪡Ωce, where Ωi,e≡Zi,eeB/mi,ec) have the attractive property of damping strongly via electron Landau resonance on relatively fast tail electrons and consequently are well-suited to driving current. Established modeling techniques use Wentzel–Kramers–Brillouin (WKB) expansions with self-consistent non-Maxwellian distributions. Higher order WKB expansions have shown some effects on the parallel wave number evolution and consequently on the damping due to diffraction [G. Pereverzev, Nucl. Fusion 32, 1091 (1991)]. A massively parallel version of the TORIC full wave electromagnetic field solver valid in the LH range of frequencies has been developed [J. C. Wright et al., Comm. Comp. Phys. 4, 545 (2008)] and coupled to an electron Fokker–Planck solver CQL3D [R. W. Harvey and M. G. McCoy, in Proceedings of the IAEA Technical Committee Meeting, Montreal, 1992 (IAEA Institute of Physics Publishing, Vienna, 1993), USDOC/NTIS Document No. DE93002962, pp. 489–526] in order to self-consistently evolve nonthermal electron distributions characteristic of LH current drive (LHCD) experiments in devices such as Alcator C-Mod and ITER (B0≈5 T, ne0≈1×1020 m−3). These simulations represent the first ever self-consistent simulations of LHCD utilizing both a full wave and Fokker–Planck calculation in toroidal geometry.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".