Modelling of LHCD at various densities in Tore Supra tokamak
Bibliographic record
Abstract
In the Tore Supra tokamak, lower hybrid (LH) waves are used to heat electrons and drive a toroidal current in a variety of plasma conditions, including fully non-inductive scenarios. The LH wave is coupled to the plasma using a fully active multijunction (FAM) launcher and/or a ITER-relevant passive active multijunction (PAM) launcher [1]. Hard X-ray measurements during high density LHCD experiments show a photon count decreasing with density at a much faster rate than anticipated [2]. In this work, LHCD modelling using a new modelling suite is presented. Tore Supra discharges are simulated using the METIS transport code for plasma equilibrium and kinetic profiles. Using LH spectra from the coupling code ALOHA, the wave propagation is calculated using the ray-tracing code C3PO. It is found that 36 rays, corresponding to the six waveguide rows and the six main lobes in the LH spectrum, are sufficient to correctly describe the LH wave propagation. The electron distribution function is calculated by the 3D Fokker-Planck code LUKE. Full convergence is obtained in the self-consistent calculation of the distribution function and the power absorption along all rays. The driven current calculated by LUKE and a synthetic diagnostic of the bremsstrahlung emission (R5X2) provide a comparison of LHCD modelling results with experimental measurements. LHCD simulations are found to agree well with experimental observations for relatively low density plasmas ( ¯ n < 2◊ 10 19 m 3 ). At higher density, the LH wave propagation enters a new regime for which the validity of ray tracing modelling becomes questionable. Not surprisingly, the comparison between modelling and experiments is not satisfactory in these conditions. Possible mechanisms explaining the strong decrease in hard X-ray signal at higher density are discussed.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".