ICRH antenna coupling physics and optimum plasma edge density profile. Application to ITER
Bibliographic record
Abstract
The performance of an ICRH system depends on the coupling capabilities of the antenna to the inhomogeneous plasma profile in front of it. The aim of this study is to understand the key physics phenomena contributing to the coupling. It is shown that the following plasma density profile characteristics are decisive: (i) distance between the antenna and the wave cutoff density, (ii) position of an optimum density with respect to the cutoff one and (iii) the density gradient leading from this optimum density to the plasma bulk. At each step of the analysis approximate relations are derived and the loading due to the plasma is compared with that of an isotropic dielectric medium in view of the application for modelling or dummy load testing. Examples are taken starting from the case of the projected ITER antenna array with its different phasing cases in front of the plasma edge profile used in the conceptual design phase. It is shown that, for the same antenna–cutoff distance, slight profile modifications can lead to substantial coupling and therefore power capability variations. Stronger profile modifications and some critically shaped resonant edge profiles are also analysed. The usefulness of a dielectric medium to simulate plasma loading is discussed in an appendix.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".