The Chodura sheath for angles of a few degrees between the magnetic field and the surface of divertor targets and limiters
Bibliographic record
Abstract
Abstract To achieve low deposited power flux density to solid surfaces in magnetic fusion devices, very small values of α are required, where α is the angle between B and the surface tangent. For an oblique magnetic field, there exists in front of the solid surface a Chodura sheath (CS) (also known as the ‘magnetic pre-sheath’) of thickness several ρ i, the ion Larmor radius. The standard assumption is that the CS is additional to the Debye sheath (DS) of thickness several λ D, the Debye length. Simple fluid modelling for collisionless CS conditions gives the drop in normalized electrostatic potential across the CS as eΔφ CS/kT e = ln(sin α). For an electrically floating wall there is the separate constraint of ambipolar flow to the wall eΔφ floating/kT e = 0.5 ln[(2π m e/m i)(1 + T i/T e)], where Δφ floating = Δφ CS + Δφ DS. For the case of a deuterium plasma and T i = T e, |eΔφ floating/kT e| = 2.84. For α < 3.35°, |eΔφ CS/kT e| exceeds 2.84 which evidently implies that the DS ceases to exist for such values of α and the entire potential drop would then occur across the CS. New analysis of the CS provides solutions for a number of quantities of practical importance, which improve on the solutions presently in use in models and edge impurity codes. Compared with the latter, the results of the present analysis indicate that (i) the E-field directed towards the solid surface is stronger and (ii) the plasma density drops more rapidly approaching the solid surface. The effect of (i) is to increase the probability of prompt local deposition of sputtered particles, while (ii) has the opposite effect.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".