Strong correlation between<i>D</i><sub>2</sub>density and electron temperature at the target of divertors found in SOLPS analysis
Bibliographic record
Abstract
A companion paper (Sang et al 2016 Nucl. Fusion ( https://doi.org/10.1088/1741-4326/aa6548 )) reports an assessment, using the SOLPS5.0 (B2-EIRENE) code, of the relative importance of two key aspects of divertor-baffle geometry: (i) divertor closure, and (ii) field-target angle. A wide range of the degree of divertor closure and field-target angle were modeled. An unexpectedly strong and simple correlation has been discovered in these data (and is reported here) between the electron temperature, T et , and the D 2 density, n D 2 t at the target, for T et < 10 eV and extending over two orders of magnitude for each correlate: T et = 6.14 × 10 13 n D 2 t − 0.68 with R 2 = 0.98. The values of T et , and n D 2 t are for each individual flux tube of the computational grid spanning two power decay widths outward from the separatrix. This may imply that achievement of low T et reduces, essentially, to identifying the divertor-baffle geometry which achieves the highest gas density near the target. To try to identify the controlling physics involved, two-point model formatting (2PMF) has been applied to the code output; it finds an equally strong and simple correlation between the 2PMF volumetric power-loss factor, f vol − pwr − loss , and n D 2 t for each flux tube: f vol − pwr − loss = 1.2 × 10 29 n D 2 t − 1.54 with R 2 = 0.93. While these trends are broadly as would be expected, the simplicity, tightness and span of the correlations are not understood at present. Additionally, since more of the volumetric power loss is due to impurities than to deuterium, and as the impurities do not radiate just at the target, it is not evident why f vol − pwr − loss is so strongly correlated with n D 2 t . To address these questions, in future work 2PMF analysis will be extended to compute the individual contributions to f vol − pwr − loss .
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".