A 1-D predictive model for energy and particle transport in H-mode
Bibliographic record
Abstract
In the last few years, significant progress has been made in the understanding of H-mode plasmas (e.g. ion temperature profile stiffness, pedestal physics, etc). Based on this improved understanding, a set of rules (models) comprising a physics picture of the H-mode has been implemented in the ASTRA code in order to improve the understanding of experimental observations and ultimately to provide a predictive capability for ITER complementary to the scaling relations. The model has been verified for consistency with experimental observations in ASDEX-UG and JET plasmas. Numerical coefficients for the transport, required because of simplifications or missing quantitative information, are determined for one plasma (e.g. from JET) and then held constant for all others (JET, ASDEX-UP, ITER). After benchmarking the model to experimental results, it was also applied to ITER. It predicts that Q = 10 can be achieved in ITER but only with at least a 50% deep fuelling contribution (inside the H-mode pedestal). However, in existing machines as well as in our model runs for existing machines, gas puffing is sufficient to achieve the observed density pedestal and line average densities. A second important result from the predictive runs for ITER is that electron energy transport in the plasma core, the neoclassical transport in the pedestal and the CX losses at the plasma edge are important constraints for a better performance. Thus future theoretical and experimental work should concentrate on these areas in order to improve our predictions.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".