Large-Scale Simulation of Energetic Particle Driven Magnetohydrodynamic Instabilities in ITER Plasmas
Bibliographic record
Abstract
Magnetohydrodynamic (MHD) instabilities driven by energetic alpha particles and beam deuterium particles are investigated for ITER operation scenarios using a hybrid simulation code for energetic particles interacting with an MHD fluid. The particle simulation method with finite Larmor radius effects is applied to both alpha and beam deuterium particles. For the steady-state scenario with 9 MA plasma current, beta-induced Alfvén eigenmodes (BAE modes) with low toroidal mode number (n = 3, 5) were found to become dominant in the nonlinear phase although many toroidal Alfvén eigenmodes (TAE modes) with n ∼ 15 are most unstable in the linear phase. The redistribution of energetic particles with δβα ∼ δβbeam ∼ 0.07%, which respectively correspond to 6% and 8% of the central values, occurs in the nonlinear phase. When the toroidal mode number of the fluctuations is restricted to n ≤ 8, the redistribution is substantially reduced, thus, suggesting that the resonance overlap between the n ∼ 15 TAE and low-n BAE modes enhances the energetic particle transport in the run with full toroidal mode numbers. For the ITER scenario with 15 MA plasma current, an MHD instability with n = 3 that peaks around the q = 1(q is the safety factor) magnetic surfaces is driven by bulk plasma current and bulk pressure, and results in significant redistribution of alpha particles with δβα ∼ 0.3%. For the equilibrium profile with the safety factor profile uniformly raised by 0.1 to remove the q = 1 surfaces, only a benign MHD instability occurs and the energetic particle transport is negligible.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".