Maximizing the ion temperature in an electron heated plasma: from WEST towards larger devices
Bibliographic record
Abstract
Abstract In electron heated plasmas, as the power increases, it is experimentally reported that the ion temperature ( T i ) saturates while the electron temperature ( T e ) increases [Beurskens NF 2022]. As on AUG, W7X and elsewhere, T i saturates around 1.5 keV in WEST L-mode electron heated plasmas while T e reaches 4 keV. Simulations within the integrated model METIS have been compared against a whole WEST campaign consisting mostly of L-mode plasmas with Lower Hybrid heating ranging from 1 to 5.5 MW. In METIS, the collisional equipartition is modeled as well as the turbulent heat transport using the neural network regression of the quasilinear gyrokinetic code QuaLiKiz. The observed T i saturation is well captured by the modeling framework. The saturation correlates with a low ratio of the energy confinement time to the volume averaged electron-ion collisional heat exchange time. It is then shown that T i saturation in electron heated plasma is due to an equipartition time higher than the energy confinement time. In larger devices, no T i saturation is expected nor predicted by physics based integrated modeling used in this work, thanks to equipartition times sufficiently shorter than the energy confinement time.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.051 | 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 teacher head, 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".