An interpretable isoflux-based observer for plasma shape control errors in tokamaks
Bibliographic record
Abstract
In tokamaks, plasma shape control is often achieved through a so-called isoflux approach that regulates the poloidal flux differences between a reference point and a set of control points and magnetic field values at suitable locations to obtain the desired shape. Despite its simplicity, this approach presents two primary drawbacks: first, a method is needed to translate desired shape modifications, e.g. , radial or vertical shifts, into variations of the poloidal flux and magnetic field references; second, interpreting controller performance metrics may not be straightforward, since control errors are expressed in terms of physical quantities, i.e. , flux differences, magnetic fields, that cannot be directly related to positional errors. In this work, we propose a comprehensive methodology to establish relationships that link variations of poloidal flux and magnetic field values concerning a nominal plasma equilibrium in a predefined set of shape control points to local deformations of the Last Closed Flux Surface (LCFS). The effectiveness of this approach is demonstrated on the Tokamak à Configuration Variable (TCV) model through extensive simulations that consider various plasma configurations and shape modifications. • Isoflux methods control plasma shape by adjusting poloidal fluxes and magnetic fields. • Translate and interpreting control errors into shape modifications is challenging. • Shape observer to estimate deformations of the LCFS using flux and field variations. • Demonstration of the effectiveness of the shape observer on the TCV Tokamak.
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 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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".