Tokamak state-space control modeling
Bibliographic record
Abstract
Nuclear fusion is nowadays one of the newest and most promising clean energies, composing a new application area of Control Engineering, with new and interesting challenges for the control community. In this sense, this paper deals with the modeling of tokamak nuclear fusion reactors. In order to control the creation of unstable modes in fusion processes, it is necessary to derive numerical models suitable for control strategies. The model presented addresses flux and energy conservation issues, discussing the mechanisms behind the creation of uncontrollable modes. The dynamics of the system is given by means of the energy functions which are solved for the currents in the structure, plasma current and plasma position. Thus, the equations for the state variables are derived based on the Hamiltonian equation of motion. In order to solve numerically, this model is linearised around an operation point by taking a Newton-Raphson step. Besides, the system output is completed by considering the equations for the flux and the poloidal field. Finally, the resulting low-order linear model is modified so as to obtain the corresponding state-space model which is verified by means of numerical simulations.
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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 | 0.000 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".