Studio dell’interazione tra un fascio di particelle energetiche e il plasma di DTT tramite modelli semplificati \n
Bibliographic record
Abstract
La tesi affronta l'argomento dell'iniezione di un fascio di particelle neutre (NBI - Neutral Beam Injection) per un tokamak e della sua interazione con un plasma fusionisico. Dopo una prima parte dedicata alla descrizione di un sistema NBI e della fisica alla base dell'interazione con il plasma, vengono riassunti i principali modelli e tecniche numeriche utilizzati per la descrizione dei fenomeni considerati. Viene poi descritto brevemente il codice METIS, che, nell'ultima parte della tesi, è utilizzato per effettuare una scansione parametrica dell'energia di iniezione. L'effetto di queste variazioni sui principali parametri di plasma viene presentato per il tokamak DTT, attualmente in fase di costruzione al centro ENEA di Frascati. \nThis work deals with the topic of Neutral Beam Injection (NBI) for a tokamak and with the interaction between neutral beam and fusion plasmas. After a first part, dedicated to the description of a NBI system and the physics behind its interaction with the plasma, some of the models and numerical techniques involved in the beam description are listed. The METIS code is then briefly described and used to perform a parametric scan in injection energy, to simulate the behavior of the main plasma parameters of the DTT tokamak under construction at the ENEA Frascati center. \n \n
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".