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Record W7064166444

Analysis of nuclear fusion reactor discharge simulations using METIS

2023· dissertation· en· W7064166444 on OpenAlexaboutno aff

Bibliographic record

VenueUPCommons institutional repository (Universitat Politècnica de Catalunya) · 2023
Typedissertation
Languageen
FieldPhysics and Astronomy
TopicMagnetic confinement fusion research
Canadian institutionsnot available
Fundersnot available
KeywordsMetisFusion powerCode (set theory)Nuclear fusionSuite
DOInot available

Abstract

fetched live from OpenAlex

The Nuclear Engineering Department of UPC has the need of a nuclear fusion plasma simulator for its research activities and educational tasks. For many years, PRETOR has been the program used for this purpose, but it has become obsolete. Nowadays there are more modern simulation codes for fusion reactor’s plasma shots, like CRONOS and METIS. Due to the complexity of CRONOS, METIS was more suitable. The Nuclear Engineering Section of UPC has been authorised by ITER Organisation to use METIS for educational and research purposes after the signing of an agreement named "Agreement on Cooperation on the ITER Modelling and Analysis Suite (IMAS) and Related Repositories by Universitat Politècnica de Catalunya". This project has gotten inside METIS program, to analyse the obtained simulations on fusion plasma, trying to understand some pieces of code as well. METIS is a code based on MATLAB, combined with some modules programmed with Fortran, C and C++, developed by J.F. Artaud from the Institute for Magnetic Fusion Research, in France. Despite being METIS a very powerful tool on the simulation of fusion reactors with a great quantity of applications, as it is a restricted program only available for researchers from the same field, it does not offer enough help information for those who begin to work with it. This document includes a brief introduction on fusion physics, the principles on magnetic confinement reactors and the presentation of some different integrated modelling codes for fusion plasma. In order to complement the METIS help guide, we have used the knowledge acquired during our work to prepare a draft manual that we hope to be useful whenever it is intended to work on a teaching practices program. During this process we have tried to understand on which models the program is based, getting to analyse the source code, study and compare simulations. Furthermore, a great amount of work done in this project has been focused on the development of a setup of MATLAB functions designed to make easier the study of METIS outputs. This has permitted us to outline an example on how it would be possible to incorporate new functionalities inside METIS source code

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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.

Opus teacher head0.020
GPT teacher head0.287
Teacher spread0.266 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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