Artificial Neural Network-Based Tomography Reconstruction of Plasma Radiation Distribution at GOLEM Tokamak

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21340%2F24%3A00376800" target="_blank" >RIV/68407700:21340/24:00376800 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1007/s10894-024-00458-z" target="_blank" >https://doi.org/10.1007/s10894-024-00458-z</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s10894-024-00458-z" target="_blank" >10.1007/s10894-024-00458-z</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Artificial Neural Network-Based Tomography Reconstruction of Plasma Radiation Distribution at GOLEM Tokamak

Popis výsledku v původním jazyce

The paper presents an artificial neural network-based model for tomography reconstruction of visible plasma radiation distribution at the GOLEM tokamak. The model was trained using a dataset from emissivity phantoms and associated synthetic measurements from a poloidal cross-section of the GOLEM tokamak. The model validation was performed on the prediction of various unseen phantom samples with shapes similar to those in the training dataset. The backfit of line-integrated measurements indicates the considerable potential of the proposed model for reconstructing the position, size, shape and intensity of the radiation function of one cross section. Additionally, the neural network-based model offers a significantly shorter prediction time compared to traditional tomography methods, providing a substantial advantage.

Název v anglickém jazyce

Artificial Neural Network-Based Tomography Reconstruction of Plasma Radiation Distribution at GOLEM Tokamak

Popis výsledku anglicky

The paper presents an artificial neural network-based model for tomography reconstruction of visible plasma radiation distribution at the GOLEM tokamak. The model was trained using a dataset from emissivity phantoms and associated synthetic measurements from a poloidal cross-section of the GOLEM tokamak. The model validation was performed on the prediction of various unseen phantom samples with shapes similar to those in the training dataset. The backfit of line-integrated measurements indicates the considerable potential of the proposed model for reconstructing the position, size, shape and intensity of the radiation function of one cross section. Additionally, the neural network-based model offers a significantly shorter prediction time compared to traditional tomography methods, providing a substantial advantage.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10305 - Fluids and plasma physics (including surface physics)

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2024

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Journal of Fusion Energy

ISSN

0164-0313

e-ISSN

1572-9591

Svazek periodika

43

Číslo periodika v rámci svazku

2

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

6

Strana od-do

—

Kód UT WoS článku

001304506000001

EID výsledku v databázi Scopus

2-s2.0-85203049921