Temperature estimation in fusion devices using machine learning techniques on infrared specular synthetic data - CEA - Commissariat à l’énergie atomique et aux énergies alternatives Accéder directement au contenu
Communication Dans Un Congrès Année : 2022

Temperature estimation in fusion devices using machine learning techniques on infrared specular synthetic data

Résumé

Infrared (IR) imaging systems are common diagnostics for monitoring in-vessel components in thermonuclear fusion devices (tokamak). Nevertheless, IR interpretation in fully metallic environment is complex due to the presence of multiple reflections and the change of optical properties of materials as the fusion operation progresses. This causes high errors on the surface temperature measurement which is a risk for machine protection. The paper presents a first demonstration of simulation-assisted machine learning method for retrieving the surface temperature from IR measurement on metallic targets with unknown properties. The technique relies on the training of a convolutional neural network on a synthetic dataset generated by a deterministic ray tracer. The performances of such an approach is first proven on tokamak prototype considering pure specular surfaces.
Fichier principal
Vignette du fichier
Temperature_Estimation_in_Fusion_Devices_using_Machine_Learning_techniques_on_Infrared_Specular_Synthetic_Data (2).pdf (2.1 Mo) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)

Dates et versions

cea-04555865 , version 1 (23-04-2024)

Identifiants

Citer

Alexis Juven, Marie-Helene Aumeunier, Romain Brunet, Mickael Le Bohec, Mouloud Adel, et al.. Temperature estimation in fusion devices using machine learning techniques on infrared specular synthetic data. IVMSP 2022 - IEEE 14th Image, Video, and Multidimensional Signal Processing Workshop, Jun 2022, Nafplio, Greece. ⟨10.1109/IVMSP54334.2022.9816270⟩. ⟨cea-04555865⟩
0 Consultations
0 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More