Surface temperature measurement from infrared synthetic diagnostic in preparation for ITER operations

M.-H. Aumeunier, A. Juven, J. Gerardin, C-M. B. Cisse, S. Pamela, R. Miorelli, C. Reboud, F. Retailleau, J. Marot, F. Rigollet2024年8月Nuclear FusionIF 3 出版社

The protection of ITER in-vessel components and the plasma-wall interaction studies will be based on a large network of infrared (IR) cameras covering 70% of the tokamak. The surface temperature measurement from IR images remains challenging due to the presence of metallic targets, with changes in surface thermo-radiative properties (emissivity) and the presence of multiple reflections. The paper provides an overview of major progress to improve the interpretation of IR image and to get more reliable surface temperature from IR synthetic diagnostics. The paper presents the latest development of (1) the forward model to include the modelling of the edge localised modes and a new advanced camera that is better adapted to experimental data (2) the inverse model to retrieve the emissivity of the targets and the surface temperature from a neural network trained exclusively from synthetic IR images. Promising results have been obtained both from simulated test images with an estimated emissivity better than 0.05 and a surface temperature better than 10%, and from WEST experimental images of ITER-like wide-angle to filter reflection patterns.

iter高精度(タイトル一致)

AIによる論文要約

ENThis paper should be read by fusion researchers and engineers involved in the development of IR diagnostics for ITER, as well as those interested in improving the interpretation of IR images for surface temperature measurements in fusion devices.#ITERDiagnostics #SurfaceTemperatureMeasurement #InfraredImaging #PlasmaWallInteraction #FusionResearch

This paper discusses advancements in interpreting infrared (IR) images to accurately measure surface temperature of ITER's in-vessel components. It presents a new forward model to simulate edge localized modes and a new camera, and an inverse model using neural networks to retrieve emissivity and surface temperature from synthetic IR images. The results show improved accuracy in estimating emissivity and surface temperature, which is crucial for protecting ITER components and studying plasma-wall interactions.

この論文は、ITERの内部構造物の保護と壁面相互作用の研究に不可欠な赤外線カメラネットワークについて述べている。金属製の対象物の放射率変化や複数の反射の影響により、赤外線画像から正確な表面温度を得るのは困難である。本論文では、(1)エッジ局所化モード(ELM)のモデル化や新しい適応カメラの導入による前方モデルの改善、(2)合成赤外線画像から放射率と表面温度を機械学習で推定する逆モデルの開発について報告している。