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RISS 인기검색어
- 검색키워드
- 저자 : Chatzidakis Stylianos (검색결과 3 건)
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Chatzidakis, Stylianos 한국원자력산업회의 2017 원자력산업 Vol.37 No.5
"많은 나라들이 앞으로도 계속하여 원자력 발전을 채택할 것으로 예상되고 있는 현시점에서 각국의 정부와 국제기구들은 핵안보 문제를 해결하기 위한 일에 보다 많은 관심과 노력을 기울이겠다는 약속을 해야만 한다."
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Image reconstruction algorithm for momentum dependent muon scattering tomography
Bae JungHyun,Montgomery Rose,Chatzidakis Stylianos 한국원자력학회 2024 Nuclear Engineering and Technology Vol.56 No.5
Nondestructive radiography using cosmic ray muons has been used for decades to monitor nuclear reactor and spent nuclear fuel storage. Because nuclear fuel assemblies are highly dense and large, typical radiation probes such as x-rays cannot penetrate these target imaging objects. Although cosmic ray muons are highly penetrative for nuclear fuels as a result of their relatively high energy, the wide application of muon tomography is limited because of naturally low cosmic ray muon flux. This work presents a new image reconstruction algorithm to maximize the utility of cosmic ray muon in tomography applications. Muon momentum information is used to improve imaging resolution, as well as muon scattering angle. In this work, a new convolution was introduced known as M-value, which is a mathematical integration of two measured quantities: scattering angle and momentum. It captures the objects’ quantity and density in a way that is easy to use with image reconstruction algorithms. The results demonstrate how to reconstruct images when muon momentum measurements are included in a typical muon scattering tomography algorithm. Using M-value improves muon tomography image resolution by replacing the scattering angle value without increasing computation costs. This new algorithm is projected to be a standard nondestructive radiography technique for spent nuclear fuel and nuclear material management.
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Papamarkou, Theodore,Guy, Hayley,Kroencke, Bryce,Miller, Jordan,Robinette, Preston,Schultz, Daniel,Hinkle, Jacob,Pullum, Laura,Schuman, Catherine,Renshaw, Jeremy,Chatzidakis, Stylianos Korean Nuclear Society 2021 Nuclear Engineering and Technology Vol.53 No.2
Nondestructive evaluation methods play an important role in ensuring component integrity and safety in many industries. Operator fatigue can play a critical role in the reliability of such methods. This is important for inspecting high value assets or assets with a high consequence of failure, such as aerospace and nuclear components. Recent advances in convolution neural networks can support and automate these inspection efforts. This paper proposes using residual neural networks (ResNets) for real-time detection of corrosion, including iron oxide discoloration, pitting and stress corrosion cracking, in dry storage stainless steel canisters housing used nuclear fuel. The proposed approach crops nuclear canister images into smaller tiles, trains a ResNet on these tiles, and classifies images as corroded or intact using the per-image count of tiles predicted as corroded by the ResNet. The results demonstrate that such a deep learning approach allows to detect the locus of corrosion via smaller tiles, and at the same time to infer with high accuracy whether an image comes from a corroded canister. Thereby, the proposed approach holds promise to automate and speed up nuclear fuel canister inspections, to minimize inspection costs, and to partially replace human-conducted onsite inspections, thus reducing radiation doses to personnel.
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