TAILIEUCHUNG - Artificial neural network surrogate development of equivalence models for nuclear data uncertainty propagation in scenario studies

Scenario studies simulate the whole fuel cycle over a period of time, from extraction of natural resources to geological storage. Through the comparison of different reactor fleet evolutions and fuel management options, they constitute a decision-making support. | Artificial neural network surrogate development of equivalence models for nuclear data uncertainty propagation in scenario studies EPJ Nuclear Sci. Technol. 3 22 2017 Nuclear Sciences G. Krivtchik et al. published by EDP Sciences 2017 amp Technologies DOI epjn 2017012 Available online at http REGULAR ARTICLE Artificial neural network surrogate development of equivalence models for nuclear data uncertainty propagation in scenario studies Guillaume Krivtchik Patrick Blaise and Christine Coquelet-Pascal Atomic Energy and Alternative Energies Commission CEA DEN Reactor Studies Department DER Cadarache 13108 Saint-Paul-lez-Durance France Received 4 January 2017 Received in final form 7 April 2017 Accepted 9 May 2017 Abstract. Scenario studies simulate the whole fuel cycle over a period of time from extraction of natural resources to geological storage. Through the comparison of different reactor fleet evolutions and fuel management options they constitute a decision-making support. Consequently uncertainty propagation studies which are necessary to assess the robustness of the studies are strategic. Among numerous types of physical model in scenario computation that generate uncertainty the equivalence models built for calculating fresh fuel enrichment for instance plutonium content in PWR MOX so as to be representative of nominal fuel behavior are very important. The equivalence condition is generally formulated in terms of end-of-cycle mean core reactivity. As this results from a physical computation it is therefore associated with an uncertainty. A state-of-the-art of equivalence models is exposed and discussed. It is shown that the existing equivalent models implemented in scenario codes such as COSI6 are not suited to uncertainty propagation computation for the following reasons i existing analytical models neglect irradiation which has a strong impact on the result and its uncertainty ii current black-box models are not suited to cross-section

• Năng lượng
• Scenario studies simulate
• Decision making support
• Reactor fleet evolution
• Fuel management options
• Artificial neural network
• Mạng thần kinh
• mạng lưới
• thần kinh
• dự đoán mạng lưới
• báo cáo hóa học
• công trình nghiên cứu về hóa học
• tài liệu về hóa học
• cách trình bày báo cáo