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Development of severe accident management advisory and training simulator (SAMAT)
Affiliation:1. GRAAL-Unitat de Bioestadística, Facultat de Medicina, Universitat Autònoma de Barcelona, Bellaterra, Barcelona, Spain;2. Programa de Salud Ambiental, Escuela de Salud Pública, Facultad de Medicina, Universidad de Chile, Chile;3. Facultad de Ciencias de la Salud, Universidad de Tarapacá, Arica, Chile;4. Unit of Infections and Cancer (UNIC), Cancer Epidemiology Research Program (CERP), Catalan Institute of Oncology (ICO)-IDIBELL, Barcelona, Spain;1. K.I.Satpayev Kazakh National Technical University, 22 Satpayev Str., Almaty, 050000, Kazakhstan;2. Suleyman Demirel University, 1/1 Abylaikhan Str., Kaskelen, 040900, Kazakhstan;1. School of Medicine, University of Electronic Science and Technology of China, Chengdu, China;2. Institute of Organ Transplantation, Sichuan Provincial People''s Hospital, University of Electronic Science and Technology of China, Chengdu, China;3. Department of Gastrointestinal Surgery, Sichuan Provincial People''s Hospital, Chengdu, China;4. Organ Transplantation Translational Medicine Key Laboratory of Sichuan Province, Chengdu, China
Abstract:The most operator support systems including the training simulator have been developed to assist the operator and they cover from normal operation to emergency operation. For the severe accident, the overall architecture for severe accident management is being developed in some developed countries according to the development of severe accident management guidelines which are the skeleton of severe accident management architecture. In Korea, the severe accident management guideline for KSNP was recently developed and it is expected to be a central axis of logical flow for severe accident management. There are a lot of uncertainties in the severe accident phenomena and scenarios and one of the major issues for developing a operator support system for a severe accident is the reduction of these uncertainties. In this paper, the severe accident management advisory system with training simulator, SAMAT, is developed as all available information for a severe accident are re-organized and provided to the management staff in order to reduce the uncertainties. The developed system includes the graphical display for plant and equipment status, the previous research results by knowledge-base technique, and the expected plant behavior using the severe accident training simulator. The plant model used in this paper is oriented to severe accident phenomena and thus can simulate the plant behavior for a severe accident. Therefore, the developed system may make a central role of the information source for decision-making for a severe accident management, and will be used as the training simulator for severe accident management.
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