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Implementation of probabilistic assessments to support the ASTRID decay heat removal systems design process

Abstract : ASTRID, the Advanced Sodium Technological Reactor for Industrial Demonstration, is a GenIV technological demonstrator. Its purpose will be to demonstrate the progress made in the field of Sodium Fast Reactor (SFR) technology on an industrial scale by qualifying innovative options, especially relative to safety and operability. To support the ASTRID design process during the conceptual design phase, probabilistic assessments have been developed since 2012. They are based on the conventional Fault Tree (FT)/Event Tree (ET) approach and on the determination of the minimal decay heat removal (DHR) systems requirement. Limited to a period of one week, without repair of component malfunctions, their goal is to provide probabilistic insights in the assessment of various design choices and to supplement the deterministic approach in the objective of continuous safety improvements throughout the design process. Nevertheless, the conventional FT/ET "static approach", developed for PWRs, appears to be inadequate for SFRs in the objectives of assessing the global risk and contributing to the demonstration of the practical elimination of the situation "loss of DHR function" for longer periods of time. Since DHR systems have to operate during long mission times, PSA modelling must consider the repair possibility, adapt the systems requirement to decay heat evolution and take into account all available systems at any time. Therefore, different dynamic probabilistic approaches are developed in a prospective way to comply with the safety demonstration. A first calculation will be performed by the end of the decade to support the demonstration of practical elimination of the DHR function of ASTRID.
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F. Aubert, B. Baude, P. Gauthe, M. Marquès, N. Perot, et al.. Implementation of probabilistic assessments to support the ASTRID decay heat removal systems design process. Nuclear Engineering and Design, Elsevier, 2018, 340, pp.405-413. ⟨10.1016/j.nucengdes.2018.09.002⟩. ⟨cea-02429987⟩



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