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A methodology to perform dynamic risk assessment using system theory and modeling and simulation: Application to nuclear batteries

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  • Antonello, Federico
  • Buongiorno, Jacopo
  • Zio, Enrico

Abstract

Accidents may occur as a result of complex dynamic processes in interconnected socio-technical systems. Such accidents cannot be explained solely in terms of static chains of failures. Therefore, the traditional Probabilistic Risk Assessment (PRA) framework, which stands on the consideration that accidents are caused by direct failures or chains of events, is not apt to describe the dynamic behavior of the relevant Systems, Structures and Components (SSCs) and assess the risk. This work proposes a novel framework that embeds (i) System-Theoretic Accident Model and Processes (STAMP) principles to guide a qualitative exploration of the SSC threats and hazards, (ii) Modeling and Simulation (M&S) to investigate the SSC dynamic behavior during accidental scenarios, and (iii) the Goal-Tree Success-Tree Master Logic Diagram (GTST-MLD) framework to assess risk quantitatively. The integration of STAMP, M&S and GTST-MLD allows a systematic analysis to provide risk insights, with due account to the SSC dependencies and interactions, and enables a dynamic assessment of the risk profile.

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  • Antonello, Federico & Buongiorno, Jacopo & Zio, Enrico, 2022. "A methodology to perform dynamic risk assessment using system theory and modeling and simulation: Application to nuclear batteries," Reliability Engineering and System Safety, Elsevier, vol. 228(C).
  • Handle: RePEc:eee:reensy:v:228:y:2022:i:c:s0951832022003921
    DOI: 10.1016/j.ress.2022.108769
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    3. Harleen Kaur Sandhu & Saran Srikanth Bodda & Abhinav Gupta, 2023. "A Future with Machine Learning: Review of Condition Assessment of Structures and Mechanical Systems in Nuclear Facilities," Energies, MDPI, vol. 16(6), pages 1-23, March.
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