Formulation of A Risk Assessment Framework Capable of Analyzing Nuclear Power Multiunit Accident Scenarios

Cai, Yinan; Golay, Michael W.

doi:10.1016/j.ress.2020.107040

Cited by 8 publications

(2 citation statements)

References 7 publications

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“…In this paper, we focus upon explaining high‐level modeling assumptions that are applicable to public emergencies in general. Technical details related to the Fukushima accidents and nuclear power plants are available in Cai and Golay ( 2020b , 2021 ). Baseline assumptions for modeling parameters employed in this work are presented in Appendix 1 Table A1 .…”

Section: Discussion Of the 2011 Fukushima Nuclear Power Plant Accidentmentioning

confidence: 99%

“… Unit 1 DC diagnosis time t – dt Diagnosis result of DC system at the end of the previous time t – dt Unit 1 Depressurization diagnosis time t – dt Diagnosis result of Depressurization system at the end of the previous time t – dt Unit 1 Portable cooling diagnosis time t – dt Diagnosis result of portable cooling system at the end of the previous time t – dt Status of systems at the end of the current time step t (green nodes). Detailed assumptions for these systems and their interactions are available in (Cai & Golay, 2020b , 2021 ). Unit 1 DC Status time t DC power status at the end of current time step t Status of DC system depends upon its status at the previous time step, its diagnosis result and site conditions.…”

Section: Table A1mentioning

confidence: 99%

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A dynamic Bayesian network‐based emergency decision‐making framework highlighting emergency propagations: Illustrated using the Fukushima nuclear accidents and the Covid‐19 pandemic

Cai

Golay

2022

Risk Analysis

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When facing public emergencies, human societies need to make decisions rapidly in order to mitigate the problems. However, this process can be difficult due to complexity of the emergency scenarios and lack of systematic methods for analyzing them. In the work reported here, we develop a framework based upon dynamic Bayesian networks in order to simulate emergency scenarios and support corresponding decisions. In this framework, we highlight the importance of emergency propagation, which is a critical factor often ignored by decisionmakers. We illustrate that failure of considering emergency propagation can lead to suboptimal mitigation strategies. By incorporating this critical factor, our framework enables decisionmakers to identify optimal response strategies minimizing emergency impacts. Scenarios developed from two public emergencies: the 2011 Fukushima nuclear power plant accidents and the Covid‐19 pandemic, are utilized to illustrate the framework in this paper. Capabilities of the framework in supporting decision making in both events illustrate its generality and adaptability when dealing with complex real‐world situations. Our analysis results reveal many similarities between these two seemingly distinct events. This indicates that seemingly unrelated emergencies can share many common features beyond their idiosyncratic characteristics. Valuable mitigation insights can be obtained by analyzing a broad range of past emergencies systematically.

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Section: Discussion Of the 2011 Fukushima Nuclear Power Plant Accidentmentioning

confidence: 99%