An integrated probabilistic risk assessment methodology for maritime transportation of spent nuclear fuel based on event tree and hydrodynamic model

Longlong Tao, Liwei Chen, Daochuan Ge, Yuantao Yao, Fang Ruan, Jie Wu and Jie Yu

Reliability Engineering and System Safety, 2022, vol. 227, issue C

Abstract: Spent nuclear fuel maritime transportation (SNFMT) accident may cause radiation hazards to personnel, vessels, and the ocean environment. Current risk assessment methods of SNFMT lack full consideration and quantification of the risk indicators. In this work, an integrated probabilistic risk assessment (IPRA) methodology incorporating multiple risk factors-based accident probability model and public dose-based radiological consequence model quantitatively is proposed for SNFMT. First, from the sociotechnical-environmental risk perspective, the SMCETC (Ship, Management, Crew, Environment, Tank, Channel) comprehensive risk indicators are identified for ET-FT modeling. Second, considering the effects of continuous emissions, water depth, tidal cycle, and radioactive decay, a shallow water equations-based hydrodynamic model is established to simulate the radionuclide concentration in coastal water. Third, the ET-FT model-based accident frequency and the radionuclide concentration-based population radiation consequence are integrated, and subsequently the public radioactive risks are obtained. Finally, a case study is presented to demonstrate the feasibility and value of the proposed method. The time-related public radioactive risks were quantified and 28 highly safety importance risk factors were found and ranked. The proposed IPRA methodology integrates deterministic and probabilistic modeling perspectives, and provides a comprehensive risk assessment tool for SNFMT.

Keywords: Spent nuclear fuel; Maritime transportation; Multiple risk indicators; IPRA; Public radioactive risks (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (9)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:227:y:2022:i:c:s0951832022003507

DOI: 10.1016/j.ress.2022.108726

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