System risk quantification and decision making support using functional modeling and dynamic Bayesian network

Kim, Junyung; Zhao, Xingang; Shah, Asad Amin; Kang, Hyun Gook

doi:10.1016/j.ress.2021.107880

System risk quantification and decision making support using functional modeling and dynamic Bayesian network

Journal Article · Sun Jun 20 00:00:00 EDT 2021 · Reliability Engineering and System Safety

DOI:https://doi.org/10.1016/j.ress.2021.107880· OSTI ID:1817413

Kim, Junyung ^[1]; ^[2]; Shah, Asad Amin ^[1]; Kang, Hyun Gook ^[1]

Rensselaer Polytechnic Inst., Troy, NY (United States)
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)

Risk-informed decision-making requires a probabilistic assessment of the likelihood of success of control action, given the system status. This paper presents a systematic state transition modeling approach integrating dynamic probabilistic risk assessment with a decision-making process using a dynamic Bayesian network (DBN) coupled with functional modeling. A functional model designed with multilevel flow modeling (MFM) technique was used to build a system state structure inferred by energy, mass, and information flow so that one can verify the developed model with respect to system functionality. The MFM model represents the causal relationship among the nodes, which captures the structure of process parameters and control units. Each node may have multiple possible states, and the DBN structured by the MFM model represents the time-domain transitions among the defined states. Furthermore, the MFM-DBN integrated state transition modeling is a white-box approach that allows one to draw the system's risk profile by updating the system states and supports the decisions probabilistically with physical inference. An example of a simple heating system has been used to illustrate this process, including decision-making support based on quantitative risk profile. For demonstrating its applicability to a complex system operational decision making, a case study of station blackout accident scenario leading to the seal loss of coolant accident in a nuclear power plant is presented. The proposed approach effectively provided the risk profile along time for each option so that the operators can make the best decision, which minimizes the plant risk.

View Accepted Manuscript (DOE)

Research Organization:: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)

Sponsoring Organization:: USDOE

Grant/Contract Number:: AC05-00OR22725; NE0008760; NE0008873

OSTI ID:: 1817413

Alternate ID(s):: OSTI ID: 1807597

Journal Information:: Reliability Engineering and System Safety, Journal Name: Reliability Engineering and System Safety Vol. 215; ISSN 0951-8320

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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42 ENGINEERING
decision-making
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System risk quantification and decision making support using functional modeling and dynamic Bayesian network

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