PSAM 16 Conference Paper Overview
Welcome to the PSAM 16 Conference paper and speaker overview page.
Lead Author: Michelle Bensi Co-author(s): • Somayeh Mohammadi (somayeh@terpmail.umd.edu)
• Tao Liu (liut8@vcu.edu)
• Camille Levine (clevine1@umd.edu)
• Ahmad Al-Douri (clevine1@umd.edu)
• Ray Schneider(schneire@westinghouse.com)
• Zeyun Wu (zwu@vcu.edu)
• Katrina Groth (kgroth@umd.edu)
• Zhegang Ma (zhegang.ma@inl.gov)
| Uncertainty in External Hazard Probabilistic Risk Assessment (PRA): A Structured Taxonomy |
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| Probabilistic risk assessment (PRA) systematically assesses the risks posed to or by a complex system such as a nuclear power plant. Such systems are often comprised of physical structures, mechanical components, and humans that interact with or control the system or respond to system upsets. This complex interdependence leads to many sources of uncertainty that must be characterized in the PRA. This problem is exacerbated when exploring the impacts of external hazards (e.g., earthquakes, floods, and fires) on complex systems due to the additional need to understand and characterize the hazard and its impacts. This study defines a framework for identifying and categorizing the common sources of uncertainty encountered in performing external hazard PRAs for nuclear power plants. The framework may be more generally applicable to the assessment of a wide range of facilities involving potentially high consequence external hazard events. Commentary on drivers of uncertainty in external hazard PRA (XHPRA) (particularly within the context of external flooding hazards) and current gaps/challenges are also provided. |
Paper MB157 Preview
Author and Presentation Info
Lead Author Name: Michelle (Shelby) Bensi (mbensi@umd.edu)
Bio: Michelle (Shelby) Bensi, Ph.D. is an assistant professor in the Department of Civil and Environmental Engineering at the University of Maryland (UMD). Dr. Bensi’s research centers on the application of probabilistic risk assessment concepts and tools as well as statistical/machine learning techniques to problems involving engineered systems exposed to natural hazards. Dr. Bensi focuses primarily on topics related to the probabilistic assessment of natural hazards (e.g., seismic, coastal, inland flooding, precipitation), risk-informed applications, and disaster resilience. Prior to joining the UMD faculty, Dr. Bensi served as an engineer at the United States Nuclear Regulatory Commission where she was heavily involved in the agency's response to the Fukushima Dai-ichi reactor accidents. Dr. Bensi serves as a member of the American Nuclear Society and American Society of Mechanical Engineers (ANS/ASME) Joint Committee on Nuclear Risk Management (JCNRM).
Country: United States of America
Company: University of Maryland
Job Title: Assistant Professor
Bio: Michelle (Shelby) Bensi, Ph.D. is an assistant professor in the Department of Civil and Environmental Engineering at the University of Maryland (UMD). Dr. Bensi’s research centers on the application of probabilistic risk assessment concepts and tools as well as statistical/machine learning techniques to problems involving engineered systems exposed to natural hazards. Dr. Bensi focuses primarily on topics related to the probabilistic assessment of natural hazards (e.g., seismic, coastal, inland flooding, precipitation), risk-informed applications, and disaster resilience. Prior to joining the UMD faculty, Dr. Bensi served as an engineer at the United States Nuclear Regulatory Commission where she was heavily involved in the agency's response to the Fukushima Dai-ichi reactor accidents. Dr. Bensi serves as a member of the American Nuclear Society and American Society of Mechanical Engineers (ANS/ASME) Joint Committee on Nuclear Risk Management (JCNRM).
Country: United States of America
Company: University of Maryland
Job Title: Assistant Professor