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Hydrogen quantitative risk assessment workshop proceedings

Harris, Aaron P.; Groth, Katrina G.

The Quantitative Risk Assessment (QRA) Toolkit Introduction Workshop was held at Energetics on June 11-12. The workshop was co-hosted by Sandia National Laboratories (Sandia) and HySafe, the International Association for Hydrogen Safety. The objective of the workshop was twofold: (1) Present a hydrogen-specific methodology and toolkit (currently under development) for conducting QRA to support the development of codes and standards and safety assessments of hydrogen-fueled vehicles and fueling stations, and (2) Obtain feedback on the needs of early-stage users (hydrogen as well as potential leveraging for Compressed Natural Gas [CNG], and Liquefied Natural Gas [LNG]) and set priorities for %E2%80%9CVersion 1%E2%80%9D of the toolkit in the context of the commercial evolution of hydrogen fuel cell electric vehicles (FCEV). The workshop consisted of an introduction and three technical sessions: Risk Informed Development and Approach; CNG/LNG Applications; and Introduction of a Hydrogen Specific QRA Toolkit.

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TYPE SAND Report YEAR 2013
DOIOSTI

Use of limited data to construct Bayesian networks for probabilistic risk assessment

Groth, Katrina G.; Swiler, Laura P.

Probabilistic Risk Assessment (PRA) is a fundamental part of safety/quality assurance for nuclear power and nuclear weapons. Traditional PRA very effectively models complex hardware system risks using binary probabilistic models. However, traditional PRA models are not flexible enough to accommodate non-binary soft-causal factors, such as digital instrumentation&control, passive components, aging, common cause failure, and human errors. Bayesian Networks offer the opportunity to incorporate these risks into the PRA framework. This report describes the results of an early career LDRD project titled %E2%80%9CUse of Limited Data to Construct Bayesian Networks for Probabilistic Risk Assessment%E2%80%9D. The goal of the work was to establish the capability to develop Bayesian Networks from sparse data, and to demonstrate this capability by producing a data-informed Bayesian Network for use in Human Reliability Analysis (HRA) as part of nuclear power plant Probabilistic Risk Assessment (PRA). This report summarizes the research goal and major products of the research.

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TYPE SAND Report YEAR 2013
DOIOSTI

Use of a SPAR-H bayesian network for predicting human error probabilities with missing observations

11th International Probabilistic Safety Assessment and Management Conference and the Annual European Safety and Reliability Conference 2012, PSAM11 ESREL 2012

Groth, Katrina G.; Swiler, Laura P.

Many of the Performance Shaping Factors (PSFs) used in Human Reliability Analysis (HRA) methods are not directly measurable or observable. Methods like SPAR-H require the analyst to assign values for all of the PSFs, regardless of the PSF observability; this introduces subjectivity into the human error probability (HEP) calculation. One method to reduce the subjectivity of HRA estimates is to formally incorporate information about the probability of the PSFs into the methodology for calculating the HEP. This can be accomplished by encoding prior information in a Bayesian Network (BN) and updating the network using available observations. We translated an existing HRA methodology, SPAR-H, into a Bayesian Network to demonstrate the usefulness of the BN framework. We focus on the ability to incorporate prior information about PSF probabilities into the HRA process. This paper discusses how we produced the model by combining information from two sources, and how the BN model can be used to estimate HEPs despite missing observations. Use of the prior information allows HRA analysts to use partial information to estimate HEPs, and to rely on the prior information (from data or cognitive literature) when they are unable to gather information about the state of a particular PSF. The SPAR-H BN model is a starting point for future research activities to create a more robust HRA BN model using data from multiple sources.

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TYPE Conference YEAR 2012
OSTIScopus
Results 51–73 of 73
Results 51–73 of 73