Building a hydrogen infrastructure system is critical to supporting the development of alternate- fuel vehicles. This report provides a methodology for implementing a performance-based design of an outdoor hydrogen refueling station that does not meet specific prescriptive requirements in NFPA 2, The Hydrogen Technologies Code . Performance-based designs are a code-compliant alternative to meeting prescriptive requirements. Compliance is demonstrated by comparing a prescriptive-based fueling station design with a performance-based design approach using Quantitative Risk Assessment (QRA) methods and hydrogen risk assessment tools. This template utilizes the Sandia-developed QRA tool, Hydrogen Risk Analysis Models (HyRAM), which combines reduced-order deterministic models that characterize hydrogen release and flame behavior with probabilistic risk models to quantify risk values. Each project is unique and this template is not intended to account for site-specific characteristics. Instead, example content and a methodology are provided for a representative hydrogen refueling site which can be built upon for new hydrogen applications.
Accident management is an important component to maintaining risk at acceptable levels for all complex systems, such as nuclear power plants. With the introduction of self - correcting, or inherently safe, reactor designs the focus has shifted from management by operators to allowing the syste m's design to manage the accident. While inherently and passively safe designs are laudable, extreme boundary conditions can interfere with the design attributes which facilitate inherent safety , thus resulting in unanticipated and undesirable end states. This report examines an inherently safe and small sodium fast reactor experiencing a beyond design basis seismic event with the intend of exploring two issues : (1) can human intervention either improve or worsen the potential end states and (2) can a Bayes ian Network be constructed to infer the state of the reactor to inform (1). ACKNOWLEDGEMENTS The author s would like to acknowledge the U.S. Department of E nergy's Office of Nuclear Energy for funding this research through Work Package SR - 14SN100303 under the Advanced Reactor Concepts program. The authors also acknowledge the PRA teams at A rgonne N ational L aborator y , O ak R idge N ational L aborator y , and I daho N ational L aborator y for their continue d contributions to the advanced reactor PRA mission area.
A series of test cases designed to verify the correct implementation of several features of the CPLOAS_2 program are documented. CPLOAS_2 is used to calculate the probability of loss of assured safety (PLOAS) for a weak link (WL)/strong link (SL) system. CPLOAS_2 takes physical properties (e.g., temperature, pressure, etc.) of a WL/SL system and uses these properties and definitions of link failure properties in probabilistic calculations to determine PLOAS. The features being tested include (i) six aleatory distribution forms, (ii) five numerical procedures for the determination of PLOAS (i.e., one quadrature procedure, two simple random sampling procedures, and two importance sampling procedures), and (iii) time and environmental margin calculations. All tests were performed with CPLOAS_2 version 2.10.
In the past several years, several international agencies have begun to collect data on human performance in nuclear power plant simulators [1]. This data provides a valuable opportunity to improve human reliability analysis (HRA), but there improvements will not be realized without implementation of Bayesian methods. Bayesian methods are widely used in to incorporate sparse data into models in many parts of probabilistic risk assessment (PRA), but Bayesian methods have not been adopted by the HRA community. In this article, we provide a Bayesian methodology to formally use simulator data to refine the human error probabilities (HEPs) assigned by existing HRA methods. We demonstrate the methodology with a case study, wherein we use simulator data from the Halden Reactor Project to update the probability assignments from the SPAR-H method. The case study demonstrates the ability to use performance data, even sparse data, to improve existing HRA methods. Furthermore, this paper also serves as a demonstration of the value of Bayesian methods to improve the technical basis of HRA.
There has been increasing interest in using Quantitative Risk Assessment [QRA] to help improve the safety of hydrogen infrastructure and applications. Hydrogen infrastructure for transportation (e.g. fueling fuel cell vehicles) or stationary (e.g. back-up power) applications is a relatively new area for application of QRA vs. traditional industrial production and use, and as a result there are few tools designed to enable QRA for this emerging sector. There are few existing QRA tools containing models that have been developed and validated for use in small-scale hydrogen applications. However, in the past several years, there has been significant progress in developing and validating deterministic physical and engineering models for hydrogen dispersion, ignition, and flame behavior. In parallel, there has been progress in developing defensible probabilistic models for the occurrence of events such as hydrogen release and ignition. While models and data are available, using this information is difficult due to a lack of readily available tools for integrating deterministic and probabilistic components into a single analysis framework. This paper discusses the first steps in building an integrated toolkit for performing QRA on hydrogen transportation technologies and suggests directions for extending the toolkit.