The frequency of unintended releases in a compressed natural gas system is an important aspect of the system quantitative risk assessment. The frequencies for possible release scenarios, along with engineering models, are utilized to quantify the risks for compressed natural gas facilities. This report documents component leakage frequencies representative of compressed natural gas components that were estimated as a function of the normalized leak size. A Bayesian statistical method was used which results in leak frequency distributions for each component which represent variation and uncertainty in the leak frequency. The analysis shows that there is high uncertainty in the estimated leak frequencies due to sparsity in compressed natural gas data. These leak frequencies may still be useful in compressed natural gas system risk assessments, as long as this high uncertainty is acknowledged and considered appropriately.
Geologic Disposal Safety Assessment Framework is a state-of-the-art simulation software toolkit for probabilistic post-closure performance assessment of systems for deep geologic disposal of nuclear waste developed by the United States Department of Energy. This paper presents a generic reference case and shows how it is being used to develop and demonstrate performance assessment methods within the Geologic Disposal Safety Assessment Framework that mitigate some of the challenges posed by high uncertainty and limited computational resources. Variance-based global sensitivity analysis is applied to assess the effects of spatial heterogeneity using graph-based summary measures for scalar and time-varying quantities of interest. Behavior of the system with respect to spatial heterogeneity is further investigated using ratios of water fluxes. This analysis shows that spatial heterogeneity is a dominant uncertainty in predictions of repository performance which can be identified in global sensitivity analysis using proxy variables derived from graph descriptions of discrete fracture networks. New quantities of interest defined using water fluxes proved useful for better understanding overall system behavior.
The Spent Fuel and Waste Science and Technology (SFWST) Campaign of the U.S. Department of Energy (DOE) Office of Nuclear Energy (NE), Office of Fuel Cycle Technology (FCT) is conducting research and development (R&D) on geologic disposal of spent nuclear fuel (SNF) and high-level nuclear waste (HLW). Two high priorities for SFWST disposal R&D are design concept development and disposal system modeling. These priorities are directly addressed in the SFWST Geologic Disposal Safety Assessment (GDSA) control account, which is charged with developing a geologic repository system modeling and analysis capability, and the associated software, GDSA Framework, for evaluating disposal system performance for nuclear waste in geologic media. GDSA Framework is supported by SFWST Campaign and its predecessor the Used Fuel Disposition (UFD) campaign.
Understanding the potential risk of stress corrosion cracking of spent nuclear fuel dry storage canisters has been identified as a knowledge gap for determining the safety of long-term interim storage of spent nuclear fuel. To address this, the DOE is funding a multi-lab DOE effort to understand the timing, occurrence, and consequences of potential canister SCC. Sandia National Laboratories has developed a probabilistic model for canister penetration by SCC. This model has been continuously updated at SNL since 2014. Model uncertainties are treated using a nested loop structure, where the outer loop accounts for uncertainties due to lack of data and the inner aleatoric loop accounts for uncertainties due to variation in nature. By separating uncertainties into these categories, it is possible to focus future work on reducing the most influential epistemic uncertainties. Several experimental studies have already been performed to improve the modeling approach through expanded process understanding and improved model parameterization. The resulting code is physics-based and intended to inform future work by identifying (1) important modeling assumptions, (2) experimental data needs, and (3) necessary model developments. In this document, several of the sub-models in the probabilistic SCC model have been exercised, and the intermediate results, as the model progresses from one sub-model to the next, are presented. Evaluating the sub-models in this manner provides a better understanding of sub-model outputs and has identified several unintended consequences of model assumptions or parameterizations, requiring updates to the modeling approach. The following updates have been made, and future updates have been identified.
Probability of loss of assured safety (PLOAS) is modeled for weak link (WL)/strong link (SL) systems in which one or more WLs or SLs could potentially degrade into a precursor condition to link failure that will be followed by an actual link failure after some amount of elapsed time. The descriptor loss of assured safety (LOAS) is used because failure of the WL system places the entire system in an inoperable configuration while failure of the SL system before failure of the WL system, although undesirable, does not necessarily result in an unintended operation of the entire system. Thus, safety is “assured” by failure of the WL system before failure of the SL system. The following topics are considered: (i) Definition of precursor occurrence time cumulative distribution functions (CDFs) for individual WLs and SLs, (ii) Formal representation, approximation and illustration of PLOAS with (a) constant delay times, (b) aleatory uncertainty in delay times, and (c) delay times defined by functions of link properties at occurrence times for link failure precursors, and (iii) Procedures for the verification of PLOAS calculations for the three indicated definitions of delayed link failure.
Swiler, Laura P.; Becker, Dirk-Alexander B.; Brooks, Dusty M.; Govaerts, Joan G.; Koskinen, Lasse K.; Plischke, Elmar P.; Röhlig, Klaus-Jürgen R.; Saveleva, Elena S.; Spiessl, Sabine M.; Stein, Emily S.; Svitelman, Valentina S.
Over the past four years, an informal working group has developed to investigate existing sensitivity analysis methods, examine new methods, and identify best practices. The focus is on the use of sensitivity analysis in case studies involving geologic disposal of spent nuclear fuel or nuclear waste. To examine ideas and have applicable test cases for comparison purposes, we have developed multiple case studies. Four of these case studies are presented in this report: the GRS clay case, the SNL shale case, the Dessel case, and the IBRAE groundwater case. We present the different sensitivity analysis methods investigated by various groups, the results obtained by different groups and different implementations, and summarize our findings.
The Spent Fuel and Waste Science and Technology (SFWST) Campaign of the U.S. Department of Energy (DOE) Office of Nuclear Energy (NE), Office of Fuel Cycle Technology (FCT) is conducting research and development (R&D) on geologic disposal of spent nuclear fuel (SNF) and high-level nuclear waste (HLW). Two high priorities for SFWST disposal R&D are design concept development and disposal system modeling. These priorities are directly addressed in the SFWST Geologic Disposal Safety Assessment (GDSA) control account, which is charged with developing a geologic repository system modeling and analysis capability, and the associated software, GDSA Framework, for evaluating disposal system performance for nuclear waste in geologic media. GDSA Framework is supported by SFWST Campaign and its predecessor the Used Fuel Disposition (UFD) campaign. This report fulfills the GDSA Uncertainty and Sensitivity Analysis Methods work package (SF-21SN01030404) level 3 milestone, Uncertainty and Sensitivity Analysis Methods and Applications in GDSA Framework (FY2021) (M3SF-21SN010304042). It presents high level objectives and strategy for development of uncertainty and sensitivity analysis tools, demonstrates uncertainty quantification (UQ) and sensitivity analysis (SA) tools in GDSA Framework in FY21, and describes additional UQ/SA tools whose future implementation would enhance the UQ/SA capability of GDSA Framework. This work was closely coordinated with the other Sandia National Laboratory GDSA work packages: the GDSA Framework Development work package (SF-21SN01030405), the GDSA Repository Systems Analysis work package (SF-21SN01030406), and the GDSA PFLOTRAN Development work package (SF-21SN01030407). This report builds on developments reported in previous GDSA Framework milestones, particularly M3SF 20SN010304032.
Swiler, Laura P.; Brooks, Dusty M.; Stein, Emily S.; Röhlig, Klaus-Jürgen R.; Plischke, Elmar P.; Becker, Dirk-Alexander B.; Spiessl, Sabine M.; Koskinen, Lasse K.; Govaerts, Joan G.; Svitelman, Valentina S.; Saveleva., Elena Saveleva.
Stress corrosion cracking (SCC) is an important failure degradation mechanism for storage of spent nuclear fuel. Since 2014, Sandia National Laboratories has been developing a probabilistic methodology for predicting SCC. The model is intended to provide qualitative assessment of data needs, model sensitivities, and future model development. In fiscal year 2021, improvement of the SCC model focused on the salt deposition, maximum pit size, and crack growth rate models.
The use of evidence theory and associated cumulative plausibility functions (CPFs), cumulative belief functions (CBFs), cumulative distribution functions (CDFs), complementary cumulative plausibility functions (CCPFs), complementary cumulative belief functions (CCBFs), and complementary cumulative distribution functions (CCDFs) in the analysis of time and temperature margins associated with loss of assured safety (LOAS) for one weak link (WL)/two strong link (SL) systems is illustrated. Article content includes cumulative and complementary cumulative belief, plausibility, and probability for (i) SL/ WL failure time margins defined by (time at which SL failure potentially causes LOAS) - (time at which WL failure potentially prevents LOAS), (ii) SL/WL failure temperature margins defined by (the temperature at which SL failure potentially causes LOAS) - (the temperature at which WL failure potentially prevents LOAS), and (iii) SL/SL failure temperature margins defined by (the temperature at which SL failure potentially causes LOAS) - (the temperature of SL whose failure potentially causes LOAS at the time at which WL failure potentially prevents LOAS).
The use of evidence theory and associated cumulative plausibility functions (CPFs), cumulative belief functions (CBFs), cumulative distribution functions (CDFs), complementary cumulative plausibility functions (CCPFs), complementary cumulative belief functions (CCBFs), and complementary cumulative distribution functions (CCDFs) in the analysis of loss of assured safety (LOAS) for weak link (WL)/strong link (SL) systems is introduced and illustrated. Article content includes cumulative and complementary cumulative belief, plausibility, and probability for (i) time at which LOAS occurs for a one WL/two SL system, (ii) time at which a two-link system fails, (iii) temperature at which a two-link system fails, and (iv) temperature at which LOAS occurs for a one WL/two SL system. The presented results can be generalized to systems with more than one WL and two SLs.
The use of evidence theory in the analysis of loss of assured safety (LOAS) for weak link (WL)/strong link (SL) systems is introduced and illustrated. Article content includes (i) an introduction to evidence theory, (ii) definition of two WLs and two SLs used for illustration, (iii) belief and plausibility for the occurrence of LOAS for a one WL/two SL system, and (iv) a sampling-based procedure for the verification of belief and plausibility calculations. The presented results can be generalized to systems with more than one WL and two SLs.
This analysis provides estimates on the leak frequencies of nine components found in liquefied natural gas (LNG) facilities. Data was taken from a variety of sources, with 25 different data sets included in the analysis. A hierarchical Bayesian model was used that assumes that the log leak frequency follows a normal distribution and the logarithm of the mean of this normal distribution is a linear function of the logarithm of the fractional leak area. This type of model uses uninformed prior distributions that are updated with applicable data. Separate models are fit for each component listed. Five order-of-magnitude fractional leak areas are considered, based on the flow area of the component. Three types of supporting analyses were performed: sensitivity of the model to the data set used, sensitivity of the leak frequency estimates to differences in the model structure or prior distributions, and sufficiency of sample sized used for convergence. Recommended leak frequency distributions for all component types and leak sizes are given. These leak frequency predictions can be used for quantitative risk assessments in the future.
The need to understand the risks and implications of traffic incidents involving hydrogen fuel cell electric vehicles in tunnels is increasing in importance with higher numbers of these vehicles being deployed. A risk analysis was performed to capture potential scenarios that could occur in the event of a crash and provide a quantitative calculation for the probability of each scenario occurring, with a qualitative categorization of possible consequences. The risk analysis was structured using an event sequence diagram with probability distributions on each event in the tree and random sampling was used to estimate resulting probability distributions for each end-state scenario. The most likely consequence of a crash is no additional hazard from the hydrogen fuel (98.1–99.9% probability) beyond the existing hazards in a vehicle crash, although some factors need additional data and study to validate. These scenarios include minor crashes with no release or ignition of hydrogen. When the hydrogen does ignite, it is most likely a jet flame from the pressure relief device release due to a hydrocarbon fire (0.03–1.8% probability). This work represents a detailed assessment of the state-of-knowledge of the likelihood associated with various vehicle crash scenarios. This is used in an event sequence framework with uncertainty propagation to estimate uncertainty around the probability of each scenario occurring.
This paper is the third paper in a special session on the State-of-the-Art Reactor Consequence Analyses (SOARCA) Uncertainty Analyses (UAs), and summarizes offsite consequence insights from the three SOARCA UAs. The U.S. Nuclear Regulatory Commission (NRC) with Sandia National Laboratories has completed three UAs for particular station blackout scenarios as part of the SOARCA research project: for a boiling-water reactor with a Mark I containment in Pennsylvania State (Peach Bottom), for a pressurized-water reactor (PWR) with an ice condenser containment in Tennessee State (Sequoyah), and for a PWR with subatmospheric large dry containment in Virginia State (Surry). The Sequoyah and Surry SOARCA UAs focused on an unmitigated short-term station blackout (SBO) scenario involving an immediate loss of offsite and onsite AC power. In the Surry UA, induced steam generator tube rupture was also modeled. The Sequoyah study focused on issues unique to the ice condenser containment and the potential for early containment failure due to hydrogen combustion. The Peach Bottom UA focused on an unmitigated long-term SBO scenario, where battery power is initially available. The MELCOR Accident Consequence Code System (MACCS) suite of codes was used for offsite radiological consequence modeling. This paper presents the offsite consequence results, individual latent cancer fatality risk and the individual early fatality risk, for the three SOARCA UAs and summarizes some of the insights and features of the analyses.
Representations for margins associated with loss of assured safety (LOAS) for weak link (WL)/strong link (SL) systems involving multiple time-dependent failure modes are developed. The following topics are described: (i) defining properties for WLs and SLs, (ii) background on cumulative distribution functions (CDFs) for link failure time, link property value at link failure, and time at which LOAS occurs, (iii) CDFs for failure time margins defined by (time at which SL system fails) – (time at which WL system fails), (iv) CDFs for SL system property values at LOAS, (v) CDFs for WL/SL property value margins defined by (property value at which SL system fails) – (property value at which WL system fails), and (vi) CDFs for SL property value margins defined by (property value of failing SL at time of SL system failure) – (property value of this SL at time of WL system failure). Included in this presentation is a demonstration of a verification strategy based on defining and approximating the indicated margin results with (i) procedures based on formal integral representations and associated quadrature approximations and (ii) procedures based on algorithms for sampling-based approximations.
Probability of loss of assured safety (PLOAS) is modeled for weak link (WL)/strong link (SL) systems in which one or more WLs or SLs could potentially degrade into a precursor condition to link failure that will be followed by an actual failure after some amount of elapsed time. The following topics are considered: (i) Definition of precursor occurrence time cumulative distribution functions (CDFs) for individual WLs and SLs, (ii) Formal representation of PLOAS with constant delay times, (iii) Approximation and illustration of PLOAS with constant delay times, (iv) Formal representation of PLOAS with aleatory uncertainty in delay times, (v) Approximation and illustration of PLOAS with aleatory uncertainty in delay times, (vi) Formal representation of PLOAS with delay times defined by functions of link properties at occurrence times for failure precursors, (vii) Approximation and illustration of PLOAS with delay times defined by functions of link properties at occurrence times for failure precursors, and (viii) Procedures for the verification of PLOAS calculations for the three indicated definitions of delayed link failure.
Representations are developed and illustrated for the distribution of link property values at the time of link failure in the presence of aleatory uncertainty in link properties. The following topics are considered: (i) defining properties for weak links and strong links, (ii) cumulative distribution functions (CDFs) for link failure time, (iii) integral-based derivation of CDFs for link property at time of link failure, (iv) sampling-based approximation of CDFs for link property at time of link failure, (v) verification of integral-based and sampling-based determinations of CDFs for link property at time of link failure, (vi) distributions of link properties conditional on time of link failure, and (vii) equivalence of two different integral-based derivations of CDFs for link property at time of link failure.
The U.S. Nuclear Regulatory Commission initiated the state-of-the-art reactor consequence analyses (SOARCA) project to develop realistic estimates of the offsite radiological health consequences for potential severe reactor accidents. The SOARCA analysis of an ice condenser containment plant was performed because its relatively low design pressure and reliance on igniters makes it potentially susceptible to early containment failure from hydrogen combustion during a severe accident. The focus was on station blackout accident scenarios where all alternating current power is lost. Accident progression calculations used the MELCOR computer code and offsite consequence analyses were performed with MACCS. The analysis included more than 500 MELCOR and MACCS simulations to account for uncertainty in important accident progression and offsite consequence input parameters. Consequences from severe nuclear power plant accidents modeled in this and previous SOARCA analyses are smaller than calculated in earlier studies. The delayed releases calculated provide more time for emergency response actions. The results show that early containment failure is very unlikely, even without successful use of igniters. However, these results are dependent on the distributions assigned to safety valve failure-to-close parameters, and considerable uncertainty remains on the true distributions for these parameters due to very limited test data. Even for scenarios resulting in early containment failure, the calculated individual latent fatal cancer risks are very small. Early and latent-cancer fatality risks are one focus of this paper. Regression results showing the most influential parameters are also discussed.
This report presents the results of instrumentation cable tests sponsored by the US Nuclear Regulatory Commission (NRC) Office of Nuclear Regulatory Research and performed at Sandia National Laboratories (SNL). The goal of the tests was to assess thermal and electrical response behavior under fire-exposure conditions for instrumentation cables and circuits. The test objective was to assess how severe radiant heating conditions surrounding an instrumentation cable affect current or voltage signals in an instrumentation circuit. A total of thirty-nine small-scale tests were conducted. Ten different instrumentation cables were tested, ranging from one conductor to eight-twisted pairs. Because the focus of the tests was thermoset (TS) cables, only two of the ten cables had thermoplastic (TP) insulation and jacket material and the remaining eight cables were one of three different TS insulation and jacket material. Two instrumentation cables from previous cable fire testing were included, one TS and one TP. Three test circuits were used to simulate instrumentation circuits present in nuclear power plants: a 4–20 mA current loop, a 10–50 mA current loop and a 1–5 VDC voltage loop. A regression analysis was conducted to determine key variables affecting signal leakage time.
This report describes the methods, results, and conclusions of the analysis of 11 scenarios defined to exercise various options available in the xLPR (Extremely Low Probability of Rupture) Version 2 .0 code. The scope of the scenario analysis is three - fold: (i) exercise the various options and components comprising xLPR v2.0 and defining each scenario; (ii) develop and exercise methods for analyzing and interpreting xLPR v2.0 outputs ; and (iii) exercise the various sampling options available in xLPR v2.0. The simulation workflow template developed during the course of this effort helps to form a basis for the application of the xLPR code to problems with similar inputs and probabilistic requirements and address in a systematic manner the three points covered by the scope.
In pressurized water reactors, the prevention, detection, and repair of cracks within dissimilar metal welds is essential to ensure proper plant functionality and safety. Weld residual stresses, which are difficult to model and cannot be directly measured, contribute to the formation and growth of cracks due to primary water stress corrosion cracking. Additionally, the uncertainty in weld residual stress measurements and modeling predictions is not well understood, further complicating the prediction of crack evolution. The purpose of this document is to develop methodology to quantify the uncertainty associated with weld residual stress that can be applied to modeling predictions and experimental measurements. Ultimately, the results can be used to assess the current state of uncertainty and to build confidence in both modeling and experimental procedures. The methodology consists of statistically modeling the variation in the weld residual stress profiles using functional data analysis techniques. Uncertainty is quantified using statistical bounds (e.g. confidence and tolerance bounds) constructed with a semi-parametric bootstrap procedure. Such bounds describe the range in which quantities of interest, such as means, are expected to lie as evidenced by the data. The methodology is extended to provide direct comparisons between experimental measurements and modeling predictions by constructing statistical confidence bounds for the average difference between the two quantities. The statistical bounds on the average difference can be used to assess the level of agreement between measurements and predictions. The methodology is applied to experimental measurements of residual stress obtained using two strain relief measurement methods and predictions from seven finite element models developed by different organizations during a round robin study.
Sandia National Laboratories (SNL) has conducted an uncertainty analysi s (UA) on the Fukushima Daiichi unit (1F1) accident progression wit h the MELCOR code. Volume I of the 1F1 UA discusses the physical modeling details and time history results of the UA. Volume II of the 1F1 UA discusses the statistical viewpoint. The model used was developed for a previous accident reconstruction investigation jointly sponsored by the US Department of Energy (DOE) and Nuclear Regulatory Commission (NRC). The goal of this work was to perform a focused evaluation of uncertainty in core damage progression behavior and its effect on key figures - of - merit (e.g., hydrogen production, fraction of intact fuel, vessel lower head failure) and in doing so assess the applicability of traditional sensitivity analysis techniques .