Environmental contours describing extreme sea states are generated as the input for numerical or physical model simulations as a part of the standard current practice for designing marine structures to survive extreme sea states. These environmental contours are characterized by combinations of significant wave height (Hs) and either energy period (Te) or peak period (Tp) values calculated for a given recurrence interval using a set of data based on hindcast simulations or buoy observations over a sufficient period of record. The use of the inverse first-order reliability method (I-FORM) is a standard design practice for generating environmental contours. This paper develops enhanced methodologies for data analysis prior to the application of the I-FORM, including the use of principal component analysis (PCA) to create an uncorrelated representation of the variables under consideration as well as new distribution and parameter fitting techniques. These modifications better represent the measured data and, therefore, should contribute to the development of more realistic representations of environmental contours of extreme sea states for determining design loads for marine structures.
Weak link (WL)/strong link (SL) systems are important parts of the overall operational design of high - consequence systems. In such designs, the SL system is very robust and is intended to permit operation of the entire system under, and only under, intended conditions. In contrast, the WL system is intended to fail in a predictable and irreversible manner under accident conditions and render the entire system inoperable before an accidental operation of the SL system. The likelihood that the WL system will fail to d eactivate the entire system before the SL system fails (i.e., degrades into a configuration that could allow an accidental operation of the entire system) is referred to as probability of loss of assured safety (PLOAS). This report describes the Fortran 90 program CPLOAS_2 that implements the following representations for PLOAS for situations in which both link physical properties and link failure properties are time - dependent: (i) failure of all SLs before failure of any WL, (ii) failure of any SL before f ailure of any WL, (iii) failure of all SLs before failure of all WLs, and (iv) failure of any SL before failure of all WLs. The effects of aleatory uncertainty and epistemic uncertainty in the definition and numerical evaluation of PLOAS can be included in the calculations performed by CPLOAS_2. Keywords: Aleatory uncertainty, CPLOAS_2, Epistemic uncertainty, Probability of loss of assured safety, Strong link, Uncertainty analysis, Weak link
For long-term storage, spent nuclear fuel (SNF) is placed in dry storage cask systems, commonly consisting of welded stainless steel containers enclosed in ventilated cement or steel overpacks. At near-marine sites, failure by chloride-induced stress corrosion cracking (SCC) due to deliquescence of deposited salt aerosols is a major concern. This paper presents a preliminary probabilistic performance assessment model to assess canister penetration by SCC. The model first determines whether conditions for salt deliquescence are present at any given location on the canister surface, using an abstracted waste package thermal model and site-specific weather data (ambient temperature and absolute humidity). As the canister cools and aqueous conditions become possible, corrosion is assumed to initiate and is modeled as pitting (initiation and growth). With increasing penetration, pits convert to SCC and a crack growth model is implemented. The SCC growth model includes rate dependencies on temperature and crack tip stress intensity factor. The amount of penetration represents the summed effect of corrosion during time steps when aqueous conditions are predicted to occur. Model results and sensitivity analyses provide information on the impact of model assumptions and parameter values on predicted storage canister performance, and provide guidance for further research to reduce uncertainties.
Environmental contours describing extreme sea states are generated as the input for numerical or physical model simulation s as a part of the stand ard current practice for designing marine structure s to survive extreme sea states. Such environmental contours are characterized by combinations of significant wave height ( ) and energy period ( ) values calculated for a given recurrence interval using a set of data based on hindcast simulations or buoy observations over a sufficient period of record. The use of the inverse first - order reliability method (IFORM) i s standard design practice for generating environmental contours. In this paper, the traditional appli cation of the IFORM to generating environmental contours representing extreme sea states is described in detail and its merits and drawbacks are assessed. The application of additional methods for analyzing sea state data including the use of principal component analysis (PCA) to create an uncorrelated representation of the data under consideration is proposed. A reexamination of the components of the IFORM application to the problem at hand including the use of new distribution fitting techniques are shown to contribute to the development of more accurate a nd reasonable representations of extreme sea states for use in survivability analysis for marine struc tures. Keywords: In verse FORM, Principal Component Analysis , Environmental Contours, Extreme Sea State Characteri zation, Wave Energy Converters
This paper describes the knowledge advancements from the uncertainty analysis for the State-of- the-Art Reactor Consequence Analyses (SOARCA) unmitigated long-term station blackout accident scenario at the Peach Bottom Atomic Power Station. This work assessed key MELCOR and MELCOR Accident Consequence Code System, Version 2 (MACCS2) modeling uncertainties in an integrated fashion to quantify the relative importance of each uncertain input on potential accident progression, radiological releases, and off-site consequences. This quantitative uncertainty analysis provides measures of the effects on consequences, of each of the selected uncertain parameters both individually and in interaction with other parameters. The results measure the model response (e.g., variance in the output) to uncertainty in the selected input. Investigation into the important uncertain parameters in turn yields insights into important phenomena for accident progression and off-site consequences. This uncertainty analysis confirmed the known importance of some parameters, such as failure rate of the Safety Relief Valve in accident progression modeling and the dry deposition velocity in off-site consequence modeling. The analysis also revealed some new insights, such as dependent effect of cesium chemical form for different accident progressions. (auth)
This paper describes the convergence of MELCOR Accident Consequence Code System, Version 2 (MACCS2) probabilistic results of offsite consequences for the uncertainty analysis of the State-of-the-Art Reactor Consequence Analyses (SOARCA) unmitigated long-term station blackout scenario at the Peach Bottom Atomic Power Station. The consequence metrics evaluated are individual latent-cancer fatality (LCF) risk and individual early fatality risk. Consequence results are presented as conditional risk (i.e., assuming the accident occurs, risk per event) to individuals of the public as a result of the accident. In order to verify convergence for this uncertainty analysis, as recommended by the Nuclear Regulatory Commission’s Advisory Committee on Reactor Safeguards, a ‘high’ source term from the original population of Monte Carlo runs has been selected to be used for: (1) a study of the distribution of consequence results stemming solely from epistemic uncertainty in the MACCS2 parameters (i.e., separating the effect from the source term uncertainty), and (2) a comparison between Simple Random Sampling (SRS) and Latin Hypercube Sampling (LHS) in order to validate the original results obtained with LHS. Three replicates (each using a different random seed) of size 1,000 each using LHS and another set of three replicates of size 1,000 using SRS are analyzed. The results show that the LCF risk results are well converged with either LHS or SRS sampling. The early fatality risk results are less well converged at radial distances beyond 2 miles, and this is expected due to the sparse data (predominance of “zero” results).
A proposed method is considered to classify the regions in the close neighborhood of selected measurements according to the ratio of two radionuclides measured from either a radioactive plume or a deposited radionuclide mixture. The subsequent associated locations are then considered in the area of interest with a representative ratio class. This method allows for a more comprehensive and meaningful understanding of the data sampled following a radiological incident.