No abstract available

No abstract available

[en] Of considerable importance in the safety analysis of nuclear power plants are methods to estimate the probability of failure-on-demand, p, of a plant component that normally is inactive and that may fail when activated or stressed. Properties of five methods for estimating from failure-on-demand data the parameters of the beta prior distribution in a compound beta-binomial probability model are examined. Simulated failure data generated from a known beta-binomial marginal distribution are used to estimate values of the beta parameters by (1) matching moments of the prior distribution to those of the data, (2) the maximum likelihood method based on the prior distribution, (3) a weighted marginal matching moments method, (4) an unweighted marginal matching moments method, and (5) the maximum likelihood method based on the marginal distribution. For small sample sizes (N = or < 10) with data typical of low failure probability components, it was found that the simple prior matching moments method is often superior (e.g. smallest bias and mean squared error) while for larger sample sizes the marginal maximum likelihood estimators appear to be best

[en] This book presents discussions of the transport description of radiation penetration, material and structural conditions in shield design, appendices and index. This book explains well the shielding against ionizing radiation, principally neutrons and photons

[en] This user's manual presents a detailed description of two FORTRAN computer codes for analyzing component failure data. The first code SAFE-R is used to analyze data giving the number of observed failures in specified component operation times, while the second code SAFE-D is to be applied to failure data giving the number of failures observed in specified numbers of component demands. The theory behind the many analyses performed by these codes is summarized in a companion report NUREG/CR-2374 'Use of Non-Conjugate Prior Distributions in Compound Failure Models.' A description of the overall program structure and detailed use of the many program commands is presented here. A sample input is given along with the resulting output

[en] Neutron depth profiling (NDP) is a nondestructive technique for determining the concentration of special isotopes within several microns of a sample's surface. Previous NDP analyses, however, have been restricted to samples with plane surfaces. Here samples with curved surfaces are considered. In particular, a method for estimating the energy spectrum of ions emitted from curved surfaces is presented. Also, a robust method for inverting the NDP ion energy spectra is presented that yields accurate concentration profiles for both under- and overdetermined NDP spectra

[en] A benchmark gamma-ray skyshine experiment is descibed in which ⁶⁰Co sources were either collimated into an upward 150-deg conical beam or shielded vertically by two different thicknesses of concrete. A NaI(Tl) spectrometer and a high pressure ion chamber were used to measure, respectively, the energy spectrum and the 4π-exposure rate of the air-reflected gamma photons up to 700 m from the source. Analyses of the data and comparison to DOT discrete ordinates calculations are presented

[en] Paper No. F78 702-3

No abstract available

[en] Several theoretical and computational techniques are presented for compound failure models in which the failure rate or failure probability for a class of components is considered to be a random variable. Both the failure-on-demand and failure-rate situation are considered. Ten different prior families are presented for describing the variation or uncertainty of the failure parameter. Methods considered for estimating values for the prior parameters from a given set of failure data are (1) matching data moments to those of the prior distribution, (2) matching data moments to those of the compound marginal distribution, and (3) the marginal maximum likelihood method. Numerical methods for computing the parameter estimators for all ten prior families are presented, as well as methods for obtaining estimates of the variances and covariance of the parameter estimators, it is shown that various confidence, probability, and tolerance intervals can be evaluated. Finally, to test the resulting failure models against the given failure data, generalized chi-squage and Kolmogorov-Smirnov goodness-of-fit tests are proposed together with a test to eliminate outliers from the failure data. Computer codes based on the results presented here have been prepared and are presented in a companion report