The safety analysis of nuclear power plant is moving towards a realistic approach in which the simulations performed using best estimate computer codes must be accompanied by an uncertainty analysis, known as the Best Estimate Plus Uncertainties approach. The most popular statistical method used in these analyses is the Wilks' method, which is based on the principle of order statistics for determining a certain coverage of the Figures-of-Merit with an appropriate degree of confidence. However, there exist other statistical techniques that could provide similar or even better results. This paper explores the performance of alternative nonparametric methods as compared to the Wilks' method of obtaining such Figure-of-Merits tolerance intervals. Three methods are investigated, i.e. Hutson and Beran-Hall methods and a bootstrap method. All the techniques have been used to perform the uncertainty analysis of a Large-Break Loss of Coolant Accident. The Figure-of-Merit of interest in this application is the maximum value reached by the Peaking Clad Temperature. In order to analyze the results obtained by the different methods, four performance metrics are proposed to measure the coverage, dispersion, conservativeness, and robustness of the tolerance intervals.
Nuclear policies have experienced an important change since Fukushima Daiichi nuclear plant accident and the safety of spent fuels has been in the spot issue among all the safety concerns. At many countries, the spent fuel of nuclear power plants is not reprocessed so it has to be stored inside the facilities, normally in pools. As nuclear power plants use best estimate codes to perform safety analysis, it is interesting to assess the capacity of such codes to simulate spent fuel pools behavior. This paper uses TRACE thermal-hydraulic code to simulate steady state and transient conditions of spent fuel pools. The steady state results are compared with plant measurements of Maine Yankee with a good agreement between the calculations and the measurements. The transient simulated is a loss of cooling together with a loss of coolant through the transfer channel. TRACE heat transfer radiation model has been activated using the parameters obtained from COBRA thermal-hydraulic code.
A substantial minority of women who experience interpersonal violence will develop posttraumatic stress disorder (PTSD). One critical challenge for preventing PTSD is predicting whose acute posttraumatic stress symptoms will worsen to a clinically significant degree. This 6-month longitudinal study adopted multilevel modeling and exploratory machine learning (ML) methods to predict PTSD onset in 58 young women, ages 18 to 30, who experienced an incident of physical and/or sexual assault in the three months prior to baseline assessment. Women completed baseline assessments of theory-driven cognitive and neurobiological predictors and interview-based measures of PTSD diagnostic status and symptom severity at 1-, 3-, and 6-month follow-ups. Higher levels of self-blame, generalized anxiety disorder severity, childhood trauma exposure, and impairment across multiple domains were associated with a pattern of high and stable posttraumatic stress symptom severity over time. Predictive performance for PTSD onset was similarly strong for a gradient boosting machine learning model including all predictors and a logistic regression model including only baseline posttraumatic stress symptom severity. The present findings provide directions for future work on PTSD prediction among interpersonal violence survivors that could enhance early risk detection and potentially inform targeted prevention programs.
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