Probabilistic Leak Before Break Using the R6 Methodology

Author(s):  
Peter Gill

Abstract The integrity of a component in a safety critical industry is determined by carrying out Engineering Critical Assessments (ECA). These are designed to provide a conservative estimate of the life of a component based on conservative inputs/methodology. It is becoming increasingly apparent that for many applications these methods are overly conservative. The only physical way to really assess the reliability of a component is by producing many thousands, if not millions of a specific component and calculating a failure probability based on testing/OPEX. This is simply not feasible for the components in, for example, a nuclear reactor, and probabilistic techniques are becoming increasingly important as a means to understand the reliability of a component. This information can then be used to assess risk and inform inspection programmes. Typically a probabilistic method relies on assigning distributions to various input parameters and evaluating a probability integral, usually by Monte-Carlo analysis. A previous PVP paper developed Monte Carlo methods using the R6 fracture mechanics procedure. Although providing good insight into the likelihood of failure, these analyses were simplified and not readily applied to realistic plant situations. Further development would enable much more of the technology contained within R6 to be applied within probabilistic software. The following new features of the software are presented in this paper: • the latest K and limit load solutions from R6 for through wall circumferential defects • Simplified V factor approach to account for secondary stresses • two phase flow (water) based on the latest SQUIRT methodology • global bending, through wall bending, weld residual stress This enables a full probabilistic leak detection calculation for circumferential through wall cracks in pipes. Examples of probabilistic Leak-before-Break calculations for PWR pipework are presented in the paper.

Author(s):  
Xuesong Yan ◽  
Xunchao Zhang ◽  
Yaling Zhang ◽  
Yangyang Yang ◽  
Lei Yang ◽  
...  

Coolant is an important part of the nuclear reactor, which can convert the fission energy into heat. At the same time, it can cool the core, and control the various components of the reactor in the allowable temperature. In this paper, gas-liquid two-phase flow is proposed as the coolant in the nuclear reactor. Based on the Monte Carlo method of particle transport program GMT program and LITAC program, the neutron performance, thermal hydraulic of nuclear reactor were calculated and analyzed. The operation of the new reactor will not be refuel for decades. The gas-liquid two-phase flow coolant can be used as one of the existing alternative coolants.


2006 ◽  
Vol 321-323 ◽  
pp. 1543-1547 ◽  
Author(s):  
Ouk Sub Lee ◽  
Dong Hyeok Kim

The reliability estimation of pipelines is performed with help of the probabilistic method which considers the uncertainties included in the load and resistance parameters. The FORM (first order reliability method) is carried out to calculate the failure probability of corroded pipeline for the classified six distribution types of random variables. And the reliability of pipelines is assessed by using the failure probability. Furthermore, the MCS (Monte Carlo Simulation) is used to verify the results of the FORM. It is noted that the failure probabilities of the linear and the power models for surface corrosion are similar and those of the two-phase model for surface corrosion are somewhat different for six cases of two failure pressure models such as ASME B31G and MB31G models. And it is also found that the FORM and the MCS give similar results for varying exposure time.


1998 ◽  
Vol 37 (03) ◽  
pp. 235-238 ◽  
Author(s):  
M. El-Taha ◽  
D. E. Clark

AbstractA Logistic-Normal random variable (Y) is obtained from a Normal random variable (X) by the relation Y = (ex)/(1 + ex). In Monte-Carlo analysis of decision trees, Logistic-Normal random variates may be used to model the branching probabilities. In some cases, the probabilities to be modeled may not be independent, and a method for generating correlated Logistic-Normal random variates would be useful. A technique for generating correlated Normal random variates has been previously described. Using Taylor Series approximations and the algebraic definitions of variance and covariance, we describe methods for estimating the means, variances, and covariances of Normal random variates which, after translation using the above formula, will result in Logistic-Normal random variates having approximately the desired means, variances, and covariances. Multiple simulations of the method using the Mathematica computer algebra system show satisfactory agreement with the theoretical results.


1996 ◽  
Author(s):  
Iain D. Boyd ◽  
Xiaoming Liu ◽  
Jitendra Balakrishnan

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