Passive System Reliability Analysis: A Study on the Isolation Condenser

2002 ◽  
Vol 139 (1) ◽  
pp. 3-9 ◽  
Author(s):  
Luciano Burgazzi
Keyword(s):  
Reliability Analysis ◽  
System Reliability ◽  
Passive System ◽  
System Reliability Analysis

Passive system reliability analysis using the APSRA methodology

2008 ◽  
Vol 238 (6) ◽  
pp. 1430-1440 ◽  
Author(s):  
A.K. Nayak ◽  
M.R. Gartia ◽  
A. Antony ◽  
G. Vinod ◽  
R.K. Sinha
Keyword(s):  
Reliability Analysis ◽  
System Reliability ◽  
Passive System ◽  
System Reliability Analysis

Correlation analysis for screening key parameters for passive system reliability analysis

2015 ◽  
Vol 77 ◽  
pp. 23-29 ◽  
Author(s):  
Yu Yu ◽  
Guohang Ma ◽  
Zulong Hao ◽  
Shengfei Wang ◽  
Fenglei Niu ◽  
...  
Keyword(s):  
Correlation Analysis ◽  
Reliability Analysis ◽  
System Reliability ◽  
Passive System ◽  
System Reliability Analysis

Passive System Reliability Analysis Based on Improved Support Vector Machine

2017 ◽  
Author(s):  
Hao Ding ◽  
Qi Cai ◽  
Yongfa Zhang ◽  
Lizhi Jiang
Keyword(s):  
Support Vector Machine ◽  
Reliability Analysis ◽  
System Reliability ◽  
Rapid Development ◽  
Support Vector ◽  
Implicit Function ◽  
Passive System ◽  
Passive Systems ◽  
System Reliability Analysis ◽  
Improved Support Vector Machine

The desire for improved safety and simplified designs has led to the increased consideration and rapid development of passive systems. Passive system reliability analysis and evaluation is essential, especially in innovative nuclear reactor designs. Uncertainty related with functional failure should be considered into assessment of passive system reliability based on thermal hydraulics. The low efficiency of thermal hydraulic simulation codes cannot meet the needs of practical application and require a surrogate method to obtain the instant failure probability of passive systems. In this paper, improved Support Vector Machine (SVM) method with Latin Hypercube Sampling (LHS) is adapted to set an approximation. This method, which can fit the nonlinear elements in thermal hydraulic equation, could determine the failure criterion, a surface classifying operation and failure mode, precisely. Then the calculation of an implicit function is trained and accomplished through the characteristics and features existing in samples. A comparison of polynomial form and neural network method is presented. According to the analysis of parameters effecting the probabilistic assessment, the inputs are adjusted to retrain the implicit function and obtain a better solution.

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