scholarly journals Effect of Molten Corium Behavior Uncertainty on the Severe Accident Progress

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Wonjun Choi ◽  
Taeseok Kim ◽  
Joongoo Jeon ◽  
Nam Kyung Kim ◽  
Sung Joong Kim
Keyword(s):  
Safety Assessment ◽  
Sampling Method ◽  
Latin Hypercube Sampling ◽  
Nuclear Industry ◽  
Severe Accident ◽  
Data Sets ◽  
Human Reliability Analysis ◽  
Rank Regression ◽  
Drainage Rate ◽  
Uncertainty Parameters

Uncertainty of a severe accident code output needs to be handled reliably considering its use in safety regulation of nuclear industry. In particular, severe accident codes are utilized for probabilistic safety assessment (PSA), where the uncertainty of severe accident progress should be considered carefully due to its influence on human reliability analysis. Therefore, in this study, the uncertainty analysis of severe accident progress was performed using MELCOR code, and a total of 200 data sets of in-vessel uncertainty parameters were generated by Latin hypercube sampling method. The rank regression analysis was also performed to investigate the effect of uncertainty parameters on the severe accident progress. Sensitivity coefficients (SCs) in MELCOR such as molten clad drainage rate and zircaloy melt breakout temperature showed significant influence on relocation time and dryout time of lower plenum. However, the influence of uncertainty parameter diminished as the accident progressed.

A Probabilistic Safety Assessment Method Based on Latin Hypercube Sampling Method

2015 ◽  
Vol 757 ◽  
pp. 159-163
Author(s):  
Ying Juan Yue ◽  
Fei Chen ◽  
Hong Li ◽  
Hai Xia Du ◽  
Xiao Jun Du
Keyword(s):  
Change Process ◽  
Safety Assessment ◽  
Sampling Method ◽  
Latin Hypercube Sampling ◽  
Assessment Method ◽  
Probabilistic Assessment ◽  
Latin Hypercube ◽  
Probabilistic Safety Assessment ◽  
Failure Assessment ◽  
Probabilistic Safety

Based on the shortcomings of traditional probabilistic assessment methods, an improved probabilistic safety assessment method was proposed, which used Latin hypercube sampling, considered the change process about fatigue crack propagation, as well as the effect of random variables on the failure assessment curve. The paper also analyzed the specific example with this method. The results showed that this method was simpler and more effective, which had some value of applications in engineering.

scholarly journals Stochastic Risk and Uncertainty Analysis for Shale Gas Extraction in the Karoo Basin of South Africa

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Abdon Atangana ◽  
Gerrit van Tonder
Keyword(s):  
South Africa ◽  
Mass Transport ◽  
Sampling Method ◽  
Latin Hypercube Sampling ◽  
Transport Equations ◽  
Upward Migration ◽  
Karoo Basin ◽  
Fracking Fluid ◽  
Stochastic Risk ◽  
Flow And Mass Transport

We made use of groundwater flow and mass transport equations to investigate the crucial potential risk of water pollution from hydraulic fracturing especially in the case of the Karoo system in South Africa. This paper shows that the upward migration of fluids will depend on the apertures of the cement cracks and fractures in the rock formation. The greater the apertures, the quicker the movement of the fluid. We presented a novel sampling method, which is the combination of the Monte Carlo and the Latin hypercube sampling. The method was used for uncertainties analysis of the apertures in the groundwater and mass transport equations. The study reveals that, in the case of the Karoo, fracking will only be successful if and only if the upward methane and fracking fluid migration can be controlled, for example, by plugging the entire fracked reservoir with cement.

The Human Reliability Analysis in Fire PSA Using Spar-H Method

2013 ◽  
Author(s):  
Yao Wang
Keyword(s):  
Reliability Analysis ◽  
Nuclear Power ◽  
Significant Contribution ◽  
Fire Risk ◽  
Nuclear Industry ◽  
Total Risk ◽  
Human Reliability ◽  
Human Reliability Analysis ◽  
Study Results ◽  
In Fire

According to existing research results, fire risk makes a significant contribution to the total risk of a nuclear power plant (NPP). So fire probabilistic safety analysis (PSA) for NPPs is becoming more and more important in recent years. How to perform human reliability analysis (HRA) which is an essential part of PSA is therefore being paid more and more attention in fire PSA. This paper describes the characteristics and special considerations of HRA in fire PSA, and demonstrates in fire PSA how to use SPAR-H method which is so-called an advanced second-generation HRA method and is being widely used in PSA for Chinese NPPs. The study results can be a reference for other HRA analysts to use SPAR-H method in fire PSA models or other PSA models in Chinese NPPs or the world-wide nuclear industry.

Optimization of the OOP Children Restraint System

2012 ◽  
Vol 155-156 ◽  
pp. 386-390
Author(s):  
Zhong Hao Bai ◽  
Jing Fei ◽  
Wei Jie Ma
Keyword(s):  
Genetic Algorithm ◽  
Sampling Method ◽  
Latin Hypercube Sampling ◽  
Optimization Method ◽  
Approximate Model ◽  
Parameters Optimization ◽  
Fe Model ◽  
Restraint System ◽  
Effective Protection ◽  
Multi Body

Based on the study of SAE J1980-2008 and FMVSS 208, MADYMO7.1 is used to establish a Multi-body and FE model for two OOP children, and the statistic test is implemented to verify the accuracy of the model. The airbag parameters impacting OOP children greatly and their ranges are selected to determine the objective function. With the Latin Hypercube Sampling method, the Kring approximate model is constructed, and multi-island genetic algorithm is used in subsequently parameters optimization. The results show that the proposed optimization method can provide effective protection for 6-year-old OOP children.

Uncertainty studies on hydrogen source term with MAAP5 code

Kerntechnik ◽  
2021 ◽  
Vol 86 (2) ◽  
pp. 152-163
Author(s):  
T.-C. Wang ◽  
M. Lee
Keyword(s):  
Hydrogen Production ◽  
Nuclear Power ◽  
Hydrogen Generation ◽  
Pearson Correlation ◽  
Latin Hypercube Sampling ◽  
Severe Accident ◽  
Model Parameters ◽  
Power Company ◽  
Sensitivity Studies ◽  
The Impact

Abstract In the present study, a methodology is developed to quantify the uncertainties of special model parameters of the integral severe accident analysis code MAAP5. Here, the in-vessel hydrogen production during a core melt accident for Lungmen Nuclear Power Station of Taiwan Power Company, an advanced boiling water reactor, is analyzed. Sensitivity studies are performed to identify those parameters with an impact on the output parameter. For this, multiple calculations of MAAP5 are performed with input combinations generated from Latin Hypercube Sampling (LHS). The results are analyzed to determine the 95th percentile with 95% confidence level value of the amount of in-vessel hydrogen production. The calculations show that the default model options for IOXIDE and FGBYPA are recommended. The Pearson Correlation Coefficient (PCC) was used to determine the impact of model parameters on the target output parameters and showed that the three parameters TCLMAX, FCO, FOXBJ are highly influencing the in-vessel hydrogen generation. Suggestions of values of these three parameters are given.

SAMS: Stochastic Analysis With Minimal Sampling—A Fast Algorithm for Analysis and Design Under Uncertainty

2004 ◽  
Vol 127 (4) ◽  
pp. 558-571 ◽  
Author(s):  
A. Mawardi ◽  
R. Pitchumani
Keyword(s):  
System Performance ◽  
Stochastic Analysis ◽  
Sampling Method ◽  
Latin Hypercube Sampling ◽  
Analysis And Design ◽  
Process Outcomes ◽  
Input Parameters ◽  
Computationally Intensive ◽  
Uncertain Input ◽  
Reliability And Robustness

Design of processes and devices under uncertainty calls for stochastic analysis of the effects of uncertain input parameters on the system performance and process outcomes. The stochastic analysis is often carried out based on sampling from the uncertain input parameters space, and using a physical model of the system to generate distributions of the outcomes. In many engineering applications, a large number of samples—on the order of thousands or more—is needed for an accurate convergence of the output distributions, which renders a stochastic analysis computationally intensive. Toward addressing the computational challenge, this article presents a methodology of S̱tochastic A̱nalysis with M̱inimal S̱ampling (SAMS). The SAMS approach is based on approximating an output distribution by an analytical function, whose parameters are estimated using a few samples, constituting an orthogonal Taguchi array, from the input distributions. The analytical output distributions are, in turn, used to extract the reliability and robustness measures of the system. The methodology is applied to stochastic analysis of a composite materials manufacturing process under uncertainty, and the results are shown to compare closely to those from a Latin hypercube sampling method. The SAMS technique is also demonstrated to yield computational savings of up to 90% relative to the sampling-based method.

scholarly journals Analysis of humanoid robotics for nuclear disaster management incorporated with biomechanics

2019 ◽  
Vol 34 (3) ◽  
pp. 291-298
Author(s):  
Kyung Jang ◽  
Tae Woo
Keyword(s):  
Reactor Core ◽  
Nuclear Industry ◽  
Severe Accident ◽  
Physical Aspect ◽  
Multidisciplinary Research ◽  
Nuclear Disaster ◽  
Collapsed Building ◽  
Very High ◽  
Mechanical Studies

The humanoid is investigated for the mechanical and physical aspect in the nuclear disaster, especially for a severe accident, which includes the core melting. There are some mechanical studies of the leg and hand of the humanoid in which the human mimicking features are described. The management of the task is accomplished by the three regional preparations. The robot is made of the radiation-resistance substance. Therefore, it could work on the normal task of a human for the removal of the broken debris in a collapsed building. However, there is a limitation for the use in the reactor core building due to very high temperature of the nuclear fuel. The regional classification of the site is studied for the practical purposes. The post-accident analysis is accompanied with multidisciplinary research for the humanoid development in the nuclear industry.

Uncertainty Quantification of Turbulence Model Coefficients via Latin Hypercube Sampling Method

2010 ◽  
Author(s):  
Matthew C. Dunn ◽  
Babak Shotorban ◽  
Abdelkader Frendi
Keyword(s):  
Uncertainty Quantification ◽  
Turbulent Flows ◽  
Turbulence Model ◽  
Sampling Method ◽  
Latin Hypercube Sampling ◽  
Recirculation Region ◽  
Free Shear Layer ◽  
Latin Hypercube ◽  
Reattachment Point ◽  
Turbulent Dissipation

This paper is concerned with the propagation of uncertainties in the values of turbulence model coefficients and parameters in turbulent flows. These coefficients and parameters are determined from experiments performed on elementary flows and they are subject to uncertainty. The widely used k–ε turbulence model is considered. It consists of model transport equations for the turbulence kinetic energy and rate of turbulent dissipation. Both equations involve various model coefficients about which adequate knowledge is assumed known in the form of probability density functions. The study is carried out for the flow over a 2D backward-facing step configuration. The Latin Hypercube Sampling method is employed for the uncertainty quantification purposes as it requires a smaller number of samples compared to the conventional Monte-Carlo method. The mean values are reported for the flow output parameters of interest along with their associated uncertainties. The results show that model coefficient variability has significant effects on the streamwise velocity component in the recirculation region near the reattachment point and turbulence intensity along the free shear layer. The reattachment point location, pressure, and wall shear are also significantly affected.

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