scholarly journals Evaluation of severe accident risks: Quantification of major input parameters

1991 ◽  
Author(s):  
F.T. Harper ◽  
A.C. Payne ◽  
R.J. Breeding ◽  
E.D. Gorham ◽  
T.D. Brown ◽  
...  
Keyword(s):  
Severe Accident ◽  
Input Parameters

Uncertainty Quantification in Support of Severe Accident Analysis Code User Confidence Using MELCOR-DAKOTA

2021 ◽  
Author(s):  
Emmanuel Boafo ◽  
Emmanuel Numapau Gyamfi
Keyword(s):  
Sensitivity Analysis ◽  
Uncertainty Quantification ◽  
Hydrogen Generation ◽  
Input Parameter ◽  
Distribution Functions ◽  
Physical Models ◽  
Severe Accident ◽  
Accident Analysis ◽  
Melt Flow Rate ◽  
Input Parameters

Abstract Uncertainty and Sensitivity analysis methods are often used in severe accident analysis for validating the complex physical models employed in the system codes that simulate such scenarios. This is necessitated by the large uncertainties associated with the physical models and boundary conditions employed to simulate severe accident scenarios. The input parameters are sampled within defined ranges based on assigned probability distribution functions (PDFs) for the required number of code runs/realizations using stochastic sampling techniques. Input parameter selection is based on their importance to the key FOM, which is determined by the parameter identification and ranking table (PIRT). Sensitivity analysis investigates the contribution of each uncertain input parameter to the uncertainty of the selected FOM. In this study, the integrated severe accident analysis code MELCOR was coupled with DAKOTA, an optimization and uncertainty quantification tool in order to investigate the effect of input parameter uncertainty on hydrogen generation. The methodology developed was applied to the Fukushima Daiichi unit 1 NPP accident scenario, which was modelled in another study. The results show that there is approximately 22.46% uncertainty in the amount of hydrogen generated as estimated by a single MELCOR run given uncertainty in selected input parameters. The sensitivity analysis results also reveal that MELCOR input parameters; COR_SC 1141(Melt flow rate per unit width at breakthrough candling) , COR_ZP (Porosity of fuel debris beds) and COR_EDR (Characteristic debris size in core region) contributed most significantly to the uncertainty in hydrogen generation.

A Systematic Approach for Severe Accident Uncertainty Analysis

2016 ◽  
Author(s):  
Seyed Mohsen Hoseyni ◽  
Mohammad Pourgol-Mohammad
Keyword(s):  
Uncertainty Analysis ◽  
Model Uncertainty ◽  
Uncertainty Assessment ◽  
Severe Accident ◽  
Test Facility ◽  
Model Uncertainties ◽  
Uncertainty Sources ◽  
Output Uncertainty ◽  
Input Parameters ◽  
Physical And Chemical

Uncertainty exists in every modeling process especially in those areas with complexity of the calculations like severe accident (SA) code which cover a broad range of physical and chemical phenomena. A systematic framework is proposed here for effective uncertainty assessment of SA computations by efficient use of available data and information. Available methodologies are either input-based or output based. The proposed methodology takes the advantages of both approaches and introduces an integrated one which quantifies the uncertainty of code input parameters (parameter uncertainty), code internal structure (model uncertainty) and code outputs (output uncertainty). The proposed methodology is comprisd of a hybrid qualitative and quantitative approach for identification of uncertainty sources. Using a Bayesian ensemble of sensitivity measures, identified severe accident phenomena are ranked according to their effect on the figure of merit. The other feature of the proposed methodology is the consideration of the SA code structural uncertainties (generally known as model uncertainty) explicitly by treating internal sub-model uncertainties and by propagating such model uncertainties in the code calculations, including uncertainties about input parameters. The code output is further updated through additional Bayesian updating with available experimental data from the integrated test facilities. In this paper, the key elements are discussed for the uncertainty analysis methodology and its application is demonstrated on the LP-FP2 experiment of LOFT test facility.

Modelling of QUENCH 03 and QUENCH 06 Experiments Using RELAP/SCDAPSIM Code

2013 ◽  
Author(s):  
Tadas Kaliatka ◽  
Eugenijus Ušpuras ◽  
Algirdas Kaliatka
Keyword(s):  
Severe Accident ◽  
Light Water Reactors ◽  
Core Flooding ◽  
Case Scenario ◽  
Worst Case ◽  
Light Water ◽  
Input Parameters ◽  
Calculation Results ◽  
Water Reactors ◽  
Quench Test

An important accident management measure for controlling severe accident transients in Light Water Reactors is the injection of water to cool the degrading core. Flooding of the overheated core, which causes quenching of the fuel rods, is considered a worst-case scenario regarding hydrogen generation rates which should not exceed safety-relevant critical values. Within the frame of the QUENCH test-program the loss of coolant accidents with the following flooding of overheated core in Light Water Reactors is analysed using an experimental facility. The modelling of QUENCH-03 and QUENCH-06 experiments was performed with RELAP/SCDAPSIM computer code. The observed calculation results showed that thermal properties of shroud materials (heat losses through the shroud) and electrical power of fuel simulators are the main source of uncertainty in the calculations. The main idea of this article is modification of input parameters to receive the best agreement with the measurements for the selected QUENCH test. Modified input parameters are used in the input deck for another QUENCH test. The good agreement between calculation results and measurements of both QUENCH tests demonstrated the correctness of modified parameters and legitimacy with the real physical processes.

Fuzzy Sets Theory in Comparison with Stochastic Methods to Analyse Nonlinear Behaviour of a Steel Member under Compression

2005 ◽  
Vol 10 (1) ◽  
pp. 65-75 ◽  
Author(s):  
Z. Kala
Keyword(s):  
Fuzzy Sets ◽  
Carrying Capacity ◽  
Stochastic Methods ◽  
Nonlinear Behaviour ◽  
Load Carrying Capacity ◽  
Load Carrying ◽  
Input Parameters ◽  
Stochastic Solution ◽  
Fuzzy Solution ◽  
Sets Theory

The load-carrying capacity of the member with imperfections under axial compression is analysed in the present paper. The study is divided into two parts: (i) in the first one, the input parameters are considered to be random numbers (with distribution of probability functions obtained from experimental results and/or tolerance standard), while (ii) in the other one, the input parameters are considered to be fuzzy numbers (with membership functions). The load-carrying capacity was calculated by geometrical nonlinear solution of a beam by means of the finite element method. In the case (ii), the membership function was determined by applying the fuzzy sets, whereas in the case (i), the distribution probability function of load-carrying capacity was determined. For (i) stochastic solution, the numerical simulation Monte Carlo method was applied, whereas for (ii) fuzzy solution, the method of the so-called α cuts was applied. The design load-carrying capacity was determined according to the EC3 and EN1990 standards. The results of the fuzzy, stochastic and deterministic analyses are compared in the concluding part of the paper.

STATUS OF THE ESTER SEVERE ACCIDENT CODE SYSTEM

1995 ◽  
Author(s):  
A. V. Jones ◽  
I. Shepherd ◽  
M. Delaval ◽  
J. Sangregorio ◽  
S. Treta
Keyword(s):  
Severe Accident ◽  
Code System
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