scholarly journals Modern Monte Carlo Variants for Uncertainty Quantification in Neutron Transport

2018 ◽  
pp. 455-481 ◽  
Author(s):  
Ivan G. Graham ◽  
Matthew J. Parkinson ◽  
Robert Scheichl
Keyword(s):  
Monte Carlo ◽  
Uncertainty Quantification ◽  
Neutron Transport

3D Calculations of PWR Vessels Neutron Fluence With EFLUVE 3D Code

2013 ◽  
Author(s):  
Cécile-Aline Gosmain ◽  
Sylvain Rollet ◽  
Damien Schmitt
Keyword(s):  
Monte Carlo ◽  
Fast Neutron ◽  
Pressure Vessel ◽  
Reaction Rate ◽  
Neutron Fluence ◽  
Neutron Transport ◽  
Reaction Rates ◽  
Surveillance Program ◽  
Monte Carlo Code ◽  
Neutron Sources

In the framework of surveillance program dosimetry, the main parameter in the determination of the fracture toughness and the integrity of the reactor pressure vessel (RPV) is the fast neutron fluence on pressure vessel. Its calculated value is extrapolated using neutron transport codes from measured reaction rate value on dosimeters located on the core barrel. EDF R&D has developed a new 3D tool called EFLUVE3D based on the adjoint flux theory. This tool is able to reproduce on a given configuration the neutron flux, fast neutron fluence and reaction rate or dpa results of an exact Monte Carlo calculation with nearly the same accuracy. These EFLUVE3D calculations does the Source*Importance product which allows the calculation of the flux, the neutronic fluence (flux over 1MeV integrated on time) received at any point of the interface between the skin and the pressure vessel but also at the capsules of the pressurized water reactor vessels surveillance program and the dpa and reaction rates at different axial positions and different azimuthal positions of the vessel as well as at the surveillance capsules. Moreover, these calculations can be carried out monthly for each of the 58 reactors of the French current fleet in challenging time (less than 10mn for the total fluence and reaction rates calculations considering 14 different neutron sources of a classical power plant unit compared to more than 2 days for a classic Monte Carlo flux calculation at a given neutron source). The code needs as input: - for each reaction rate, the geometric importance matrix produced for a 3D pin by pin mesh on the basis of Green’s functions calculated by the Monte Carlo code TRIPOLI; - the neutron sources calculated on assemblies data (enrichment, position, fission fraction as a function of evolution), pin by pin power and irradiation. These last terms are based on local in-core activities measurements extrapolated to the whole core by use of the EDF core calculation scheme and a pin by pin power reconstruction methodology. This paper presents the fundamental principles of the code and its validation comparing its results to the direct Monte Carlo TRIPOLI results. Theses comparisons show a discrepancy of less than 0,5% between the two codes equivalent to the order of magnitude of the stochastic convergence of Monte Carlo results.

scholarly journals Methods for the Calculation of Thermoacoustic Stability Margins and Monte Carlo-Free Uncertainty Quantification

2017 ◽  
Author(s):  
Georg A. Mensah ◽  
Luca Magri ◽  
Jonas P. Moeck
Keyword(s):  
Monte Carlo ◽  
Factor Analysis ◽  
Risk Factor ◽  
Uncertainty Quantification ◽  
Frequency Domain ◽  
Gas Turbines ◽  
Network Models ◽  
System Stability ◽  
Risk Factor Analysis ◽  
Model Parameters

Thermoacoustic instabilities are a major threat for modern gas turbines. Frequency-domain based stability methods, such as network models and Helmholtz solvers, are common design tools because they are fast compared to compressible CFD computations. Frequency-domain approaches result in an eigenvalue problem, which is nonlinear with respect to the eigenvalue. Nonlinear functions of the frequency are, for example, the n–τ model, impedance boundary conditions, etc. Thus, the influence of the relevant parameters on mode stability is only given implicitly. Small changes in some model parameters, which are obtained by experiments with some uncertainty, may have a great impact on stability. The assessment of how parameter uncertainties propagate to system stability is therefore crucial for safe gas turbine operation. This question is addressed by uncertainty quantification. A common strategy for uncertainty quantification in thermoacoustics is risk factor analysis. It quantifies the uncertainty of a set of parameters in terms of the probability of a mode to become unstable. One general challenge regarding uncertainty quantification is the sheer number of uncertain parameter combinations to be quantified. For instance, uncertain parameters in an annular combustor might be the equivalence ratio, convection times, geometrical parameters, boundary impedances, flame response model parameters etc. Assessing also the influence of all possible combinations of these parameters on the risk factor is a numerically very costly task. A new and fast way to obtain algebraic parameter models in order to tackle the implicit nature of the eigenfrequency problem is using adjoint perturbation theory. Though adjoint perturbation methods were recently applied to accelerate the risk factor analysis, its potential to improve the theory has not yet been fully exploited. This paper aims to further utilize adjoint methods for the quantification of uncertainties. This analytical method avoids the usual random Monte Carlo simulations, making it particularly attractive for industrial purposes. Using network models and the open-source Helmholtz solver PyHoltz it is also discussed how to apply the method with standard modeling techniques. The theory is exemplified based on a simple ducted flame and a combustor of EM2C laboratory for which experimental validation is available.

scholarly journals ANALYSIS OF UNCERTAINTY QUANTIFICATION METHOD BY COMPARING MONTE-CARLO METHOD AND WILKS’ FORMULA

2014 ◽  
Vol 46 (4) ◽  
pp. 481-488 ◽  
Author(s):  
SEUNG WOOK LEE ◽  
BUB DONG CHUNG ◽  
YOUNG-SEOK BANG ◽  
SUNG WON BAE
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Method ◽  
Uncertainty Quantification ◽  
Quantification Method

scholarly journals Uncertainty quantification through the Monte Carlo method in a cloud computing setting

2014 ◽  
Vol 185 (5) ◽  
pp. 1355-1363 ◽  
Author(s):  
Americo Cunha ◽  
Rafael Nasser ◽  
Rubens Sampaio ◽  
Hélio Lopes ◽  
Karin Breitman
Keyword(s):  
Cloud Computing ◽  
Monte Carlo ◽  
Monte Carlo Method ◽  
Uncertainty Quantification ◽  
The Monte Carlo Method
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