Uncertainty propagation based on correlated sampling technique for nuclear data applications

A correlated sampling technique has been implemented to estimate the impact of cross section modifications on the neutron transport and in Monte Carlo simulations in one single calculation. This implementation has been coupled to a Total Monte Carlo approach which consists in propagating nuclear data uncertainties with random cross section files. The TMC-CS (Total Monte Carlo with Correlated Sampling) approach offers an interesting speed-up of the associated computation time. This methodology is detailed in this paper, together with two application cases to validate and illustrate the gain provided by this technique: the highly enriched uranium/iron metal core reflected by a stainless-steel reflector HMI-001 benchmark, and the PETALE experimental programme in the CROCUS zero-power light water reactor.

Download Full-text

Uncertainty propagation for the design study of the PETALE experimental programme in the CROCUS reactor

EPJ Nuclear Sciences & Technologies ◽

10.1051/epjn/2020004 ◽

2020 ◽

Vol 6 ◽

pp. 9

Author(s):

Axel Laureau ◽

Vincent Lamirand ◽

Dimitri Rochman ◽

Andreas Pautz

Keyword(s):

Variance Reduction ◽

Uncertainty Propagation ◽

Sampling Technique ◽

Nuclear Data ◽

Reaction Rates ◽

Nuisance Parameters ◽

Acceleration Techniques ◽

Experimental Programme ◽

Correlated Sampling ◽

The Impact

The PETALE experimental programme in the CROCUS reactor intends to provide integral measurements to constrain stainless steel nuclear data. This article presents the tools and the methodology developed to design and optimize the experiments, and its operating principle. Two acceleration techniques have been implemented in the Serpent2 code to perform a Total Monte Carlo uncertainty propagation using variance reduction and correlated sampling technique. Their application to the estimation of the expected reaction rates in dosimeters is also discussed, together with the estimation of the impact of the nuisance parameters of aluminium used in the experiment structures.

Download Full-text

Impact of the cross section library on 93mNb activity in VVER-1000 reactor dosimetry

EPJ Web of Conferences ◽

10.1051/epjconf/202022503009 ◽

2020 ◽

Vol 225 ◽

pp. 03009

Author(s):

P. Haroková ◽

M. Lovecký

Keyword(s):

Cross Section ◽

Neutron Transport ◽

Nuclear Data ◽

Nuclear Data Library ◽

The Cross ◽

Materials Used ◽

The Impact ◽

Data Library ◽

Reactor Dosimetry

One of the objectives of reactor dosimetry is determination of activity of irradiated dosimeters, which are placed on reactor pressure vessel surface, and calculation of neutron flux in their position. The uncertainty of calculation depends mainly on the choice of nuclear data library, especially cross section used for neutron transport and cross section used as the response function for neutron activation. Nowadays, number of libraries already exists and can be still used in some applications. In addition, new nuclear data library was recently released. In this paper, we have investigated the impact of the cross section libraries on activity of niobium, one of the popular materials used as neutron fluence monitor. For this purpose, a MCNP6 model of VVER-1000 was made and we have compared the results between 14 commonly used cross section libraries. A possibility of using IRDFF library in activation calculations was also considered. The results show good agreement between the new libraries, with the exception of the most recent ENDF/B-VIII.0, which should be further validated.

Download Full-text

Examples of Monte Carlo techniques applied for nuclear data uncertainty propagation

EPJ Web of Conferences ◽

10.1051/epjconf/201921107008 ◽

2019 ◽

Vol 211 ◽

pp. 07008 ◽

Cited By ~ 1

Author(s):

Oscar Cabellos ◽

Luca Fiorito

Keyword(s):

Monte Carlo ◽

Large Scale ◽

Uncertainty Propagation ◽

Nuclear Data ◽

Monte Carlo Technique ◽

Data Uncertainty ◽

Design Parameters ◽

Monte Carlo Techniques ◽

Bayesian Monte Carlo ◽

The Impact

The aim of this work is to review different Monte Carlo techniques used to propagate nuclear data uncertainties. Firstly, we introduced Monte Carlo technique applied for Uncertainty Quantification studies in safety calculations of large scale systems. As an example, the impact of nuclear data uncertainty of JEFF-3.3 235U, 238U and 239Pu is demonstrated for the main design parameters of a typical 3-loop PWR Westinghouse unit. Secondly, the Bayesian Monte Carlo technique for data adjustment is presented. An example for 235U adjustment using criticality and shielding integral benchmarks shows the importance of performing joint adjustment based on different set of integral benchmarks.

Download Full-text

SUPER-HISTORY METHODS FOR ADJOINT-WEIGHTED TALLIES IN MONTE CARLO TIME EIGENVALUE CALCULATIONS

EPJ Web of Conferences ◽

10.1051/epjconf/202124704008 ◽

2021 ◽

Vol 247 ◽

pp. 04008

Author(s):

F. Filiciotto ◽

A. Jinaphanh ◽

A. Zoia

Keyword(s):

Monte Carlo ◽

Eigenvalue Problems ◽

Neutron Transport ◽

Computation Time ◽

Nuclear Data ◽

Analytic Solutions ◽

Benchmark Problems ◽

Probability Method ◽

Fission Probability ◽

Transport Problems

Time eigenvalues emerge in several key applications related to neutron transport problems, including reactor start-up and reactivity measurements. In this context, experimental validation and uncertainty quantification would demand to assess the variation of the dominant time eigenvalue in response to a variation of nuclear data. Recently, we proposed the use of a Generalized Iterated Fission Probability method (G-IFP) to compute adjoint-weighted tallies, such as kinetic parameters, perturbations and sensitivity coefficients, for Monte Carlo time (or alpha) eigenvalue calculations. With the massive use of parallel Monte Carlo calculations, it would be therefore useful to trade the memory burden of the G-IFP method (which is comparable to that of the standard IFP method for k-eigenvalue problems) for computation time and to rely on history-based schemes for such adjoint-weighted tallies. For this purpose, we investigate the use of the super-history method as applied to estimating adjoint-weighted tallies within the α-k power iteration, based on previous work on k-eigenvalue problems. Verification of the algorithms is performed on some simple preliminary tests where analytic solutions exist. In addition, the performances of the proposed method are assessed by comparing the super-history and the G-IFP methods for the same sets of benchmark problems.

Download Full-text

Efficient use of Monte Carlo: the fast correlation coefficient

EPJ Nuclear Sciences & Technologies ◽

10.1051/epjn/2018019 ◽

2018 ◽

Vol 4 ◽

pp. 15 ◽

Cited By ~ 1

Author(s):

Henrik Sjöstrand ◽

Nicola Asquith ◽

Petter Helgesson ◽

Dimitri Rochman ◽

Steven van der Marck

Keyword(s):

Monte Carlo ◽

Correlation Coefficient ◽

Pearson Correlation ◽

Uncertainty Propagation ◽

Synthetic Data ◽

Nuclear Data ◽

Reactor Spectrum ◽

Flux Reactor ◽

Parameter Dependent ◽

The Impact

Random sampling methods are used for nuclear data (ND) uncertainty propagation, often in combination with the use of Monte Carlo codes (e.g., MCNP). One example is the Total Monte Carlo (TMC) method. The standard way to visualize and interpret ND covariances is by the use of the Pearson correlation coefficient, [see formula in PDF] where x or y can be any parameter dependent on ND. The spread in the output, σ, has both an ND component, σND, and a statistical component, σstat. The contribution from σstat decreases the value of ρ, and hence it underestimates the impact of the correlation. One way to address this is to minimize σstat by using longer simulation run-times. Alternatively, as proposed here, a so-called fast correlation coefficient is used, [see formula in PDF] In many cases, cov(xstat; ystat) can be assumed to be zero. The paper explores three examples, a synthetic data study, correlations in the NRG High Flux Reactor spectrum, and the correlations between integral criticality experiments. It is concluded that the use of ρ underestimates the correlation. The impact of the use of ρfast is quantified, and the implication of the results is discussed.

Download Full-text

Development and validation of uncertainty neutron transport calculations at an industrial scale

EPJ Nuclear Sciences & Technologies ◽

10.1051/epjn/2018031 ◽

2018 ◽

Vol 4 ◽

pp. 45

Author(s):

Julien Gaillet ◽

Thomas Bonaccorsi ◽

Gilles Noguere ◽

Guillaume Truchet

Keyword(s):

Monte Carlo ◽

Neutron Transport ◽

Uncertainty Propagation ◽

Computational Cost ◽

Nuclear Data ◽

Turnaround Time ◽

Monte Carlo Code ◽

Time Calculation ◽

Long Time ◽

Full Core

Evaluating uncertainties on nuclear parameters such as reactivity is a major issue for conception of nuclear reactors. These uncertainties mainly come from the lack of knowledge on nuclear and technological data. Today, the common method used to propagate nuclear data uncertainties is Total Monte Carlo [1] but this method suffers from a long time calculation. Moreover, it requires as many calculations as uncertainties sought. An other method for the propagation of the nuclear data uncertainties consists in using the standard perturbation theory (SPT) to calculate reactivity sensitivity to the desire nuclear data. In such a method, sensitivities are combined with a priori nuclear data covariance matrices such as the COMAC set developed by CEA. The goal of this work is to calculate sensitivites by SPT with the full core diffusion code CRONOS2 for propagation uncertainties at the core level. In this study, COMAC nuclear data uncertainties have been propagated on the BEAVRS benchmark using a two-step APOLLO2/CRONOS2 scheme, where APOLLO2 is the lattice code used to resolve Boltzmann equation within assemblies using a high number of energy groups, and CRONOS2 is the code resolving the 3D full core diffusion equation using only four energy groups. A module implementing the SPT already exists in the APOLLO2 code but computational cost would be too expensive in 3D on the whole core. Consequently, an equivalent procedure has been created in CRONOS2 code to allow full-core uncertainty propagation. The main interest of this procedure is to compute sensitivities on reactivity within a reduced turnaround time for a 3D modeled core, even after fuel depletion. In addition, it allows access to all sensitivites by isotope, reaction and energy group in a single calculation. Reactivity sensitivities calculated by this procedure with four energy groups are compared to reference sensitivities calculated by the iterated fission probability (IFP) method in Monte Carlo code. For the purpose of the tests, dedicated covariance matrix have been created by condensation from 49 to 4 groups of the COMAC matrix. In conclusion, sensitivities calculated by CRONOS2 agree with the sensitivities calculated by the IFP method, which validates the calculation procedure, allowing analysis to be done quickly. In addition, reactivity uncertainty calculated by this method is close to values found for this type of reactor.

Download Full-text

On the impact of nuclear data uncertainties on LWR neutron dosimetry assessments

EPJ Web of Conferences ◽

10.1051/epjconf/202023922003 ◽

2020 ◽

Vol 239 ◽

pp. 22003

Author(s):

Alexander Vasiliev ◽

Marco Pecchia ◽

Dimitri Rochman ◽

Hakim Ferroukhi ◽

Erwin Alhassan

Keyword(s):

Neutron Transport ◽

Uncertainty Propagation ◽

Nuclear Data ◽

Source Distribution ◽

Neutron Dosimetry ◽

Calculation Methodology ◽

Water Reactors ◽

Paul Scherrer ◽

The Impact ◽

The Given

In this work, an overview on the relevance of the nuclear data (ND) uncertainties with respect to the Light Water Reactors (LWR) neutron dosimetry is presented. The paper summarizes results of several studies realized at the LRT laboratory of the Paul Scherrer Institute over the past decade. The studies were done using the base LRT calculation methodology for dosimetry assessments, which involves the neutron source distribution representation, obtained based on validated CASMO/SIMULATE core follow calculation models, and the subsequent neutron transport simulations with the MCNP® software. The methodology was validated using as reference data results of numerous measurement programs fulfilled at Swiss NPPs. Namely, the following experimental programs are considered in the given overview: PWR “gradient probes” and BWR fast neutron fluence (FNF) monitors post irradiation examination (PIE). For the both cases, assessments of the nuclear data related uncertainties were performed. When appropriate, a cross-verification of the deterministic and stochastic based uncertainty propagation techniques is provided. Furthermore, the observations on which particular neutron induced reactions contribute dominantly to the overall ND-related uncertainties are demonstrated. The presented results should help with assessing the overall impact of the various nuclear data uncertainties with respect to dosimetry applications and provide relevant feedback to the nuclear data evaluators.

Download Full-text

Effects of different nuclear evaluation data on the RMC keff calculation

EPJ Web of Conferences ◽

10.1051/epjconf/202023919005 ◽

2020 ◽

Vol 239 ◽

pp. 19005

Author(s):

Zhang Wenxin ◽

Qiang shenglong ◽

Yin qiang ◽

Cui Xiantao

Keyword(s):

Cross Section ◽

Nuclear Power ◽

Power Plants ◽

Nuclear Reactor ◽

Neutron Cross Section ◽

Nuclear Data ◽

Section Data ◽

Calculation Results ◽

The Impact ◽

Physical Calculation

Neutron cross section data is the basis of nuclear reactor physical calculation and has a decisive influence on the accuracy of calculation results. AFA3Gassemble is widely used in nuclear power plants. CENACE is an ACE format multiple-temperature continuous energy cross section library that developed by China Nuclear Data Centre. In this paper, we calculated the AFA3G assemble by RMC.We respectively used ENDF6.8/, ENDF/7 and CENACE data for calculation. The impact of nuclear data on RMC calculation is studied by comparing the results of different nuclear data.

Download Full-text