GENERALIZED SENSITIVITY ANALYSIS CAPABILITY WITH THE DIFFERENTIAL OPERATOR METHOD IN RMC CODE

Sensitivity analysis is an important way for us to know how the input parameters will affect the output of a system. Therefore, recently, there is an increased interest in developing sensitivity analysis methods in continuous-energy Monte Carlo Code due to the fact that Monte Carlo method can perform high-fidelity simulations of nuclear reactor. Previous studies mainly focused on developing sensitivity analysis method suitable for analyze eigenvalue. There are relatively few researches for performing sensitivity analysis of generalized response function by using continuous-energy Monte Carlo code. So, in this work, the differential operator method (DOM) has been investigated and implemented in continuous-energy Reactor Monte Carlo code (RMC) to perform sensitivity analysis of generalized response function in the form of ratios of reaction rate. The DOM implemented in RMC is based on the analog Monte Carlo transport mode and non-analog Monte Carlo transport mode. The correctness of the newly implemented method has been verified by comparing the results with those calculated by using the collision history-based method through the Jezeble and Flattop benchmark problems. In general, the results given by the DOM agree well with those obtained by the collision history-based method with an accuracy of 5%. Moreover, it is also shown that the non-analog Monte Carlo transport mode can obtain lower relative standard deviation of the sensitivity coefficients than the analog Monte Carlo transport mode

GENERALIZED SENSITIVITY ANALYSIS CAPABILITY WITH THE DIFFERENTIAL OPERATOR METHOD IN RMC CODE

Sensitivity analysis is an important way for us to know how the input parameters will affect the output of a system. Therefore, recently, there is an increased interest in developing sensitivity analysis methods in continuous-energy Monte Carlo Code due to the fact that Monte Carlo method can perform high-fidelity simulations of nuclear reactor. Previous studies mainly focused on developing sensitivity analysis method suitable for analyze eigenvalue. There are relatively few researches for performing sensitivity analysis of generalized response function by using continuous-energy Monte Carlo code. So, in this work, the differential operator method (DOM) has been investigated and implemented in continuous-energy Reactor Monte Carlo code (RMC) to perform sensitivity analysis of generalized response function in the form of ratios of reaction rate. The DOM implemented in RMC is based on the analog Monte Carlo transport mode and non-analog Monte Carlo transport mode. The correctness of the newly implemented method has been verified by comparing the results with those calculated by using the collision history-based method through the Jezeble and Flattop benchmark problems. In general, the results given by the DOM agree well with those obtained by the collision history-based method with an accuracy of 5%. Moreover, it is also shown that the non-analog Monte Carlo transport mode can obtain lower relative standard deviation of the sensitivity coefficients than the analog Monte Carlo transport mode

New water drive characteristic curves at ultra-high water cut stage

A function expression of the oil-water relative permeability ratio with normalized water saturation at high water saturation was proposed based on statistics of measured oil-water relative permeability data in oilfields. This expression fits the later section of conventional relative permeability ratio curve more accurately. Two new water drive characteristic curves at the ultra-high water cut stage (fw>90%) were derived by combining the new oil-water relative permeability ratio expression and reservoir engineering method. Then, the numerical simulation results of five-point well pattern and production data of Yangerzhuang Oilfield and Liuzan Oilfield were used to verify the adaptability of the new water drive characteristic curves. The results showed that the new water drive characteristic curves are more accurate than conventional water drive characteristic curves after A type and B type water drive curves rise, and can be used to predict production performance at ultra-high water cut stage, ultimate recovery efficiency and recoverable reserves. Key words: water flooding development, ultra-high water cut stage, water drive characteristic curve, recoverable reserve

Nuclear data sensitivity analysis and uncertainty propagation in the KYADJ whole-core transport code

Nuclear data sensitivity analysis and uncertainty propagation have been extensively applied to nuclear data adjustment and uncertainty quantification in the field of nuclear engineering. Sensitivity and Uncertainty (S&U) analysis is developed in the KYADJ whole-core transport code in order to meet the requirement of advanced reactor design. KYADJ aims to use two-dimension Method of Characteristic (MOC) and one-dimension discrete ordinate (SN) coupled method to solve the neutron transport equation and achieve one-step direct transport calculation of the reactor core. Developing sensitivity and uncertainty analysis module in KYADJ can minimize deviations caused by modeling approximation and enhance calculation efficiency. This work describes the application of the classic perturbation theory to the KYADJ transport solver. In order to obtain uncertainty, a technique is proposed for processing a covariance data file in 45-group energy grid instead of 44-group SCALE 6.1 covariance data which is extensively used in various codes. Numerical results for Uncertainty Analysis in Modelling (UAM) benchmarks and the SF96 benchmark are presented. The results agree well with the reference and the capability of S&U analysis in KYADJ is verified

Nuclear data sensitivity analysis and uncertainty propagation in the KYADJ whole-core transport code

Nuclear data sensitivity analysis and uncertainty propagation have been extensively applied to nuclear data adjustment and uncertainty quantification in the field of nuclear engineering. Sensitivity and Uncertainty (S&U) analysis is developed in the KYADJ whole-core transport code in order to meet the requirement of advanced reactor design. KYADJ aims to use two-dimension Method of Characteristic (MOC) and one-dimension discrete ordinate (SN) coupled method to solve the neutron transport equation and achieve one-step direct transport calculation of the reactor core. Developing sensitivity and uncertainty analysis module in KYADJ can minimize deviations caused by modeling approximation and enhance calculation efficiency. This work describes the application of the classic perturbation theory to the KYADJ transport solver. In order to obtain uncertainty, a technique is proposed for processing a covariance data file in 45-group energy grid instead of 44-group SCALE 6.1 covariance data which is extensively used in various codes. Numerical results for Uncertainty Analysis in Modelling (UAM) benchmarks and the SF96 benchmark are presented. The results agree well with the reference and the capability of S&U analysis in KYADJ is verified

Eigenvalue sensitivity and uncertainty analysis based on a 2-D/1-D whole-core transport code KYADJ

An evaluated nuclear data file is one of the important footstones in reactor physics design. Nuclear data can be viewed as arguments of output parameters. Therefore, the uncertainty of nuclear data can directly influence on the uncertainty of output parameters. A process of Sensitivity and Uncertainty (S&U) analysis is used to quantify the influence. In this study, we choose 2-D/1-D transport code to develop S&U analysis in order to reduce the impact of numerical model uncertainty. We can solve 2-D/1-D adjoint transport equation and apply the classical perturbation theory to sensitivity analysis of eigenvalue with respect to nuclear data. In addition, we obtain the uncertainty of eigenvalue using the "Sandwich" rule. The Peach Bottom-2 (PB-2) BWR cell benchmark and the Three Mile Island-1 (TMI-1) PWR cell benchmark are applied to verification. The Reactor Monte Carlo code RMC is used as a reference code. The results show that the code has ability of eigenvalue sensitivity and uncertainty analysis with high accuracy. (C) 2018 Elsevier Ltd. All rights reserve