Speaker
Description
LA-UR-21-29561
Abstract
High-fidelity simulation of nuclear systems are paramount to the operational success and safety of many nuclear applications. Simulations rely on quality nuclear data. The nuclear data input for these simulations is continuously being reassessed by nuclear data evaluators. One way to support this ongoing evaluation is to present the evaluators with detailed analysis from data assimilation techniques. These techniques examine major sources of nuclear data-induced uncertainties in important nuclear parameters, such as measures of criticality. In this work, a data assimilation technique called the bias factor method will be utilized to improve the predicted accuracy and precision of the effective neutron multiplication factor documented for multiple International Criticality Safety Benchmark Experiment Project (ICSBEP) benchmarks [1]. This data assimilation technique has been shown to be successful in reducing nuclear data-induced uncertainty in the effective neutron multiplication factor by researchers at Nagoya University [2]. To perform calculations of the bias factor, one must calculate sensitivity coefficient matrices of a parameter of interest. The parameters to be examined in this work include the effective delayed neutron fraction, reactivity coefficient, and prompt neutron decay constant. Monte Carlo N-Particle® Code Version 6.21 (MCNP®6.2) will be used in a non-invasive manner (i.e., source code will not be modified) to calculate these sensitivity coefficients. The calculation of these sensitivity coefficients follows work performed by researchers at TerraPower LLC [3] and Jožef Stefan Institute [4]. The data will be subsequently used to assist with the optimization of an integral experiment for the Experiments Underpinned by Computational Learning for Improvements in Nuclear Data (EUCLID) project at Los Alamos National Laboratory. By examining the results of the bias factor method with the three aforementioned parameters, more certainty can be attributed to the optimal integral experiment providing results that can improve/adjust uncertain or inaccurate nuclear data.
1 MCNP® and Monte Carlo N-Particle® are registered trademarks owned by Triad National Security, LLC, manager and operator of Los Alamos National Laboratory. Any third party use of such registered marks should be properly attributed to Triad National Security, LLC, including the use of the designation as appropriate. For the purposes of visual clarity, the registered trademark symbol is assumed for all references to MCNP within the remainder of this paper.
Acknowledgements
Research reported in this publication was supported by the U.S. Department of Energy Laboratory Directed Research and Development (LDRD) program at Los Alamos National Laboratory. Los Alamos National Laboratory is operated by Triad National Security, LLC, for the National Nuclear Security Administration of the US Department of Energy under Contract No.89233218CNA000001.
References
[1] J. D. Bess, T. Ivanova, L. Scott, I. Hill, “The 2019 Edition of the ICSBEP Handbook,” Transactions of the American Nuclear Society, 121, 901-904 (2019).
[2] T. Endo, A. Yamamoto, “Data Assimilation Using Subcritical Measurement of Prompt Neutron Decay Constant,” Nuclear Science and Engineering, 194, 1089-1104 (2020).
[3] N. Touran, J. Yang, “Sensitivities and Uncertainties Due to Nuclear Data in a Traveling Wave Reactor,” PHYSOR, Sun Valley, Idaho, May 1 – 5 (2016).
[4] I. Kodeli, “Sensitivity and uncertainty in the effective delayed neutron fraction (β_eff),” Nuclear Instruments and Methods in Physics Research A, 715, 70-78 (2013)