Abstract
This article describes the nuclear data covariance analysis of an experimental design for a neutron energy-tuning assembly (ETA) created to shape a 14-MeV neutron point source to an objective spectrum. Underlying nuclear data uncertainties play a large role in the radiation transport and reaction rates for the range of responses to be expected from an experiment. The methodology leveraged the Standardized Computer Analysis for Licensing Evaluation (SCALE) Sampler module to determine the uncertainty in the neutron transport. The reaction uncertainty was perturbed with the International Reactor Dosimetry and Fusion File v.1.05 uncertainty, correlation matrix, and reaction cross section through multivariate normal distribution sampling to provide a final response metric. The resultant neutron fluence uncertainty for the ETA ranged from 2.7% to 6.2% in the energy range from 1.28 keV to 14.1 MeV, which contains 99.99% of the neutron fluence. The integrated uncertainties, including statistical and systematic nuclear data uncertainties, for the reaction products analyzed were 2.33% to 4.84% for most reactions, but 55Mn(n, $\gamma $ ), a less well-characterized reaction occurring in an energy domain with high flux uncertainty, was 19.7%. The mean of the reaction distributions was within 1.1% of the unperturbed nuclear data simulation. The experiment is planned for late 2019, where the predicted results will be compared against the experimental outcomes. The methodology presented can be utilized with alternate nuclear libraries in SCALE to develop uncertainty bounds and neutron flux spectra for many radiation-transport problems.
Highlights
N UCLEAR data covariance analysis is an important modeling technique to consider when simulating the expected outcomes from one-off, high-cost experiments, the range of operating conditions that can lead to safety considerations in the reactors, or the criticality safety in the enriched uranium or plutonium operations [1], [2]
The planned energy-tuning assembly (ETA) experiment at the National Ignition Facility (NIF) is of relatively large cost, so capturing the impact of nuclear data covariances is important to understand the range of potential experimental outcomes
Some of these deviations are based on the group structure utilized, but others are due to incomplete nuclear data covariance information, which necessitated the effort noted above to generate complete libraries for use with stochastic sampling and necessitated the to approximate the behavior of the MCNP’s surface source
Summary
N UCLEAR data covariance analysis is an important modeling technique to consider when simulating the expected outcomes from one-off, high-cost experiments, the range of operating conditions that can lead to safety considerations in the reactors, or the criticality safety in the enriched uranium or plutonium operations [1], [2]. Stochastic methods rely on performing independent neutron-transport calculations with perturbed nuclear data libraries sampled based on the covariances of the cross sections to build a distribution of the responses [6]. Monte Carlo N-Particle 5 (MCNP5) is utilized to perform continuous-energy (CE) neutron-transport simulations that were mapped to the SCALE Monaco with Automated Variance Reduction using Importance Calculations (MAVRIC) sequence [10], [20]. The SCALE Sampler sequence is a general-purpose total Monte Carlo approach and is utilized to assess the systematic uncertainties for a given radiation-transport simulation that can include nuclear data, material specification, and geometry.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
