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At least 91 records · Page 5

Testing actinide fission yield treatment in CINDER90 for use in MCNP6 burnup calculations

Most of the development of the MCNPX/6 burnup capability focused on features that were applied to the Boltzman transport or used to prepare coefficients for use in CINDER90, with little change to CINDER90 or the CINDER90 data. Though a scheme exists for best solving the coupled Boltzman and Bateman equations, the most significant approximation is that the employed nuclear data are correct and complete. Thus, the CINDER90 library file contains 60 different actinide fission yields encompassing 36 fissionable actinides (thermal, fast, high energy and spontaneous fission). Fission reaction data exists for more than 60 actinides and as a result, fission yield data must be approximated for actinides that do not possess fission yield information. Several types of approximations are used for estimating fission yields for actinides which do not possess explicit fission yield data. The objective of this study is to test whether or not certain approximations of fission yield selection have any impact on predictability of major actinides and fission products. Further we assess which other fission products, available in MCNP6 Tier 3, result in the largest difference in production. Because the CINDER90 library file is in ASCII format and therefore easily amendable, we assess reasons for choosing, as well as compare actinide and major fission product prediction for the H. B. Robinson benchmark for, three separate fission yield selection methods: (1) the current CINDER90 library file method (Base); (2) the element method (Element); and (3) the isobar method (Isobar). Results show that the three methods tested result in similar prediction of major actinides, Tc-99 and Cs-137; however, certain fission products resulted in significantly different production depending on the method of choice.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

The Los Alamos CP2011 ACE Format Charged Particle Transport Library for MCNP6

Twenty-five ACE tables for MCNP6 are given for the various combinations of incident protons, deuterons, tritons, helions (He-3), and alphas onto targets of protons, deuterons, tritons, helions, alphas, Li-6 and Li-7. An appropriate xsdir file is also included. The usual convention has been followed that the lighter charged particle is defined to be the incident particle and the heavier is defined to be the target.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Studying the Random Number Generators in MCNP6 using an Analytic Benchmark

An analytic solution to a previously studied toy problem is derived and used as a code verification benchmark. Using various Random Number Generators (RNGs) in MCNP6, including the newest SFC64 RNG available in MCNP6.3.1, and their various properties (e.g., RNG stride), we show how these RNGs perform and how to correct or workaround potential issues with respect to the analytic benchmark problem.

97 MATHEMATICS AND COMPUTING↗

EBR-II Static Neutronic Calculations by PHISICS / MCNP6 codes

The International Atomic Energy Agency (IAEA) launched a Coordinated Research Project (CRP) on the Shutdown Heat Removal Tests (SHRT) performed in the '80s at the Experimental fast Breeder Reactor EBR-II, USA. The scope of the CRP is to improve and validate the simulation tools for the study and the design of the liquid metal cooled fast reactors. Moreover, training of the next generation of fast reactor analysts is being also considered the other scope of the CRP. In this framework, a static neutronic model was developed, using state-of-the art neutron transport codes like SCALE/PHISICS (deterministic solution) and MCNP6 (stochastic solution). Comparison between both solutions is briefly illustrated in this summary.

Code Validation↗

MCNP6 Results for the Phase III Sensitivity Benchmark of the OCED/NEA Expert Group on Uncertainty Analysis for Criticality Safety Assessment

Within the last decade, there has been increasing interest in the calculation of cross section sensitivity coefficients of k eff for integral experiment design and uncertainty analysis. The OECD/NEA has an Expert Group devoted to Sensitivity and Uncertainty Analysis within the Working Party for Nuclear Criticality Safety. This expert group has developed benchmarks to assess code capabilities and performance for doing sensitivity and uncertainty analysis. Phase III of a set of sensitivity benchmarks evaluates capabilities for computing sensitivity coefficients. MCNP6 has the capability to compute cross section sensitivities for k eff using continuous-energy physics. To help verify this capability, results for the Phase III benchmark cases are generated and submitted to the Expert Group for comparison. The Phase III benchmark has three cases: III.1, an array of MOX fuel pins, III.2, a series of infinite lattices of MOX fuel pins with varying pitches, and III.3 two spheres with homogeneous mixtures of UF 4 and polyethylene with different enrichments.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

Verification of Unstructured Mesh Capabilities in MCNP6 for Reactor Physics Problems

New unstructured mesh capabilities in MCNP6 (developmental version during summer 2012) show potential for conducting multi-physics analyses by coupling MCNP to a finite element solver such as Abaqus/CAE. Before these new capabilities can be utilized, the ability of MCNP to accurately estimate eigenvalues and pin powers using an unstructured mesh must first be verified. Previous work to verify the unstructured mesh capabilities in MCNP was accomplished using the Godiva sphere, and this work attempts to build on that. To accomplish this, a criticality benchmark and a fuel assembly benchmark were used for calculations in MCNP using both the Constructive Solid Geometry (CSG) native to MCNP and the unstructured mesh geometry generated using Abaqus/CAE. The Big Ten criticality benchmark was modeled due to its geometry being similar to that of a reactor fuel pin. The C5G7 3-D Mixed Oxide (MOX) Fuel Assembly Benchmark was modeled to test the unstructured mesh capabilities on a reactor-type problem.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

Delayed-Neutron and Delayed-Photon Dose-Rate Estimations for Chemical Processing of Spent Fuel Using MCNP6

A nondestructive assay (NDA) computational study is conducted using the MCNP6 Monte Carlo radiation-transport code to calculate dose estimates caused by delayed-neutron (DN) and delayed-gamma (DG) emission for spent nuclear fuel (SNF) that is immersed in nitric acid. The SNF isotopic inventories and delayed-particle (DP) sources are prepared using a generic Westinghouse fuel assembly model for three sets of parameters.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗

Quick-Start Guide to Low-Energy Photon/Electron Transport in MCNP6

A new feature of the particle transport code MCNP6 is the ability to transport photons and electrons below the traditional lower energy limit of 1 keV. Full discussions of this new capability, its supporting data, and its methods, especially the new single-event electron transport, a data-based method that replaces condensed-history in selected energy ranges, will appear in various publications in the near future. In this document, we present a few practical points that users of the new feature will need to know.

61 RADIATION PROTECTION AND DOSIMETRY↗

MCNP6™ Release 1.0: Creating and Testing the Code Distribution

This report documents the preparations for and testing of the production release of MCNP6™ through RSICC at ORNL. It addresses tests on supported operating systems (Linux, MacOSX, Windows) with the supported compilers (Intel, Portland Group International, PathsScale and gfortran). Verification and Validation test results are documented elsewhere. This report does not address in detail the overall packaging of the distribution. Specifically, it does not address the nuclear and atomic data collection, the other included software packages (MCNP5 and MCNPX) and the collection of reference documents.

07 ISOTOPE AND RADIATION SOURCES↗

Implementation of Cerenkov Radiation and Reflection/Refraction into MCNP6

This is a summary report on implementation of Cerenkov Emission and Reflection/Refraction in MCNP6. Both of these mechanisms depend on the refractive index of a material. An interface has been written which allows these to be specified by material. The physics of both have been implemented for optical light photons (200 to 800 nm). Examples of results using these new physics capabilities are shown. Some issues which could be addressed in follow-on development in the future are discussed.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Characterization of the NPOD3 Detectors in MCNP5 and MCNP6

Researchers performed a series of measurements in May 2012 to characterize the NPOD3 detector systems. The detectors were placed in varying states of disassembly to determine the effect of individual components on detection efficiency. A 4.5 kg α-phase Pu sphere known as the Los Alamos BeRP Ball was used as the SNM source in both a bare configuration and reflected by varying thicknesses of polyethylene. A set of simulations matching the experimental setups were run and the data were compared to the measured data. The total and leakage multiplication and the inferred k values were determined for both the simulations and the measurements. Table 3 shows a comparison of the results from MCNP6 and MCNP5 with the list-mode patch to the measured results. The count rates for the calculated results were obtained by dividing the total line count in the list-mode file (equivalent to the total number of absorptions in the NPOD detectors) by the total run time. The count rates are identical for both codes, and they both produce the same multiplicity and inferred k values regardless of measurement time as expected.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

A New On-the-Fly Sampling Method for Incoherent Inelastic Thermal Neutron Scattering Data in MCNP6

At thermal energies, the scattering of neutrons in a system is complicated by the comparable velocities of the neutron and target, resulting in competing upscattering and downscattering events. The neutron wavelength is also similar in size to the target's interatomic spacing making the scattering process a quantum mechanical problem. Because of the complicated nature of scattering at low energies, the thermal data files in ACE format used in continuous-energy Monte Carlo codes are quite large { on the order of megabytes for a single temperature and material. In this paper, a new storage and sampling method is introduced that is orders of magnitude less in size and is used to sample scattering parameters at any temperature on-the-fly. In addition to the reduction in storage, the need to pre-generate thermal scattering data tables at fine temperatures has been eliminated. This is advantageous for multiphysics simulations which may involve temperatures not known in advance. A new module was written for MCNP6 that bypasses the current S(α,β) table lookup in favor of the new format. The new on-the-fly sampling method was tested for graphite for two benchmark problems at ten temperatures: 1) an eigenvalue test with a fuel compact of uranium oxycarbide fuel homogenized into a graphite matrix, 2) a surface current test with a \broomstick" problem with a monoenergetic point source. The largest eigenvalue difference was 152pcm for T= 1200K. For the temperatures and incident energies chosen for the broomstick problem, the secondary neutron spectrum showed good agreement with the traditional S(α,β) sampling method. These preliminary results show that sampling thermal scattering data on-the-fly is a viable option to eliminate both the storage burden of keeping thermal data at discrete temperatures and the need to know temperatures before simulation runtime.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

MCNP6. Simulating Correlated Data in Fission Events

This report is a series of slides discussing the MCNP6 code and its status in simulating fission. Applications of interest include global security and nuclear nonproliferation, detection of special nuclear material (SNM), passive and active interrogation techniques, and coincident neutron and photon leakage.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Semi-Analytic Benchmarks for MCNP6

This series of slides shows results for benchmarks for code verifications in MCNP6. For additional related information see report LA-UR--16-28568.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Validation of MCNP6 Version 1.0 with the ENDF/B-VII.1 Cross Section Library for Plutonium Metals, Oxides, and Solutions on the High Performance Computing Platform Moonlight

This report documents a validation of the MCNP6 Version 1.0 computer code on the high performance computing platform Moonlight, for operations at Los Alamos National Laboratory (LANL) that involve plutonium metals, oxides, and solutions. The validation is conducted using the ENDF/B-VII.1 continuous energy group cross section library at room temperature. The results are for use by nuclear criticality safety personnel in performing analysis and evaluation of various facility activities involving plutonium materials.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

MCNP6 and DRiFT modeling efforts for the NEUANCE/DANCE detector array

This report seeks to study and benchmark code predictions against experimental data; determine parameters to match MCNP-simulated detector response functions to experimental stilbene measurements; add stilbene processing capabilities to DRiFT; and improve NEUANCE detector array modeling and analysis using new MCNP6 and DRiFT features.

47 OTHER INSTRUMENTATION↗

MCNP6 UM Utility Programs [Slides]

The purpose of these slides is to introduce the user to MCNP6’s utility programs for the unstructured mesh capability.

97 MATHEMATICS AND COMPUTING↗

Big Ten MCNP6 Unstructured Mesh Benchmark

The purpose of this report is to document a benchmarking effort for the MCNP6 Unstructured Mesh (UM) geometry description method applied to a large and fairly complex critical assembly, Big Ten. A secondary goal of this effort is to provide a set of best practices for modeling a critical assembly with the MCNP UM method. Specification of geometry using UM requires creating a mesh from a solid geometry model and there are some important considerations when using this method for criticality problems. These considerations will be discussed along with suggested practices for correctly modeling these types of problems.

97 MATHEMATICS AND COMPUTING↗