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MCNP6.3 Unstructured Mesh Verification: GodivR and CANDU Models

A geometric cell of the Monte Carlo N-Particle (MCNP)1 transport code is traditionally created by using Boolean operators on defined surfaces. This constructive solid geometry (CSG) capability has been available in the MCNP code since its beginning. However, a CSG model approach is limited when it comes to constructing a representative geometry for a complex model in its ability to capture a correct model representation. Starting with the version 6.0, the MCNP code has the ability of embedding an unstructured mesh (UM) model into a CSG cell to create a hybrid geometry [1]. The MCNP UM feature provides the flexibility of defining very complex geometries because computer aided design (CAD) and mesh generation software packages can be utilized to construct UM models for MCNP simulations.

97 MATHEMATICS AND COMPUTING↗

Multigroup Cross-section Generation in MCNP6.3 [Slides]

This presentation states that in comparison to the NJOY-produced multigroup cross sections, the MCNP-produced multigroup cross sections are generally consistent. Statistical uncertainties, however, are challenging and the unresolved resonance region may be looked at in the future. It also discusses how the SPM and LCS options were compared to each other for internal consistency. Additionally, some reactor pin-cell-like problems were used to compare to multigroup capabilities in other Monte Carlo codes (e.g., Serpent, OpenMC).

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

Improvements in MCNP6.3 (Rev. 1) [Slides]

This presentation outlines improvements in the Monte Carlo N-Particle Code (MCNP) version 6.3. MCNP is an internationally renowned Monte Carlo particle transport code developed by Los Alamos National Laboratory.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Generating MCNP Input Files for Unstructured Mesh Geometries

The Los Alamos National Laboratory’s (LANL) Monte Carlo N-Particle (MCNP)1 transport code version 6.3 (also known as MCNP6.3) has the capability for tracking particles on unstructured mesh (UM) geometry models embedded into constructive solid geometry (CSG) cells. This feature has been developed for performing calculations of complex geometry models because manually creating CSG models is time-consuming and error-prone as the complexities of geometries increase. A UM geometry model is a collection of finite elements representing a solid geometry. The first step of the MCNP UM calculation is using other software packages to create a finite element mesh representation of a solid 3D geometry because the MCNP code cannot be used to generate a UM model. Computer-aided design (CAD) software is typically used to create a solid geometry model, which is later imported into mesh generation software to create a UM model. Some mesh generation software packages may also be used to create solid geometries and thus CAD files are not needed. The MCNP UM feature was originally designed for models generated by the Abaqus/CAE software suite. The MCNP code version 6.0 and later can process UM models formatted as Abaqus input files. Starting with a 6.3 version, the MCNP code can process HDF5 mesh input files. We only focus on the UM models formatted as Abaqus input files in this report since currently no external software can be used to generate HDF5 mesh input files for MCNP UM calculations. The MCNP code version 6.3 can be used to convert the Abaqus mesh input files into the HDF5 mesh input files, but this option is typically used by the MCNP code development team to test the HDF5 mesh input file feature. Several software packages (such as Abaqus, Attila4MC, or Cubit) can be used to create the Abaqus input files for MCNP UM calculations. An MCNP UM calculation using an Abaqus model requires two input file types: MCNP and Abaqus input files. The Abaqus input files needed for MCNP UM calcu lations must have the correct Abaqus syntax and meet the additional requirements by the MCNP code. The MCNP code can process only Abaqus input files that make use of part and assembly definitions, where elements in each part must be grouped into one or more element sets (i.e., elset) using *Elset keyword lines with specified naming formats. The MCNP and Abaqus input files required for MCNP UM simulations must be related; pseudo-cells in an MCNP input file must be constructed from mesh model data from an Abaqus input file. For large complex UM models, it is tedious to manually create MCNP UM input files. The um pre op (unstructured mesh pre operations) program with the -m option can be used to create a skeleton MCNP input file from an Abaqus input file [6]. Since the um pre op program was written in Fortran and was not written for optimized performance, this program is a deprecated feature in the MCNP code version 6.3 and may be removed in the next release of the code. To improve calculation flow of multiphysics calculations, a Python3 code called write mcnp um input has been developed to generate an MCNP input file instead of using the um_pre_op -m option. This Python code was initially released to the public in 2020. We have updated this Python code for MCNP6.3 and it was used to generate the MCNP input files used to verify the MCNP6.3 code. The write_mcnp_um_input code is included with the MCNP6.3 code package which will be released to the public through the Radiation Safety Information Computational Center (RSICC) at Oak Ridge National Laboratory. This report is a revision of LA-UR-20-27139 report.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

FY21 MCNP ® Updates for the Nuclear Criticality Safety Program [Slides]

LANL has devoted a substantial amount effort toward the MCNP6.3 release over the past year by improving many of their processes, infrastructure, and capabilities. Many new resources in support of the MCNP code and DOE NCSP applications are coming online now. LANL is excited to receive feedback on all of the new and improved capabilities that will be packaged with MCNP6.3.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

MCNP6 Developments: A 2022-23 Year in Review [Slides]

A separate MCNP6.3 V&V document reports on all the default calculations for all test suites is touched on. This report does not include the subcritical multiplication benchmark suite. After some additional clean-up and finalizing the post-processing and documentation steps, the subcritical multiplication benchmark suite will be released in the next version of our vnvstats repository. We tested the new HDF5 PTRAC feature in MCNP6.3 and found encouraging outcomes. Identical results coming out of the simulation with respect to the legacy PTRAC results. The overall runtime for all simulations is reduced by ~20% with the new HDF5 PTRAC capability. We consider giving the new HDF5 PTRAC features a try and using it for all subcritical multiplication and any other relevant (PTRAC) calculations.

97 MATHEMATICS AND COMPUTING↗

Fixed Source Sensitivity Calculations for Inertial Confinement Fusion Applications

A numerical code library was developed for the radiation transport code MCNP6.3 to calculate generalized response sensitivity coefficients for fixed source neutron transport problems with applications to inertial confinement fusion (ICF) experiments. The new MCNP6.3 dependency is used to generate a novel time convolution response that represents a neutron time-of-flight (nToF) signal. The traditional suite of macroscopic cross-section sensitivities and constrained fixed source probability distribution sensitivities are available for both the standard and the new response tallies in this library. However, novel sensitivity coefficients for the constrained hyperparameters of analytic fixed source probability distributions are emphasized in this work for their connection to ICF neutron transport models. Particularly, advanced Monte Carlo methods are developed for calculating the sensitivity of a nToF signal to perturbations in an ICF plasma’s ion temperature and burn history as well as perturbations in the target liner mass density and the shape parameters of the nToF detector’s impulse response function. Together, these capabilities form an advanced suite of computational tools that can be used to analyze and extract information from any ICF experimental platform.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Demonstration of TOFFEE: A Response Uncertainty Quantification Tool

A key characteristic in neutron transport is nuclear data. Cross-section uncertainty is not used in MCNP6.3 to propagate response uncertainty without external analysis. Here, the TOol For Fast Error Estimation (TOFFEE) is a Python-based code developed to automate the propagation of cross-section uncertainty for MCNP evaluations. TOFFEE implements the sandwich rule to calculate the uncertainty from cross sections with sensitivity coefficients from MCNP6.3 and ENDF/B covariance data. In this paper, TOFFEE has been tested with benchmark experiments, and it has been compared to the uncertainty quantification capabilities of Sampler and TSUNAMI, within SCALE, to verify the application’s capabilities.

97 MATHEMATICS AND COMPUTING↗

Improved Verification and Validation Testing and Tools [Slides]

All MCNP team supported V&V test suites are now developed in a separate repository from the MCNP source code within a Python-based framework. This entire framework will be distributed with the upcoming MCNP6.3 release. Most V&V test suites distributed with MCNP6.2 will be distributed in new framework. New V&V test suites are done or being worked on for the MCNP6.3 release.

97 MATHEMATICS AND COMPUTING↗