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The MCNP unstructured mesh (UM) feature was implemented to allow MCNP to read in an Abaqus mesh geometry file, exported by external software packages such as Abaqus and CUBIT.
The Monte Carlo N-Particle (MCNP) transport code developed by Los Alamos National Laboratory (LANL) can be used to transport various particles across user defined three dimensional (3D) geometries. Traditionally, these geometries are created as constructive solid geometry (CSG), involving the use of Boolean operators on defined surfaces to create 3D regions known as cells. A newer method of geometry definition in the MCNP code is unstructured mesh (UM) embedded within a CSG cell using the universe and fill repeated-structure features. An MCNP UM calculation requires an MCNP input file and mesh geometry file. The MCNP code cannot be used to generate UM geometry models. A computer-aided design (CAD) software is typically used to construct a solid geometry model, and a CAD file is then imported into a mesh generation software to prepare and generate a mesh model. Some mesh generation software packages have the ability to create a solid geometry and thus a separate CAD software for creating a CAD model is not needed. In this work, Cubit, a geometry creation and meshing software developed by Sandia National Laboratories, is used to construct UM models for MCNP simulations.
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The Monte Carlo N-Particle (MCNP) transport code version 6 (also known as MCNP6) 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 creating CSG models is a time-consuming and error-prone process as the complexities of geometries increase. An MCNP UM calculation requires UM geometry input files. The UM capability was originally designed to work with UM models created with the Abaqus/CAE software and ASCII input files that it generates. The Abaqus-formatted input files needed for MCNP UM calculations must have the correct Abaqus syntax and meet the additional MCNP requirements. Several software packages can generate a UM model formatted as an Abaqus input file. Cubit, the Sandia National Laboratory automated mesh generation toolkit, can generate a UM model formatted as an Abaqus input file. However, the Abaqus input files created by Cubit cannot be used for MCNP simulations. A Python script has been developed to convert an Abaqus file created by Cubit to an Abaqus file that the MCNP code can process. This report describes the process of using Cubit to create UM models for MCNP calculations.
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This report describes the verification performed to ensure that a new HDF5-based MCNP® unstructured mesh (UM) [1] file correctly represents elemental values on the associated mesh geometry.
Los Alamos National Laboratory's (LANL) Monte Carlo N-Particle (MCNP) transport code version 6 has the capability for tracking particles on unstructured mesh (UM) geometry models. The MCNP UM feature has been developed for performing calculations of complex geometry models. This capability tracks particles on hybrid geometries where finite element meshes are embedded into constructive solid geometry (CSG) cells. The MCNP UM feature was originally designed to read UM models created by Abaqus/CAE software suite. MCNP versions 6.2.0 and later can process UM models read from Abaqus input les or MCNPUM les converted from Abaqus input les. Sandia National Laboratory's Cubit Toolkit and other finite element analysis software packages may generate UM models and then convert these models into Abaqus input formats.
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.
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This report describes a variety of metrics calculated by the MCNP code for a user to assess the quality of an input unstructured mesh (UM). These UM quality metrics are calculated automatically, but the user has the opportunity to opt-out of doing so. At present, only linear tetrahedral elements and linear hexahedral elements have extensive quality metrics calculated and reported.
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This report details the results for an optimization of the dimensions of the moderators in the preliminary design of the Spallation Neutron Source Second Target Station (STS). This study uses the optimization algorithms of Dakota and an unstructured mesh model for the moderators in MCNP. More details on the unstructured mesh model and the automated mesh generation can be found in [3]. Parallel to this effort, the same moderator geometries have been optimized using a constructive solid geometry (CSG) MCNP model. More details on this model and its results can be found in [4]. Three optimal designs are selected for each moderator: one that is optimized for maximum peak brightness, one for maximum time-integrated brightness, and one for a combination of peak and time-integrated brightness. The backbone of the optimization work flow is provided by Dakota. For each set of design parameters requested by Dakota, a new solid geometry is automatically built in Creo and SpaceClaim, and subsequently exported to Attila4MC to generate an unstructured mesh geometry for MCNP. After the MCNP calculation is finished, the objective function (e.g., brightness metric) is returned to Dakota. After the new design has been evaluated, a result-file is written, and Dakota proposes the next set of design parameters to be evaluated. The loop continues until a specified convergence criterion has been met. The design parameters of the cylindrical (upper) moderator include the hydrogen radius, the premoderator thickness (top, bottom, radial), the beryllium radius and the horizontal position of the moderator. The crucial design choice is the hydrogen radius. A radius of 62 mm is shown to provide the maximum time-integrated brightness. The maximum peak brightness occurs with a radius of 40 mm. A combined (middle) design, which balances peak and time-integrated brightnesses, is obtained with a hydrogen radius of 50 mm. The premoderator thicknesses and the beryllium radius are slightly larger in the design optimized for time-integrated brightness than in the design optimized for peak brightness. The sensitivity to these two parameters is relatively small close to the optimal configurations. The hydrogen vessel and vacuum vessel wall thicknesses are dependent on the radius of the liquid hydrogen due to structural integrity requirements. The increased wall thicknesses for larger vessels significantly penalize the time-integrated brightness, with the maximum obtainable value reduced by more than 10% relative to earlier studies which used fixed vessel wall thicknesses. The impact of the variable wall thicknesses is much less for the peak brightness and combined brightness designs. The design parameters of the tube (lower) moderator selected for the optimization are the tube length, the annular premoderator thickness, the beryllium radius and the horizontal position of the moderator. The tube length is the crucial parameter and is chosen large (210 mm) and small (125 mm) in the designs optimized for time-integrated and peak brightness respectively. A combined optimal design has a tube length of 170 mm. The premoderator thickness and the beryllium radius are chosen larger in the design optimized for time-integrated brightness.
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This presentation covers the activation, depletion, and activation source synthesis capabilities of the Attila deterministic solver using the Attila GUI.
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