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All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Verification of the DIF3D Software to Support Fast Reactor Analysis (Rev. 3)

Ongoing design activities at Argonne National Laboratory are requiring a thorough verification of the Argonne Reactor Computation codes be performed. DIF3D is central to this system. The driver for this effort requires the 3D Cartesian, triangular-Z, and hexagonal-Z core geometry options of DIF3D be verified. Previous work identified the DIF3D features required to be verified to support current design activities, features of which are generally applicable to hexagonal-Z fast reactor designs. The scope of this verification effort includes verifying DIF3D’s ability to correctly translate the user’s model in to DIF3D’s preferred format, verifying that options planned for use have the desired effect, and verifying the correctness of the eigenvalue, fixed-source, forward, and adjoint solvers in DIF3D-FD and DIF3D-VARIANT. This manuscript provides the verification tasks and their results with respect to the features needed for current design activities. Since analytic solutions of the neutron diffusion and transport equations are either limited in scope or not possible, multiple tiers of problems unique to each solver and geometry type were implemented. Each of these tiers tests features independent and complementary arguments for why the separate testing of functionalities is acceptable. Finally, this separate testing was also supplemented with a high-level integral check of each the diffusion and transport capabilities and applicable geometries. To accommodate cases which an analytic solution is not feasible, MCNP6.2 was relied upon to provide a higher-order reference solution. This therefore required that the capabilities within MCNP6.2 which were relied upon for this work are also verified in this work. No MCNP discrepancies were noted in this effort. Note that the MCNP6.2 verification included in this work does not stand as a full verification of MCNP6.2, but merely verifies the features used in verifying DIF3D. The verification effort identified no issues that are debilitating or otherwise impactful to design usage of DIF3D, and thus DIF3D version 11.0, release 3012 is considered verified. As some additional changes have been made to the ARC software since this point all versions between release 3012 and 3266 can be considered verified as version 3253 was used for all updates in this revision. The types of issues that were identified were predominantly in the areas of: unclear documentation, software bugs which were inconsequential to final results, editing options which were ignored in favor of printing more information than requested, bugs in the outputs of intermediate results, or secondary output binary file information which was not present. While not a bug, this verification report also identified that the algorithm used to evaluate the peak fast flux in a nodal transport solution can be quite unreliable due to the methodology used and the location of the peak within the mesh. The authors of the report therefore recommend the usage of the EvaluateFlux software (distributed with ARC) as a more robust alternative noting that DIF3D will properly notify the user when the peaking values it is providing are potentially incorrect.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2023

Athena-I CUBIT Journal Files

The Monte Carlo N-Particle (MCNP)1 transport code version 6 (also known as MCNP6) has the capability for tracking particles on unstructured mesh (UM) geometry models embedded into con structive solid geometry (CSG) cells. This feature has been developed for performing calculations of complex geometry models because manually creating CSG models is a time-consuming and error prone process as the complexities of geometries increase. The UM capability was originally designed to work with UM models created with the Abaqus software and ASCII input files that it generates. The MCNP code version 6.0 and later can process UM models formatted as Abaqus input files. Starting with version 6.3, MCNP can also process HDF5 mesh input files. External codes must be used to generate Abaqus input files for MCNP UM calculations. 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 exported from CUBIT cannot be used for MCNP simulations because it lacks the proper syntax. A Python script was developed to convert an Abaqus file created by CUBIT to an Abaqus file format that MCNP can process. Creating UM models for complex geometries is not an easy task. The process of creating UM models in CUBIT for MCNP simulations is detailed in. CUBIT provides several user interface options including a graphical user interface (GUI) and a command line interface. A GUI provides an easy way to use CUBIT without learning the CUBIT command syntax. When using CUBIT with either interface option, command lines are written into an ASCII file known as a journal file; this journal file can be edited and archived so that it can be played back in CUBIT to automatically generate a UM model. This report describes the CUBIT journal files of the UM models developed for Athena-I. The Athena platform, an energy-tuning assembly, was developed to spectrally shape the National Ignition Facility (NIF) deuterium-tritium fusion neutron source to a thermonuclear (fusion) plus prompt fission neutron spectrum with capability to act as a short-pulse neutron source. MCNP6 was used for the Athena experiment design analysis.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

Earth Filter Mathematica Toolbox V4 User’s Guide: Simplifying, Visualizing and Quick Modeling of All Fission Products from 60 Nuclear Sources

The Earth Filter Mathematica toolbox is a set of user-friendly routines that accurately calculate the ingrowth and decay of any of the 908 fission products for an infinite combination of sources over a large dynamic range and large time scales. This toolbox was developed by Dr. W. P. Brug at Los Alamos National Laboratory and eliminates the need for the user to define and solve differential equations by automatically scanning the ENDF-349 dataset and creating a complete set of differential equations (including delayed neutron branches to both the ground and metastable states) for every fission product. Additionally, this code allows the user to choose the data source for half-lives from several key references (i.e., TORI, IAEA, ENDF or NUDAT.) The complete set of ENDF-349 independent and cumulative fission yields for all 60 isotopic targets and 4 different neutron spectrums are included. This allows nearly any nuclear fission source to be accurately modeled as a linear combination of endpoint sources. The Earth Filter Toolbox has no limit as to the length of split list, and split lists of 5 or more endpoint sources can be quite common.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

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↗
DOE OSTI2023

PMT-004 Validation Testing for Thermal Scattering Laws and Some Interesting Temperature-Dependent Implications [Slides]

This presentation finds that PMT-004 has four new benchmark cases highly sensitive to PE TSL (2 cases) and PMMA TSL (2 Cases). Additionally discovered was that the PE cases were well predicted using MCNP6.2 and ENDF/B-VIII.0. PMMA cases overpredicted by approximately 0.6-0.7% in k eff likely due to the lack of integral benchmarks sensitive to PMMA. The NCSU ENDF/B-VIII.1 PMMA TSL is an improvement over VIII.0. This was Accepted at last ICSBEP meeting pending comment resolution, should be published in 2024 version of the Handbook. It was found that temperature had a large impact on reactivity of the critical configuration. This has Implications for validation work for thermal cases- need to adjust TSL data to correct temperature as it can have hundreds of pcm effects for a few °C. Future thermal experiments should try and measure reactivity at multiple temperatures to add in data testing and benchmark adjustment.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

Proposed Updates to Pu Evaluation for ENDF/B-VIII.0β 2 [Slides]

This presentation shows current status of in ENDF/B-VIII.0 and ENDF/B-VIII.0β 1 Pu evaluations. With a focus on the Pu thermal solutions (PST) and sanity check on fast metal assemblies (PMF). Pu ENDFβ 1 file in the low-energy region included updates in RRR (up to 2.5 keV), TNC and PFNS. Discussed are improvements over ENDF/B-VIII.0 in the C/E benchmarks (PST 81 cases) with preliminary ν p and cross section covariance generation for the resolved resonance energy range up to 2.25 keV. Highlighted are proposed updates for ENDFβ 2 with an extension of the RRR evaluation up to 5 keV, inclusion of Mosby’s data including proper resolution function (thanks to Marian J.), and a particular focus on η energy dependence from thermal up to 10 eV with the impact of the capture-to-fission ratio at 0.29 eV. This presentation concludes with an updated ν p and cross section covariance generation for the resolved resonance energy range up to 5 keV. And additional validation to test performance at about 2 eV and in the keV region.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

On Integral Benchmarks for Resolving TSL Conflicts for ENDF/B-VIII.1 Release [Slides]

This presentation touches on polystyrene ORNL evaluation in TSL review process for ENDF/B-VIII.1. the presentation found excellent agreement with INS and transmission measurements. Additionally, PS and PE evaluations with different phonon spectra, similar total xs., calculate keff similarly. This presentation found for critical benchmarks. While extremely useful for validation of nuclear data at all energies, critical benchmarks are not the best tool to provide a definitive answer on conflicting TSLs. In conclusion this presentation found INS and transmission measurements need to be the basis of validation of TSLs.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

ORNL’s Continuing Efforts in Evaluating and Validating TSLs [Slides]

This presentation finds that INS measurements are not as variable based on sample composition, because phonon spectra is a properly of the bulk material. Additionally, INS and transmission measurements are and should be the most significant part of TSL validation, as they are the fundamental quantities used by Monte Carlo neutronics codes. This presentation reasons that relaying solely on theoretical atomistic calculations for TSL evaluation is not enough, they are highly dependent on inter-atomic potentials (which are not the most accurate depending on the quantity trying to be reproduced) and evaluators knowledge and understanding of the material being studied. Finally on Critical benchmarks, while extremely useful for validation of nuclear data at all energies, critical benchmarks are not the best tool to provide a definitive answer on conflicting TSLs. The presentation concludes that INS and transmission measurements need to be the basis of validation of TSLs.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Impact of Thermal Scattering Law on Similarity Assessment in Light-Water or Polyethylene-Moderated Systems [Slides]

This presentation found validation efforts revealed noticeable differences in c k for light-water and polyethylene moderated systems when compared to PST-002-001. Additionally, although 1 H-H 2 O and 1 H-poly use the same cross section and covariance data, the treatment of TSLs in SCALE lead to no contribution to ck between polyethylene application and benchmark experiment. When H 2 O contribution were removed the difference in c k appears to come from 239 Pu chi. Sensitivity profiles and data-induced uncertainty confirmed that the polyethylene application was more closely resembling the benchmark experiment. Specific differences between similar systems can be accounted for and examined to help understand differences in similarity (c k ) between systems was a conclusion reached by this presentation.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

Fission Product Yields: SCALE/ORNL Perspective [Slides]

This presentation covers a few key points. First and foremost, that ORIGEN is a consumer of FPY data. Secondly, TRITON and POLARIS are assets which can be used to validate new FPY data, e.g., from existing radiochemical benchmarks and new irradiation experiments. Lastly, this presentation finds that new capabilities with covariance data should be developed, and V and V methods and analysis are also needed for the covariance data.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

ENDF/B-VIII.1 and AMPX [Slides]

This presentation is on ENDF/B-VIII.1 and AMPX. This presentation begins with a brief introduction to the AMPX nuclear data processing code. The lecture then touches on nuclear data libraries that ship with the SCALE code system. Finally, this lecture concludes on preparations for the ENDF/B-VIII.1 libraries.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

Nuclear Data Evaluations in the Resolved Resonance Region and Proposed Updates to R­matrix Modeling in the SAMMY Code [Slides]

This detailed presentation starts off with an evaluation workflow scheme along with observables definition. Provided next are typical cases of explicit experimental effects and the basic quantities for reproducibility. Lastly the presentation mentions the Inverse channel, Complex radius, and the level­ dependent boundary condition. A visual workflow scheme is provided.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

Recent MCNP® Code Developments and Improvements for Nuclear Energy Applications [Slides]

The MCNP® code is a general-purpose radiation transport code developed at LANL over the past 46+ years. This code is capable of modeling the fundamental physics of particles as they move through and interact with materials. It is used in many LANL and worldwide radiation transport applications (see next slide) for example in the Fundamental nuclear physics and data experiments at LANSCE.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

The Nuclear System-of-Systems Capabilities Analytic Process

This dissertation discusses the impetus for, development of, and initial demonstration of NuSCAPTM: the Nuclear System-of-Systems Capabilities Analytic Process TM . NuSCAP is an approach executed via a Python® application that enables capabilities-based vulnerability analyses of military systems of systems (SOS) exposed to prompt nuclear weapon effects. The NuSCAP application calls on industry-standard, fast-running nuclear weapon effects tools and the Monte Carlo N-Particle®1 (MCNP®) code to evaluate the impact of nuclear weapon environments on the military capabilities of a complex and networked SOS.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2023

Evolution of Two-Phase High Explosive Reactive Flow [Slides]

Reactive hydrodynamics involve rapid conversion of reactants to products along a detonation wave and needs a closure rule: P-T equilibrium. To calculate P-T equilibrium, we use a root finding method, which can be a computational bottleneck. We evolve the guess for the root finder rather than using the previous value to reduce iterations. Evolving products volume fraction Φ p for an initial root finding guess does reduce root finding iterations and seems to give the best results immediately following a detonation wave (reducing root finding iterations from 5 to 2).

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Athena-I Modeling with MCNP6.3 [Poster]

This objective of this project is to develop a model of the Athena-I experiment by creating unstructured mesh (UM) geometries in CUBIT and to perform calculations of the models in Monte Carlo N-Particle (MCNP) transport code version 6.3 (MCNP6.3).

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

Oktavian Modeling and Analysis with MCNP6.3 [Poster]

Project Goals: Apply MCNP6 variance reduction (VR) techniques to improve the Oktavian benchmark experiment calculations; Verify VR methods and electron transport with MCNP6.3 unstructured mesh (UM) geometry. All MCNP calculations in this poster are for mode n, p, e.

42 ENGINEERING↗
DOE OSTI2023

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↗