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All sourcesNASA NTRSDOE OSTI
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 OSTI2021

Serpent and MCNP Calculations of the Energy Deposition in the Transformational Challenge Reactor

This paper focuses on the calculation of the energy deposition in the Transformational Challenge Reactor by two major Monte Carlo codes: Serpent and MCNP. The first software computation relies on Kinetic Energy Released per unit Mass (KERMA) factors while the second one relies on Q-values. The results from these two independent computation methodologies are in very good agreement; however, Serpent runs much faster than MCNP (for the same computational model) and allows for a detailed energy deposition distribution from a 1-mm-side square mesh with a relative statistical error between 0.5% and 1%. This detailed energy deposition is suitable for multiphysics analyses aimed at design optimizations. In order to calculate the energy deposition, Serpent needs enhanced ACE files (distributed by the software developers). Unlike other Monte Carlo software that uses inputs based on Python or Java languages, the Serpent input syntax is very similar to that of MCNP; a Python script can convert a MCNP input to a Serpent input in seconds. For simulations not requiring the calculation of the energy deposition, Serpent can also read nuclear data from MCNP ACE files, which eventually improves the comparison of the results of the two codes.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2012

A Preliminary Analysis of Dose Rates Associated with ITER CVCS Equipment/Area Location

A preliminary analysis of the ITER Chemical and Volume Control System (CVCS) Area was performed to assess dose rates outside the walls and ceiling of the facility after 1.5 years of operation at shutdown, 2 days, and 10 days after shutdown. For this purpose a simplified Monte Carlo computer model was developed using the MCNP (MCNP5 Ver. 1.51) code. Two components are included: the smaller filter tank and the larger ion exchanger. These pieces of equipment are associated with the Integrated Blanket ELM Divertor Primary Heat Transfer System, which will have the largest dose rates associated with activated corrosion products during operation in comparison with other systems. The ion exchanger contained two source regions, a 1.2-m-thick resin bed above a 0.55 m-thick skirt, and a 0.8-m-thick water region. The filter constituted an additional source. Thus the model consisted of three sources (filter, resin, water), homogeneously distributed within the appropriate source regions. However, much of the results (that address individual isotopes) are presented with the two sources in the ion exchanger combined. In these cases the sources are referred to as the 'ion exchanger source' and the 'filter source.' Dimensions for the facility and components, as well as source isotopes and strengths, and material densities, were supplied by US ITER. Because of its simplification, the model does not contain pipes. Consequently, radiation streaming through pipe penetrations, radiation emanating from the pipes, and shielding from the pipes were not considered in this analysis. Dose rates on the outside of two walls and the ceiling were calculated. The two walls are labeled as the 'long' wall (aligned with the X-axis) and the 'short' wall (aligned with the Y-axis). These walls and ceiling were nominally set to 30-cm-thick concrete. In the original analysis, standard concrete (2.3 g/cc density) was used. In addition to the shielding walls/ceiling, a floor and an additional wall opposite the long wall were added for photon scattering contributions. These were both 10-cm-thick, standard concrete structures. Other components (tanks, pipes, etc.), that were not included in the model, would potentially add additional scattering and shielding. Possibly these additional effects will be addressed in a later, more detailed analysis. The room was 29.6 m in length (X-axis), but was limited to 15 m in the model. The inside width (Y axis) and height (Z axis) were 4 m and 3.4 m, respectively. The origin for the model was located inside the room at the corner opposite the long wall and adjacent to the short wall at the floor level. The room was filled with air at standard temperature and pressure. The stainless steel (SS304) wall thicknesses for the ion exchanger and filter were 2.2 cm and 0.8 cm, respectively. The axial center of the filter was located 140 cm from the short wall and 100 cm from the long wall (outer surface). The axial center of the ion exchanger was located 440 cm from the short wall and 250 cm from the long wall (inner surface). The resin was assumed to be a homogeneous mixture of equal atom density fractions of hydrogen and carbon* at a specified density of 1.136 g/cc. The filter material was assumed to be homogeneous carbon at a specified density of 1.8 g/cc. If the filter media were stainless steel and the accumulated activity were the same, the dose rate outside the filter would be lower, provided the density of the stainless steel sintered filter material is significantly higher than 1.8g/cc. The densities of the water and air were assumed to be 1.0 g/cc and 1.096E-3 g/cc, respectively. The model included 10 small volumes placed outside the model adjacent to the wall at locations where the dose rates were expected to be highest and which showed to a degree the fall-off of the dose rate with distance along a particular wall or ceiling. These 'tally cells' are shown in Fig. 2 and are described in Table 1. Each cell had dimensions of 50 cm x 50 cm x 1 cm and was oriented so that the 1-cm-thick dimension was perpendicular to the wall against which the cell was located. Also, smaller tally volumes were placed at 5 cm (updated model), 30 cm, 50 cm, and 100 cm from each source, both radially from the outside of each component at the height of each source mid-plane, and vertically, along the axial centerline, above the ion-exchanger and below the filter. (For the ion exchanger, the vertical distance was limited to 75 cm because of interference from the ceiling.) These tally regions can be seen in Fig. 3. Each cell had dimensions of 5 cm x 5 cm x 1 cm, oriented so that the 1-cm-thick dimension was perpendicular to the X, Y or Z axis.

08 HYDROGEN↗
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 OSTI2019

Modeling of Pyroprocessing Hot Cell for Process Monitoring Evaluation

In this report, we examine studies undertaken to determine the applicability of applying measurements with a neutron counter within a pyroprocessing hot cell. The studies focus on the modeling of the hot cell and neutron counter in Monte Carlo N- Particle Code (MCNP) to determine expected radiation effects and applicability of neutron counters. The neutron counter of focus was the High Dose Neutron Detector (HDND) developed at Los Alamos National Laboratory (LANL). We studied the HDND to determine what would be the detector response under normal background conditions with operating process equipment as well as the background’s potential effect on counting applications involving a U/TRU ingot produced by the pyroprocessing electrorefiner. To perform this task, we were required to develop models of the pyroprocessing process cell and unit operations and generate source terms for various different times in the process. The data for the pyroprocessing materials came from SNL’s Safeguards and Security Process Model (SSPM) as part of MPACT’s advanced integration mission. This report will discuss the development of the pyroprocessing process cell and unit operations models in MCNP as well as various simulations to determine the applicability of the HDND for process monitoring efforts in the hot cell.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2015

Radiography benchmark 2014

The purpose of the 2014 WFNDEC RT benchmark study was to compare predictions of various models of radiographic techniques, in particular those that predict the contribution of scattered radiation. All calculations were carried out for homogenous materials and a mono-energetic X-ray point source in the energy range between 100 keV and 10 MeV. The calculations were to include the best physics approach available considering electron binding effects. Secondary effects like X-ray fluorescence and bremsstrahlung production were to be taken into account if possible. The problem to be considered had two parts. Part I examined the spectrum and the spatial distribution of radiation behind a single iron plate. Part II considered two equally sized plates, made of iron and aluminum respectively, only evaluating the spatial distribution. Here we present the results of above benchmark study, comparing them to MCNP as the assumed reference model. The possible origins of the observed deviations are discussed.

36 MATERIALS SCIENCE↗
DOE OSTI2012

IER-163 Post-Experiment MCNP Calculations (U)

IER-163 has been modeled with high fidelity in MCNP6. The model k eff was high, as in other similar calculations. The fission ratio 238 U(n,f)/ 235 U(n,f) was 12.6% too small compared with measurements; the ratio 239 Pu(n,f)/ 235 U(n,f) was 11.5% too small compared with measurements; the iridium ratio 193 Ir(n,n´)/ 191 Ir(n,γ) was 16.4% too large; and the gold ratios 197 Au(n,2n)/ 197 Au(n,γ), 197 Au(n,2n)/ 235 U(n,f), and 197 Au(n,γ)/ 235 U(n,f) were within one standard deviation of the measured values. It is suggested that the calculated 235 U fission rate is too large and the calculated 238 U fission rate is too small.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2017

Gamma-ray dose from an overhead plume

Standard plume models can underestimate the gamma-ray dose when most of the radioactive material is above the heads of the receptors. Typically, a model is used to calculate the air concentration at the height of the receptor, and the dose is calculated by multiplying the air concentration by a concentration-to-dose conversion factor. Models indicate that if the plume is emitted from a stack during stable atmospheric conditions, the lower edges of the plume may not reach the ground, in which case both the ground-level concentration and the dose are usually reported as zero. However, in such cases, the dose from overhead gamma-emitting radionuclides may be substantial. Such underestimates could impact decision making in emergency situations. The Monte Carlo N-Particle code, MCNP, was used to calculate the overhead shine dose and to compare with standard plume models. At long distances and during unstable atmospheric conditions, the MCNP results agree with the standard models. As a result, at short distances, where many models calculate zero, the true dose (as modeled by MCNP) can be estimated with simple equations.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2016

Implementation of a Portable HPGe for Field Contamination Assay

In this work, using MCNP to construct a detector model based initially on x-ray images of a portable high purity germanium (HPGe) detector followed by normalizing covering material values to also agree with check source responses, a validation of the model was attained. By calibrating the detector parameters using large count spectra, rigorous reproducibility is attained for high activity measurements but does not prevent deviations from normality in error distributions at the very low count events where spectral peaks are not always identifiable. The resulting model was created to allow operational assay of contamination over large areal distributions that could not otherwise be measured, such as the exhaust shaft at the Waste Isolation Pilot Plant (WIPP). Results indicate that contamination levels of activity in the exhaust shaft can be assayed to within a factor of 2. Lastly, detection limits are evaluated to be well below the contamination levels, which would constitute a legal environmental release if unfiltered ventilation of the underground facility were used.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2015

Radiation Background and Attenuation Model Validation and Development

This report describes the initial results of a study being conducted as part of the Urban Search Planning Tool project. The study is comparing the Urban Scene Simulator (USS), a one-dimensional (1D) radiation transport model developed at LLNL, with the three-dimensional (3D) radiation transport model from ORNL using the MCNP, SCALE/ORIGEN and SCALE/MAVRIC simulation codes. In this study, we have analyzed the differences between the two approaches at every step, from source term representation, to estimating flux and detector count rates at a fixed distance from a simple surface (slab), and at points throughout more complex 3D scenes.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

As-Run Neutronics Evaluation for the CSM-10584 Experiment in the ATR

This engineering calculations and analysis report (ECAR) documents the results of the Advanced Test Reactor (ATR) detailed Monte Carlo N-Particle (MCNP) code full-core model as-run physics analysis performed to support the Colorado School of Mines (CSM) experiment in the B-5 position.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2020

ISOCS-based Extended In Situ Gamma Spectrometry Services Tool SuperISOCS for Waste Measurements - 20275

Canberra's In Situ Object Counting System (ISOCS) is an established tool used for gamma spectrometry. It allows physical representation of complex geometries and mathematical calculation of the calibration function while avoiding the need for radioisotope standards. ISOCS provides a large range of templates to generate the geometry for most nuclear waste components in nuclear facilities. Nonetheless, there are some complex geometries where the accuracy attainable is limited by the conventional ISOCS templates. An ISOCS-based extended in-situ Gamma Spectrometry service tool has been developed, based on the so-called SuperISOCS (SISOCS) software. This tool was developed to generate the most complex geometries. The principle of geometry construction is based on the composition of 8 different primitive geometries. The primitive object is able to be cut and to change a part of an object. An object is able to overlap to another one to form a more complex object. These functions refine the modelled geometry compared to the real objects and decrease the measurement systematic error because they allow the mathematical model to represent the real-life geometry more faithfully through reducing the approximations and simplifications that need to be made The algorithm of SISOCS is the same as that of standard ISOCS. A set of calculations was done in comparison with MCNP efficiency calculation and SISOCS results. 80 different models were compared between SISOCS and MCNP, with added 30 cases compared between ISOCS and SISOCS. The average differences were less than 2%, for all energy's efficiency calculations. In this paper we illustrate the benefits that this new tool can provide, in terms of improved accuracy for complex realistic geometries, through simulation of some example scenarios. Furthermore, we demonstrate, through SISOCS modelling of the real object data test, the comparison between SISOCS to ISOCS calculations for the same geometry. This paper describes the added functions that are easily employed using SISOCS. Our results show the potential application and benefit of this technique for real waste assay projects and the way to integrate the tool into the waste assay systems. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2012

Final Report Forensics City Transport 2012

This project was to demonstrate practical Monte Carlo transport calculational methods applied to city models in prompt forensics. Typically 1 source gamma ray in ~10 18 is physically detected in most real world scenarios for this class of problems. Major factors are detector solid angles at ~ km distances, building and air attenuation, as well as huge variations in source intensities. We used the world-wide standard for transport calculations, MCNP, developed at LANL. We used ARA supplied city models used in consequence management. One indication of the extreme difficulty of these problems is that the huge numbers were larger than the space allotted by the code developers and were output as “*********”. (LANL was very helpful in resolving multiple issues we encountered like this.) Hardware memory issues also needed to be resolved. Calculational success was achieved in the simpler WDC model by a combination of variance reduction, voxelized geometry eliminated the mcnp geometry errors in the NYC model, eliminating peripheral buildings, and convolution of the source terms.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2023

MTA Secondary Beamline Production and Optics Optimization

A new secondary beamline was recently installed in the MeV Test Area (MTA) with the objective of enhancing mu+/mu- production by factors of 3/8 by using a tungsten target versus the conventional graphite production target using the 400 MeV Fermilab proton Linac beam. Ultra-low energy muon beams can support world-class physics experiments for fundamental muon measurements, sensitive searches for symmetry violation, and precision tests of theory. Extensive production studies have confirmed higher muon yield from heavy targets (tungsten vs carbon), but also, surprisingly, showed significant differences in production fluence and energy spectrum between modern hadronic models (GENIEhad) and between GEANT and MCNP. Studies are also underway towards a high-efficiency source of muonium by stopping the mu+ beam in superfluid helium. Muon fluxes can be measured at MTA including using higher-Z and novel target geometries to test hadronic and production models which are critical to understanding ongoing experiments (Mu2e) and will significantly impact the planning of future HEP experiments and planning PIP II facilities. The MTA beamline will additionally support a broad user muon test beam for future experiments and R&D.

Ahmed, Shiza↗
DOE OSTI2024

Simulating gas-filled neutron detector responses with DRiFT

– Gas-filled neutron detectors have numerous applications across the nuclear engineering and nuclear physics fields. The ability to accurately model and simulate these detectors is important for those applications but is currently limited by the lack of readily-useable detector response software. Recently, the capabilities of DRiFT, a Detector Response Function Toolkit, were expanded to model gas-filled, He-3 and BF3, neutron detectors so that, combined with the radiation transport capabilities of the MCNP code, a high-fidelity treatment of gas-filled neutron detectors can be obtained. Further, this model has been validated by an experiment carried out with the Epithermal Neutron Multiplicity Counter and its capabilities have been demonstrated in two additional experiments. This work shows that utilizing DRiFT to post-process MCNP outputs produces more accurate results than using the MCNP code alone, reducing the difference between experimental and simulated results for measurements taken near the end of a He-3 tube, where the MCNP code struggles to model inactive regions of the detector, from a maximum of 35% with the MCNP code alone to 15% with the MCNP code plus DRiFT. DRiFT's diagnostic capabilities are also demonstrated with measurements for scenarios when pulse pileup or room return effects are significant and must be considered. Altogether, these measurements underpin the ability of DRiFT to accurately model and predict the behavior of gas-filled neutron detectors, making it a valuable tool for the design and testing of systems and experiments that utilize these detectors.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

Radiation Source Localization Using Surrogate Models Constructed from 3-D Monte Carlo Transport Physics Simulations

Recent research has focused on the development of surrogate models for radiation source localization in a simulated urban domain. We employ the Monte Carlo N-Particle (MCNP) code to provide high- delity simulations of radiation transport within an urban domain. The model is constructed to employ a source location (x, y, z) as input and return the estimated count rate for a set of speci ed detector locations. Because MCNP simulations are computationally expensive, we develop e cient and accurate surrogate models of the detector responses. We construct surrogate models using Gaussian processes (GP) and neural networks (NN) that we train and verify using the MCNP simulations. The trained surrogate models provide an e cient framework for Bayesian inference and experimental design. We employ Delayed Rejection Adaptive Metropolis (DRAM), a Markov Chain Monte Carlo (MCMC) algorithm, to infer the location and intensity of an unknown source. The DRAM results yield a posterior probability distribution for the source's location conditioned on the observed detector count rates. The posterior distribution exhibits regions of high and low probability within the simulated environment identifying potential source locations. In this manner, we can quantify the source location to within at least one of these regions of high probability in the considered cases. Employing these methods, we are able to reduce the space of potential source locations by at least 60%.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2017

Some Notes on Neutron Up-Scattering and the Doppler-Broadening of High-Z Scattering Resonances

When neutrons are scattered by target nuclei at elevated temperatures, it is entirely possible that the neutron will actually gain energy (i.e., up-scatter) from the interaction. This phenomenon is in addition to the more usual case of the neutron losing energy (i.e., down-scatter). Furthermore, the motion of the target nuclei can also cause extended neutron down-scattering, i.e., the neutrons can and do scatter to energies lower than predicted by the simple asymptotic models. In recent years, more attention has been given to temperature-dependent scattering cross sections for materials in neutron multiplying systems. This has led to the inclusion of neutron up-scatter in deterministic codes like Partisn and to free gas scattering models for material temperature effects in Monte Carlo codes like MCNP and cross section processing codes like NJOY. The free gas scattering models have the effect of Doppler Broadening the scattering cross section output spectra in energy and angle. The current state of Doppler-Broadening numerical techniques used at Los Alamos for scattering resonances will be reviewed, and suggestions will be made for further developments. The focus will be on the free gas scattering models currently in use and the development of new models to include high-Z resonance scattering effects. These models change the neutron up-scattering behavior.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗