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All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Modeling Radiation Sources for Experimental Validation

To build on previously completed experimental work, I have been working to implement a model of our lab’s Americium-Beryllium neutron source and Ludlum detection instruments computationally to provide secondary verification of our experimental results. This has been done using the radiation transport code MCNP6 (Monte Carlo N-Particle). I have developed files to calculate the mass ratio of various composite materials that we have exposed to the radiation source, as well as the components of the detection instruments and converted all of these into geometric coordinates that define the experimental setup. Identifying the proper energy distribution and detector efficiencies as well as the correct interpretation of the detector physics have proven to be the most difficult and time-consuming challenges of the work. It is currently too early for results based on the most updated and accurate models. With this experience, the next step is to develop another source model of a proton beam to simulate exposures on other composite targets. This will be developed based on existing experimental datasets, but not used to validate them. Each of these projects will further define the radiation shielding needs for humans in space radiation environments that astronauts will someday be exposed to beyond Low Earth Orbit.

Juliana Simon↗
DOE OSTI2023

Design of Nuclear Criticality Safety Framework for Hands-on Construction of Fast Systems Over Ranges of Multiplication

Since 1945, Los Alamos National Laboratory (LANL) has performed critical experiments, primarily at the Los Alamos Critical Experiments Facility (LACEF) at Technical Area 18 (TA-18). Since 2011, these experiments have been conducted by the National Criticality Experiments Research Center (NCERC), operated by LANL, at the Device Assembly Facility (DAF) in the Nevada National Security Site (NNSS). These experiments utilize various types of Special Nuclear Material (SNM). Some of these experiments utilize what is referred to as the Rocky Flat Shells, which are called such as they came from the Rocky Flats Plant. These concentric hemi-shells are made of Highly Enriched Uranium (HEU), which is 93 w/o 235U. Fig. 1. Subset of the Rocky Flat Shells LANL is designing a new subcritical hands-on experiment with the goal of achieving a neutron multiplication in the range of 50 to 200, which correlates to Keff values of 0.98 to 0.995. ANSI/ANS-1 is the standard for Conduct of Critical Experiments which governs critical operations at NCERC. Section 3.9 of ANSI/ANS-1 states that when manipulating a critical assembly by hand, the predicted k eff of a known configuration should not exceed 0.95 (a neutron multiplication of 20). This presents a challenge, as this system would have a higher multiplication than 20 and the assembly would have to remain subcritical in normal and credible accident scenarios. To ensure the safety of such an assembly, the different normal and abnormal conditions that could alter the criticality 1 MCNP® and Monte Carlo N-Particle® are registered trademarks owned by Triad National Security, LLC, manager and operator of Los Alamos National Laboratory. Any third party use of such registered marks should be properly of the system must be considered and analyzed. If such a condition is deemed to be credible, it will be further investigated using the Monte Carlo N-Particle (MCNP) transport code. If the results of these simulations show that a k eff larger than one could be possible, modifications to the assembly will be made until such an event is deemed no longer possible.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

MontePy: a Python library for reading, editing, and writing MCNP input files.

The Monte Carlo N-Particle (MCNP) radiation transport code is a highly capable and accurate code with a long legacy. MCNP uses the Monte Carlo simulation process to simulate the path of particles (e.g., neutrons, photons, charged particles, etc.), and their interaction with materials. It is widely used in nuclear engineering, high-energy physics, and other fields. Its origins in the mid-twentieth century predate many modern software conventions. MCNP users provide an input file to MCNP, which it then uses to create an internal representation of the simulation problem. These input files originally had to be stored as punchcard decks, and the user manual still uses the terminology of cards and decks, despite moving beyond punchcards. MCNP predates nearly all modern human readable markup or data serialization languages, such as the extensible Markup Language (XML), the Standard Generalized Markup Language (SGML), YAML (YAML Ain’t Markup Language), and Javascript Object Notation (JSON). Due to this, MCNP uses an entirely custom defined syntax language for its input, making off-the-shelf libraries for XML, YAML, and JSON impossible to use for scripting various operations on MCNP input files (Kulesza et al., 2022).

97 - MATHEMATICS AND COMPUTING↗
DOE OSTI2024

MCNP ® Code Version 6.3.1 Theory & User Manual

This document acts as a repository of knowledge for the Monte Carlo N-Particle (MCNP) transport computer code. It is maintained alongside the source code and attempts to introduce new users and re-familiarize experienced users with the theory and practices of using the MCNP code for the wide range of particle transport analyses that it is appropriate for. The latest version of the MCNP code, version 6.3.1, provides the Monte Carlo particle transport community with the latest feature developments and bug fixes in the MCNP code. The MCNP code version 6.0 and later is also known as the MCNP6 code.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2026

MCNP® Code Version 6.3.2 Theory & User Manual (Rev. 1)

This document acts as a repository of knowledge for the Monte Carlo N-Particle (MCNP) transport computer code. It is maintained alongside the source code and attempts to introduce new users and re-familiarize experienced users with the theory and practices of using the MCNP code for the wide range of particle transport analyses that it is appropriate for. The latest version of the MCNP code, version 6.3.2, provides the Monte Carlo particle transport community with the latest feature developments and bug fixes in the MCNP code. The MCNP code version 6.0 and later is also known as the MCNP6 code.

42 ENGINEERING↗
DOE OSTI2023

Validation of the single-event method for low-energy electron transport via stopping power calculations with $\mathrm{MCNP}$

Monte Carlo simulations of low-energy ( <50 keV) electron transport in matter are essential for a broad range of application fields. Several Monte Carlo codes have developed specialized treatments for this case, but a comprehensive validation of low-energy electron transport for general-purpose simulations remains lacking in the literature. One approach to accomplish this validation is calculation of stopping power using low-energy electron transport physics, as stopping power is a fundamental radiation transport quantity which must be simulated accurately for nearly any application. Here in this work, we use the Monte Carlo N-Particle (MCNP) radiation transport code with the single-event method for electron transport to calculate stopping powers of low-energy electrons (50 eV to 30 keV) in 41 elemental solids, 14 compound solids, and five rare gas solids, comparing simulation results to published semi-empirical stopping power calculations from optical measurements. In general, the simulations give good agreement (typically within ±10%) with semi-empirical stopping power values at higher energies: 300 eV and above for most elemental solids, 1 keV and above for compound solids, and 400 eV and above for rare gas solids. Agreement between MCNP and semi-empirical values is worse below these energies. The most significant source of error is the EPRDATA14 cross section data, which does not account for changes in electronic structure due to solid-state bonding, particularly in compound materials. The simplistic model of atomic excitation used to generate the EPRDATA14 cross sections is another key source of error. Additionally, the breakdown of the continuous slowing-down approximation introduces significant uncertainty at low energies, although this is a limitation of the calculation method and not of the simulation procedure. Accounting for these and other uncertainty sources, the single event method in MCNP is robust and able to give good accuracy for a variety of low-energy electron transport problems through diverse kinds of materials.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
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 OSTI2025

Impact of Time Dependent Reactor and Sensor Physics on Core Power Synthesis

Online synthesis of the power distribution is critical in the operation and control of nuclear power reactors to ensure that the core is operating within safety margins, and to provide essential knowledge associated with the burnup of the fuel. In light water reactors (LWRs), power synthesis is achieved by using some a priori knowledge of the state of the reactor core and updating based on the signals coming from in-core sensors—namely, self-powered neutron detectors (SPNDs). This report aims to study the effects of fuel burnup and sensor degradation on the ability to accurately synthesize the power distribution in a LWR. Several modeling tools were used to simulate power synthesis based on the responses of SPNDs, with emitters made out of Rh or V. A representative pressurized water reactor low-enriched uranium (LEU) core was modeled using the Polaris/Purdue Advanced Reactor Core Simulator (PARCS) approach. The Monte Carlo N-Particle Transport 6 (MCNP6) code was used, as well, to calculate response functions between different segments of fuel to individual SPNDs; this is a crucial parameter for power synthesis. The Oak Ridge Isotope GENeration (ORIGEN) package in the Standardized Computer Analyses for Licensing Evaluation (SCALE) code was used to model the time-dependent isotopic transmutation in the SPND emitters. All these data were fed into a custom code that enacted the point-based iterative (PBI) method to simulate power synthesis. Developmental work was also performed on high-fidelity SPND models in the GEometry ANd Tracking 4 (Geant4) code, which enables higher-accuracy modeling of the current responses from SPNDs. In this work, five sets of time-dependent power synthesis test cases were conducted. In these test cases, systematic changes in the input conditions enabled an analysis of the effect of (1) slightly inaccurate a priori power distribution assumptions with respect to fuel burnup, (2) highly inaccurate a priori assumptions with respect to fuel burnup (such that burnup is not included in the a priori assumed distribution), and (3) differences between Rh and V SPNDs in terms of downstream consequences of the transmutation in the emitters. The authors discovered that one may permissibly have slightly inaccurate a priori assumptions of the fuel burnup (such that the level of burnup may be slightly under- or over-approximated by the accumulated burnup in approximately 9.3 full power days), but to not account for burnup at all in the a priori assumption leads to severe levels of error, approaching 25% at maximum. The authors also discovered that V SPNDs are extraordinarily robust in the low-enriched uranium fuel cycle considered in this modeling work, whereas Rh SPNDs undergo significant transmutation that can result in large errors in the synthesized power distribution.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2026

Impact of Time-Dependent Reactor and Sensor Physics on Core Power Synthesis (Rev.1)

Online synthesis of power distribution is critical in the operation and control of nuclear power reactors to ensure that the core is operating within safety margins and to provide essential knowledge associated with the burnup of the fuel. In light-water reactors, power synthesis is achieved by using some a priori knowledge of the state of the reactor core and updating based on the signals coming from in-core sensors—namely, self-powered neutron detectors (SPNDs). This report examines the effects of fuel burnup and sensor degradation on the ability to accurately synthesize the power distribution in a pressurized water reactor (PWR), considering the typical low-enriched uranium (LEU, 3%-5% enrichment) fuel cycle as well as the higher enrichment LEU+ (5%-8% enrichment) fuel cycle. Several modeling tools were used to simulate power synthesis based on the responses of SPNDs, with emitters made out of Rh or V. A representative PWR LEU core was modeled using the Polaris/Purdue Advanced Reactor Core Simulator (PARCS) approach. The Monte Carlo N-Particle Transport 6 (MCNP6) code was used as well to calculate response functions between different segments of fuel to individual SPNDs; this is a crucial parameter for power synthesis. The Oak Ridge Isotope GENeration (ORIGEN) package in the Standardized Computer Analyses for Licensing Evaluation (SCALE) code was used to model the time-dependent isotopic transmutation in the SPND emitters. All these data were fed into a custom code that enacted the point-based iterative method to simulate power synthesis. Developmental work was also performed on high-fidelity SPND models in the GEometry ANd Tracking 4 (Geant4) code, which enables higher-accuracy modeling of the current responses from SPNDs. In this work, five sets of time-dependent power synthesis test cases were conducted. In these test cases, systematic changes in the input conditions enabled an analysis of the effect of (1) slightly inaccurate a priori power distribution assumptions with respect to fuel burnup, (2) highly inaccurate a priori power distribution assumptions with respect to fuel burnup (such that burnup is not included in the a priori assumed distribution), and (3) differences between Rh and V SPNDs in terms of downstream consequences of the transmutation in the emitters and the extended nature of the LEU+ fuel cycle in comparison with LEU. The authors discovered that one may permissibly have slightly inaccurate a priori assumptions of the fuel burnup (such that the level of burnup may be slightly underapproximated or overapproximated by the accumulated burnup in approximately 9.3 full power days), but to not account for burnup at all in the a priori assumptions leads to severe levels of error, approaching 25% at maximum (for LEU). The authors also discovered that V SPNDs are extraordinarily robust in both the LEU and LEU+ fuel cycles considered in this modeling work, whereas Rh SPNDs undergo significant transmutation that can result in large errors in the synthesized power distribution.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2023

Activation Analysis in Preparation for a Tungsten Irradiation Experiment at LANSCE

To organize the safe handling of activated material, knowing the residual dose rates is crucial. Here, in this work, we present the pre-experiment activation analysis for an experiment in which tungsten blocks are irradiated by 800-MeV protons. In this analysis, we use the Monte Carlo N-Particle (MCNP) code for radiation transport, Attila4MC for unstructured mesh generation, and Activation in Accelerator Radiation Environments (AARE), including CINDER2008, for activation analysis. If the tungsten blocks must be removed within a day after the experiment, then exposure to personnel entering the room must be reduced. One exposure-reduction strategy is to add carbon steel shielding around the tungsten blocks, efficiently reducing the dose from the activated tungsten. However, the shielding becomes activated itself during irradiation: 56Mn is the dominant contributor for short decay times. The actual schedule at the time of the experiment allowed sufficient cool-off time for the tungsten in the room so that additional shielding was not necessary. A less rigorous comparison of the calculated values with the post-experiment measurements showed reasonable agreement.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

Niowave Neutron Source Converter: Lead-Bismuth-Eutectic (LBE) Windowless Target Design and Evaluation

Los Alamos National Laboratory (LANL) is working with Niowave on the design and evaluation of their lead-bismuth-eutectic (LBE) windowless target (i.e., neutron source converter). Niowave plans to use 200 kW electron beam at 40 MeV beam energy to produce neutrons by photonuclear reaction with LBE. Then, the neutrons undergo fission at the surrounding uranium target assembly (UTA) to produce Molybdenum 99 (Mo-99) as a fission product, which eventually decays to Technetium-99 (Tc-99m). Tc-99m is one of important radioisotopes that is used for medical diagnostics. LANL conducted 3D multiphysics analysis for the Niowave neutron converter design and provided design assessment in thermohydraulic aspects. LANL conducted radiation transport calculations using Monte-Carlo N-Particle (MCNP) code with unstructured meshing scheme. The 3D volumetric heating profiles in the LBE and Stainless-Steel (SS) housing were imported into multiphase computational fluid dynamics (CFD) to obtain 3D temperature profiles of LBE and SS through conjugate heat transfer (CHT) analysis. The key findings are: LBE film thickness at the center of the beam is approximately 1.6 cm with a maximum LBE velocity of approximately 1.8 m/s, which is below a 2 m/s limit to avoid erosion issues on supporting structures; Heat deposition in the LBE peaks at ~1 cm depth from the LBE free-surface because of the forward interactions of electron, photon, and neutron with LBE; LBE maximum temperature is ~360 °C, which is below LBE evaporation initiative temperature, ~450 °C; LBE-SS interface temperature is ~350 °C, which is below the safety thermal limit to prevent severe corrosion on SS. The results indicate that Niowave’s neutron converter design satisfies both hydraulic and thermal criteria for safe operation. By virtue of such computational analysis, Niowave can move toward establishing an experimental setup to experimentally test their LBE neutron converter. The following sections describe the detailed work done by LANL.

43 PARTICLE ACCELERATORS↗
NASA NTRS2009

Monte Carlo Analysis of Pion Contribution to Absorbed Dose from Galactic Cosmic Rays

Accurate knowledge of the physics of interaction, particle production and transport is necessary to estimate the radiation damage to equipment used on spacecraft and the biological effects of space radiation. For long duration astronaut missions, both on the International Space Station and the planned manned missions to Moon and Mars, the shielding strategy must include a comprehensive knowledge of the secondary radiation environment. The distribution of absorbed dose and dose equivalent is a function of the type, energy and population of these secondary products. Galactic cosmic rays (GCR) comprised of protons and heavier nuclei have energies from a few MeV per nucleon to the ZeV region, with the spectra reaching flux maxima in the hundreds of MeV range. Therefore, the MeV - GeV region is most important for space radiation. Coincidentally, the pion production energy threshold is about 280 MeV. The question naturally arises as to how important these particles are with respect to space radiation problems. The space radiation transport code, HZETRN (High charge (Z) and Energy TRaNsport), currently used by NASA, performs neutron, proton and heavy ion transport explicitly, but it does not take into account the production and transport of mesons, photons and leptons. In this paper, we present results from the Monte Carlo code MCNPX (Monte Carlo N-Particle eXtended), showing the effect of leptons and mesons when they are produced and transported in a GCR environment.

Aghara, S.K.↗
NASA NTRS2006

Lunar Surface Reactor Shielding Study

Nuclear reactor system could provide power to support a long term human exploration to the moon. Such a system would require shielding to protect astronauts from its emitted radiations. Shielding studies have been performed for a Gas Cooled Reactor (GCR) system because it is considered to be the most suitable nuclear reactor system available for lunar exploration, based on its tolerance of oxidizing lunar regolith and its good conversion efficiency (Wright, 2003). The goals of the shielding studies were to provide optimal material shielding configuration that reduces the dose (rem) to the required level in order to protect astronauts, and to estimate the mass of regolith that would provide an equivalent protective effect if it were used as the shielding material. All calculations were performed using MCNPX code, a Monte Carlo transport code.

Monte Carlo N-Particle eXtended (MCNPX)↗
DOE OSTI2026

CADIS and FW-CADIS Variance Reduction in Gamma Transport for Predicting Prompt Forensics Signatures

The goal of prompt nuclear forensics is to determine the characteristics of a nuclear detonation based on the signatures available almost immediately after the explosion. An important characteristic is the reaction time history (RTH), a measure of the device’s rate of neutron multiplication. The RTH can be estimated by observation of the gamma radiation emitted from the detonation, which can be detected directly or observed indirectly as Teller light. Gamma transport simulations used to predict these radiation fields are often modeled stochastically using the Monte Carlo N-Particle (MCNP) code, which can be a computationally demanding task due to the number of particle histories needed to achieve statistical convergence. In an attempt to improve the efficiency of these calculations, we evaluate two variance reduction techniques: Consistent Adjoint-Driven Importance Sampling (CADIS) and Forward-Weighted Consistent Adjoint-Driven Importance Sampling (FW-CADIS). These methods use a deterministically calculated adjoint flux to create weight windows and source biasing that guide MCNP sampling. We study the utility of CADIS and FW-CADIS for their use in MCNP gamma transport for nuclear forensics prediction simulations. Furthermore, the results demonstrate that both CADIS and FW-CADIS improve the accuracy for forensics-focused simulations, with CADIS being most beneficial in direct detection and FW-CADIS being ideal for computing a global Teller light source.

CADIS↗
DOE OSTI2025

MCNP® Code Version 6.3.1 Release Notes

The Monte Carlo N-Particle® (MCNP® ) code is a general-purpose, continuous-energy, generalized-geometry, time-dependent, radiation transport code developed by the MCNP development team. MCNP calculations provide predictive capabilities that can replace expensive or impossible-to-perform experiments. Specific application problems include simulations of experimental diagnostics, intrinsic radiation, radiation detection and measurement, criticality safety, nuclear threat reduction and response, radiation health protection, nuclear weapons effects, and nuclear forensics. This MCNP code, version 6.3.1, follows the MCNP6.3.0 version. Since the release of MCNP6.3.0, a variety of bug fixes and code enhancements have been completed for MCNP6.3.1. A few new features have also been added to this release to support both ongoing research and the release of the latest ENDF/B-VIII.1 nuclear data library. The MCNP code, version 6.3.1, theory and user input information is documented in MCNP® Code Version 6.3.1 Theory & User Manual, the build guidance for various platforms is documented in MCNP® Code Version 6.3.1 Build Guide, and the verification and validation testing for various application benchmark test suites is documented in MCNP® Code Version 6.3.1 Verification & Validation Testing.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
NASA NTRS1997

Earth and Planetary Science Letters

Systematic measurements of the concentrations of cosmogen Ca-41 (half-life = 1.04 x 10(exp 5) yr) in the Apollo 15 long core 15001-15006 were performed by accelerator mass spectroscopy. Earlier measurements of cosmogenic Be-10, C-14, Al-26, Cl-36, and Mn-53 in the same core have provided confirmation and improvement of theoretical models for predicting production profiles of nuclides by cosmic ray induced spallation in the Moon and large meteorites. Unlike these nuclides, Ca-40 in the lunar surface is produced mainly by thermal neutron capture reactions on Ca-40. The maximum production of Ca-41, about 1 dpm/g Ca, was observed at a depth in the Moon of about 150 g/sq cm. For depths below about 300 g/sq cm, Ca-41 production falls off exponentially with an e-folding length of 175 g/sq cm. Neutron production in the Moon was modeled with the Los Alamos High Energy Transport Code System, and yields of nuclei produced by low-energy thermal and epithermal neutrons were calculated with the Monte Carlo N-Particle code. The new theoretical calculations using these codes are in good agreement with our measured Ca-41 concentrations as well as with Co-60 and direct neutron fluence measurements in the Moon.

K. Nishiizumi↗
DOE OSTI2025

The MCNP ® 6 code: A decade of progress

After several years of effort involved in merging the Los Alamos National Laboratory MCNP5 and MCNPX codes, in 2013 the first production release of version 6 of the Monte Carlo N-Particle ® , or MCNP ® , code MCNP6.1 was distributed publicly. Since then, three significant releases have been issued: MCNP6.1.1beta in 2014, MCNP6.2 in 2018, and MCNP6.3 in 2023. While each release always contains new features, code enhancements, and bug fixes, each version has had a different primary focus, ranging from improved calculational efficiency to new powerful utilities and tools, to software modernization of the code base. With all that has been learned over the first decade of the MCNP6 code, continuous progress is being made toward a modernized, general-purpose Monte Carlo radiation transport code that remains a trusted resource for the global community of practitioners. This paper describes these first 10+ years of the MCNP6 code and its continually improving data libraries, and gives some insight into how the next decade is expected to unfold.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
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↗