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All sourcesNASA NTRSDOE OSTI
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↗
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 OSTI2026

Evaluation of simulated HPGe detector efficiencies in OpenMC compared to MCNP

In late 2023, the open-source radiation transport code OpenMC introduced a pulse-height tally (PHT) feature, enabling users to track the total energy deposited by individual photons in OpenMC cells. This function represents an important improvement of the OpenMC code because it allows users to simulate the response function of a gamma detector without relying on closed-source alternatives. Despite this, limited work has been published evaluating OpenMC as a radiation transport code for simulating gamma spectroscopy experiments. This study attempts to demonstrate the usefulness of OpenMC in this space by directly comparing its PHT output to MCNP (a trusted industry-standard Monte Carlo code). In the first half of this study, a radiation transport experiment is described in which a detector with a complex internal geometry is exposed to various gamma-emitting isotopes ( 133 Ba, 137 Cs, and 60 Co) over a range of distances. The setup of this experiment was modeled in MCNP with sufficient detail to capture the efficiency characteristics of a high purity germanium (HPGe) detector for the primary gammas of each isotope. After good agreement between the MCNP model and experiment was achieved, an identical model was produced in OpenMC to allow direct comparison between the full energy peak (FEP) values produced in OpenMC and MCNP. The results show strong agreement between OpenMC and MCNP across the full range of tested energies, with each model’s FEP values typically within 2% of each other and most FEP areas within 4% of experimental data. Worse agreement was seen between the Monte Carlo codes and experiment below 300 keV (an expected result). For the 662 keV line of 137 Cs, both codes were found in poor agreement with experiment and each other over the full range of distances tested (possibly indicating an error with the 137 Cs experimental data). Ignoring the anomalous results of the 662 keV line, all other data show good qualitative and quantitative agreement between MCNP and OpenMC. This result demonstrates the accuracy of OpenMC’s PHT feature for spectroscopic applications in which detector efficiency is a primary concern.

07 - ISOTOPES AND RADIATION SOURCES↗
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 OSTI2025

A Practical guide to Parsing MCNP Inputs: Lessons Learned from Implementing Context-Free Parsing in MontePy

Monte Carlo N-Particle (MCNP) is a widely used Monte Carlo transport solver that began development in the 1960’s. Due to this MCNP input files uses a custom input syntax, for which there are no off-the-shelf parsing libraries available. For MontePy to create an effective Object-Oriented interface for MCNP input files, an context-free parser was implemented to be able to fully parse the files. MontePy uses a number of shortcuts and optimizations to avoid creating a single universal input file parser. . These lessons can be applied to working with the many other custom input syntax languages persistent throughout the nuclear industry.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Updating the MCNP neutron guide extension for supermirror absorption, gamma production, and neutron polarization

As supermirror technology has developed, there is a trend towards higher m-values for supermirrors which propagate more neutrons with higher divergence to experimental areas. These supermirrors have thousands of alternating layers of typically nickel and titanium with varying thicknesses deposited on the surface. Neutrons of grazing incidence are reflected from these layers with a probability dependent on the angle of incidence and the wavelength of the neutron. Those neutrons that are not reflected may be captured in the supermirror layers and produce high energy gamma rays that require shielding. The supermirror extension for MCNP has been updated to estimate the capture of neutrons in the supermirror layers of a neutron guide through the banking of photons in a virtual supermirror layer and the reduction of the weight of the neutrons transmitted into the substrate. A new functionality has been added to simulate neutron polarization specifically in the context of a supermirror polarizer for neutron beamlines. In conclusion, the underlying theory, code implementation in MCNP, and examples of gamma production and a V-cavity polarizer will be discussed.

Gamma production↗
DOE OSTI2025

An NCrystal extension for thermal neutron scattering in MCNP

A new extension for MCNP has been developed that incorporates the NCrystal thermal scattering package in the form of a Fortran to C++ wrapper, which adds the capability to describe thermal neutron scattering in the NCrystal formalism instead of the ACE tabular format. This can be used to described poly- or single-crystalline materials as well as liquids. In conclusion, the functionality of this extension to MCNP is illustrated using a layered crystal monochromator, leakage of neutrons from a moderating sphere of material, and by simulating criticality benchmarks.

MCNP↗
DOE OSTI2025

On MCNP Stochastic Volume Estimation Normalization

This paper resolves a perennial point of confusion regarding the source-weighting normalization factor recommended in the MCNP manual (𝜋⁢𝑟 2 ) to stochastically estimate the volume of a region within an enclosing inward-directed spherical surface source with radius 𝑟. The normalization factor arises from the relationship between a sphere’ s mean chord length, its volume, and the values estimated by MCNP track-length tallies. A brief derivation is given that relates these quantities and results in the stated normalization. The correctness of this factor is demonstrated by estimating the volume of a variety of convex and nonconvex volumes. A heuristic demonstration of how biasing the inward-directed source reduces the statistical uncertainty of the stochastic volume estimate is also given, but a rigorous analysis of this improvement is left as future work.

42 ENGINEERING↗
DOE OSTI2026

ACRRF High-Bay Dose Calculations using MCNP (Part A)

Analytical tools and models have been developed as a starting point for directly assessing dose in the Annular Core Research Reactor Facility (ACRRF) due to reactor operation. Key results include peak dose along the Central Cavity (CC) centerline (beamline) at the cavity level, dose throughout the High-Bay (HB), and dose on the facility roof for partially-shielded reactor operation where the 4” insert is removed from the CC Shield Plug (SP). Model results in the beamline are benchmarked against measured doses from passive dosimetry evaluations. Personnel total (neutron and gamma) dose in the ACRRF HB is calculated using Monte Carlo N-Particle (MCNP). Various CC and SP configurations are analyzed, including unshielded (no SP) and partially shielded (SP installed but 4” insert removed). Novel application of Variance Reduction (VR) techniques, namely the Surface Source Write (SSW) and Surface Source Read (SSR) capabilities in MCNP, enable impressive resolution (in a Monte Carlo modeling sense) of dose throughout much the facility. The VR techniques reduce stochastic error for challenging tallies, with more advanced techniques explored in the companion to this report (Part B) [1]. Supplementary studies (including a verification analysis) and pedagogic evaluations in Part B involve neutron spectra, angular distributions, and the dose impact of facility characteristics. With the SP 4” insert removed and the Lead-Boron (44”) Bucket (LB–44) in the reactor cavity, Total Effective Dose (TED) within the CC beamline is ≈140 rem per 300 MJ of reactor yield (or 3900 rem per hour at 100% Steady-State (SS) power). With no SP (unshielded) and a Free-Field (FF) cavity, TED within the beamline is ≈610 rem per 300 MJ (or 17000 rem per hour at 100% SS power). Due to the predicted collimation of radiation by the reactor pool (and partial SP, if present), beamline dose is much greater than the scattered radiation field surrounding the cavity and reactor tank. Comparisons are made to beamline dosimetry measurements to validate the model. Model predictions agree reasonably well (⪅10%) with measured quantities of neutron fluence, gamma fluence, and spectral metrics. Away from the beamline, comparisons made to previous dose measurements in the HB agreement within an order of magnitude.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2025

MCNP® Code Version 6.3.1: Verification & Validation Testing (Rev.1)

This report describes the verification and validation testing performed on MCNP® code version 6.3.1. The purpose of this report is to act as a compendium of test suite descriptions and results. Accordingly the document is divided into two parts. Part I (this part) describes each test suite in the following sections. Part II provides the results of testing each suite and comparisons to experimental and/or alternative computational results, as appropriate.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

MCNP® Code Version 6.3.1: Build Guide (Rev. 1)

This is a build guide for the MCNP® code, version 6.3.1, that expands upon the README.md included with the source code. It covers compilers, dependencies, building, testing, and installing the code in one of its supported configurations.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2026

Continuous-Energy Verification of MCNP Calculations Using One-Group Spherical and Slab Criticality Benchmarks

This work presents a continuous-energy Monte Carlo verification study of one group spherical and slab criticality benchmarks using the MCNP ®1 code. Classical tabulations and newly generated benchmark solutions obtained by direct numerical evaluation by the author are considered. The benchmarks span weakly to strongly multiplying regimes and provide analytically defined critical radii as functions of a single parameter, c .

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
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 OSTI2025

FSEN Reaction Rate Calculations in MCNP [Slides]

FSEN provides an opportunity to quickly calculate sensitivities to orthogonal measurements during integral experiments. Toy problem has been deployed to expand on verification of FSEN for reaction rate ratios. While there is decent agreement, further investigation must be done on multiplication’s impact on sensitivity vector.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗