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All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Application of an Empirical Density Law via Python for Aqueous Plutonium Chloride Systems for MCNP6

Criticality safety models for aqueous plutonium chloride systems often contain a significant bias due to assumptions in material compositions. These systems are currently modeled as a fictitious metal-water mixture because little is known about the true solution density. Furthermore, no predictive density tools or capabilities for modeling aqueous plutonium chloride systems are approved for use at Los Alamos National Laboratory. Recent density measurements of this ternary system (PuCl 3 -HCl-H 2 O) have allowed for the development of a more realistic density law, which is applied in this work via an empirical method based in Python. This tool, entitled PuCS (Plutonium Chloride Solution tool) may be used to determine solution density and composition based on the plutonium content, acid content, and temperature for MCNP6 inputs. PuCS has been found to predict density within 2% of experimental data. In conclusion, MCNP6 calculations have found that crediting minimal amounts of free acid (0.5 M) may correspond to a ~12% decrease in peak reactivity in comparison to current modeling methods.

42 ENGINEERING↗
DOE OSTI2015

A Verification of MCNP6 FMESH Tally Capabilities

This work serves to verify the MCNP6 FMESH capability through comparison to two types of data. FMESH tallies, binned in time, were generated on an ideal detector face for neutrons undergoing a single scatter in a graphite target. For verification, FMESH results were compared to analytic calculations of the nonrelativistic TOF for elastic and inelastic single neutron scatters (TOF for the purposes of this paper is the time for a neutron to travel from its scatter location in the graphite target to the detector face). FMESH tally results were also compared to F4 tally results, an MNCP tally that calculates fluence in the same way as the FMESH tally. The FMESH tally results agree well with the analytic results and the F4 tally; hence, it is believed that, for simple geometries, MCNP6 FMESH tallies represent the physics of neutron scattering very well.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

Application of an Empirical Density Law via Python for Aqueous Plutonium Chloride Systems in MCNP6

Current aqueous plutonium processing models for criticality safety often contain significant bias due to material modeling assumptions. These solutions include plutonium chloride solutions, which are modeled as fictitious plutonium metal-water mixtures because little is known about the actual density of the solution. Furthermore, there is no current predictive capability for modeling plutonium metal-water mixtures that is approved for use at Los Alamos National Laboratory (LANL). Recent density measurements for aqueous plutonium chloride systems (PuCl 3 -HCl-H 2 O) now allow for the development and application of a more realistic density law. This work develops a Python-based density law for this ternary solution using an empirical method. This code can be used in conjunction with an MCNP6 input to determine the density and composition of a solution based upon user inputs of plutonium concentration, hydrochloric acid concentration, and temperature. The tool allows users to input plutonium and acid content of a solution in terms of molality, molarity, or concentration, and predicts density within the current data range within 1.4% of experimental data. Current preliminary MCNP6 calculations utilizing this tool have demonstrated a minimum decrease in system reactivity of 5% in comparison to the current modeling conventions. Thus, this tool enables more accurate criticality safety operational limits by better crediting chorine content while still maintaining necessary conservatism.

Density Law↗
DOE OSTI2016

Implementation and testing of the on-the-fly thermal scattering Monte Carlo sampling method for graphite and light water in MCNP6

Here, a proper treatment of thermal neutron scattering requires accounting for chemical binding through a scattering law S(α,β,T). Monte Carlo codes sample the secondary neutron energy and angle after a thermal scattering event from probability tables generated from S(α,β,T) tables at discrete temperatures, requiring a large amount of data for multiscale and multiphysics problems with detailed temperature gradients. We have previously developed a method to handle this temperature dependence on-the-fly during the Monte Carlo random walk using polynomial expansions in 1/T to directly sample the secondary energy and angle. In this paper, the on-the-fly method is implemented into MCNP6 and tested in both graphite-moderated and light water-moderated systems. The on-the-fly method is compared with the thermal ACE libraries that come standard with MCNP6, yielding good agreement with integral reactor quantities like k-eigenvalue and differential quantities like single-scatter secondary energy and angle distributions. The simulation runtimes are comparable between the two methods (on the order of 5–15% difference for the problems tested) and the on-the-fly fit coefficients only require 5–15 MB of total data storage.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2015

Total reaction cross sections in CEM and MCNP6 at intermediate energies

Accurate total reaction cross section models are important to achieving reliable predictions from spallation and transport codes. The latest version of the Cascade Exciton Model (CEM) as incorporated in the code CEM03.03, and the Monte Carlo N-Particle transport code (MCNP6), both developed at Los Alamos National Laboratory (LANL), each use such cross sections. Having accurate total reaction cross section models in the intermediate energy region (50 MeV to 5 GeV) is very important for different applications, including analysis of space environments, use in medical physics, and accelerator design, to name just a few. The current inverse cross sections used in the preequilibrium and evaporation stages of CEM are based on the Dostrovsky et al. model, published in 1959. Better cross section models are now available. Implementing better cross section models in CEM and MCNP6 should yield improved predictions for particle spectra and total production cross sections, among other results.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2017

Background-Source Cosmic-Photon Elevation Scaling and Cosmic-Neutron/Photon Date Scaling in MCNP6

Here, cosmic neutron and photon fluxes are known to scale exponentially with elevation. Consequently, cosmic neutron elevation scaling was implemented for use with the background-source option shortly after its introduction into MCNP6, whereby the neutron flux weight factor was adjusted by the elevation scaling factor when the user-specified elevation differed from the selected background.dat grid-point elevation. At the same time, an elevation scaling factor was suggested for the cosmic photon flux, however, cosmic photon elevation scaling is complicated by the fact that the photon background consists of two components: cosmic and terrestrial. Previous versions of the background.dat file did not provide any way to separate these components. With Rel. 4 of this file in 2015, two new columns were added that provide the energy grid and differential cosmic photon flux separately from the total photon flux. Here we show that the cosmic photon flux component can now be scaled independently and combined with the terrestrial component to form the total photon flux at a user-specified elevation in MCNP6.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2012

MCNP6 for Criticality Accident Alarm Systems – A Primer

MCNP6 is a Los Alamos National Laboratory (LANL) developed, general-purpose Monte Carlo radiation transport simulation package. The code is capable of transporting a broad range of particles for numerous applications. MCNP6 has continuous-energy neutron and photon physics that make it suitable for both criticality and shielding problems. The analysis of a criticality accident alarm system (CAAS) combines methodologies and techniques for both classes of problems, and the requisite knowledge for both is quite different in some areas. For this reason, a primer has been created with the focus on teaching criticality safety practitioners the Monte Carlo techniques needed to analyze a CAAS. This primer specifically focuses on the Monte Carlo transport techniques for CAAS design. The use of radiation transport codes is a small portion of the overall design process, which is preceded and followed by many other design calculations and decisions. Those readers unfamiliar with the design of CAAS systems are directed to a PNNL report by Greenfield, which gives a thorough overview of the CAAS design process. This document gives reference to applicable standards and regulatory requirements and how to meet them, which is not the subject of this primer. The PNNL report explains where radiation transport codes such as MCNP play a role in this process, and this primer is meant to provide a more in-depth set of lessons for this portion of the design process.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2012

Characterization of Delayed-Neutron and Delayed-Gamma Pyroprocessing Emission Signatures Using MCNP6

A computational non-destructive safeguards study has been conducted using the MCNP6 1 radiation-transport code to examine delayed-neutron (DN) current and delayed-gamma (DG) activity ratio (AR) signatures for a conceptual electrorefining and pyroprocessing (EP) model. Spent-fuel (SNF) inventories were prepared using MCNP6 burnup calculations for a generic Westinghouse fuel assembly 2 with 3% and 5% 235 U enrichments; 20-, 30-, 40-, and 50-GWd/MTU burnups; and 3-, 5-, 10-, 20-, and 30-year cooling times to facilitate parametric evaluations. The electrorefining model was patterned after the INL Mark-IV electrorefiner (ER), 3 and treats the distribution of active metals, noble metals, lanthanides, uranium, and TRUs following fuel dissolution and transport to the molten salt eutectic, liquid cadmium cathode (LCC), and cathode mandrel. Signatures have been assessed before and after electrorefining. DG ARs for 134 Cs/ 137 Cs, 134 Cs/ 154 Eu, and 154 Eu/ 137 Cs were developed, and preliminary assessment of new DG ARs that may be better suited for pyroprocessing safeguards was done. Figure 1 shows calculated DN and DG fluxes for 3% enrichment, 20 GWd/MTU burnup, 3 year cooling before electrorefining, with SNF in the ER, and after electrorefining, with material assignment to processing units. Our work suggests that DN and DG markers can be useful for EP forensic analysis.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2012

Modification of ENDF Law 4/44/61 Sampling in MCNP6

During testing of ENDF/B-VII.1 data at Los Alamos National Laboratory (LANL) an issue was discovered with how MCNP sampled data from ENDF Law 4 (or Laws 44 or 61, which are extensions thereof, but Law 4 will be used to imply the others), the Tabular Distribution. A new sampling scheme in MCNP6 was implemented, and is more robust and has a more mathematically correct interpolation scheme. The consequence of this modification is that particle tracking may change with neutron or (more noticeably) in coupled neutron-photon problems where Law 4 data has an outgoing energy distribution represented as both discrete lines and a continuum. The effects on tally results are expected to be small. An overview of ENDF Law 4 is given, the issues with MCNP are discussed, the modifications to MCNP6 that were made are explained, and the effect on test problems is shown.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2013

Summary of DBCN Options in MCNP6

The functions of the DBCN card in MCNP6 have expanded considerably beyond their original uses for debugging and other low-level control of the code, and now include a variety of options affecting the general operation and physics of the transport process. In this document, we present a concise table summarizing the functions of this card. This document refers specifically to the first production release of MCNP6.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

An Electron/Photon/Relaxation Data Library for MCNP6

The capabilities of the MCNP6 Monte Carlo code in simulation of electron transport, photon transport, and atomic relaxation have recently been significantly expanded. The enhancements include not only the extension of existing data and methods to lower energies, but also the introduction of new categories of data and methods. Support of these new capabilities has required major additions to and redesign of the associated data tables. In this paper we present the first complete documentation of the contents and format of the new electron-photon-relaxation data library now available with the initial production release of MCNP6.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2016

Semi-Analytical Benchmarks for MCNP6

Code verification is an extremely important process that involves proving or disproving the validity of code algorithms by comparing them against analytical results of the underlying physics or mathematical theory on which the code is based. Monte Carlo codes such as MCNP6 must undergo verification and testing upon every release to ensure that the codes are properly simulating nature. Specifically, MCNP6 has multiple sets of problems with known analytic solutions that are used for code verification. Monte Carlo codes primarily specify either current boundary sources or a volumetric fixed source, either of which can be very complicated functions of space, energy, direction and time. Thus, most of the challenges with modeling analytic benchmark problems in Monte Carlo codes come from identifying the correct source definition to properly simulate the correct boundary conditions. The problems included in this suite all deal with mono-energetic neutron transport without energy loss, in a homogeneous material. The variables that differ between the problems are source type (isotropic/beam), medium dimensionality (infinite/semi-infinite), etc.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2017

CGMF & FREYA Verification in MCNP6

At the present time, the new and updated fission event generators included in MCNP6.2 have been verified to be functioning properly through a variety of detailed tests. This work describes the detailed verification steps taken to ensure these complicated fission event generators, FREYA and CGMF, are integrated into MCNP6 properly. Ultimately, with the knowledge that MCNP6 is making use of these models appropriately, we can now begin to validate the models against benchmarked experiments. Some benchmarks, including criticality and subcritical experiments interested in multiplication and bulk counting rates, are easy to model and understand but are likely insensitive to the detailed nature of these models. It will take some new measurements with coincidence detection capabilities to be able to stress the physics within each of these fission event generator models. Once the models are validated and it is understood where the models can truly be predictive, then we can study what SNM observables can be characterized for nonproliferation applications.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2017

Using MCNP6 to Estimate Fission Neutron Properties of a Reflected Plutonium Sphere

The purpose of this project was to determine the fission multiplicity distribution, p(v), for the Beryllium Reflected Plutonium (BeRP) ball and to determine whether or not it changed appreciably for various High Density Polyethylene (HDPE) reflected configurations. The motivation for this project was to determine whether or not the average number of neutrons emitted per fission, v, changed significantly enough to reduce the discrepancy between MCNP6 and Robba, Dowdy, Atwater (RDA) point kinetic model estimates of multiplication. The energy spectrum of neutrons that induced fissions in the BeRP ball, NIF (E), was also computed in order to determine the average energy of neutrons inducing fissions, NIF . p(v) was computed using the FMULT card, NIF (E) and NIF were computed using an F4 tally with an FM tally modifier (F4/FM) card, and the multiplication factor, k eff , was computed using the KCODE card. Although NIF (E) changed significantly between bare and HDPE reflected configurations of the BeRP ball, the change in p(v), and thus the change in v, was insignificant. This is likely due to a difference between the way that NIF is computed using the FMULT and F4/FM cards. The F4/FM card indicated that NIF (E) was essentially Watt-fission distributed for a bare configuration and highly thermalized for all HDPE reflected configurations, while the FMULT card returned an average energy between 1 and 2 MeV for all configurations, which would indicate that the spectrum is Watt-fission distributed, regardless of the amount of HDPE reflector. The spectrum computed with the F4/FM cards is more physically meaningful and so the discrepancy between it and the FMULT card result is being investigated. It is hoped that resolving the discrepancy between the FMULT and F4/FM card estimates of NIF(E) will provide better v estimates that will lead to RDA multiplication estimates that are in better agreement with MCNP6 simulations.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2018

The MCNP6 Book On Unstructured Mesh Geometry: User's Guide for MCNP 6.2.1

Los Alamos National Laboratory’s (LANL) Monte Carlo N-Particle R (MCNP R ) transport code has a more general geometry capability than has been available in most combinatorial geometry codes [1]. In addition to the capability of combining several predefined geometric bodies, as in a combinatorial scheme, MCNP6 [2] gives the user the added flexibility of defining geometric regions from all the first and second degree surfaces of analytical geometry and elliptical tori and then combining them with Boolean operators. This decades-old constructive solid geometry (CSG) capability has been well-tested and verified. However, it has long been recognized that as the model complexity increases, this process of describing the geometry is difficult, tedious, time-consuming, and error prone[3, 4, 5]. Consequently, innovators have taken on the task of developing a better way to construct geometries, not only in MCNP6, but other particle transport codes as well

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

MCNP6 Source Primer: Release 1.0

This document contains examples of MCNP general source usage to support the MCNP6 User’s Manual. The examples range from basic to advanced sources with different applications. There are also examples of how MCNP can fail and warnings for the users to look for at the output to ensure proper results are being created. The overall aim of this document is to provide solid examples of how to define sources with MCNP as well as how to verify the sources are setup properly. The reasoning for needing such complex sources is not discussed in the present work. Also, this document is not meant to be exhaustive of every MCNP source option and combinations of source options possible. However, it is intended to frequently update this document with new source examples provided by users. Please feel free to contact the MCNP6 development team to learn about how to submit a new or updated source example. Please include “General Sources Primer” in the subject line to route the request to the appropriate developers.

97 MATHEMATICS AND COMPUTING↗