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All sourcesNASA NTRSDOE OSTI
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 OSTI2022

MCNP ® Code Version 6.3.0 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 6.3.0, 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.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2014

SU-E-T-586: Field Size Dependence of Output Factor for Uniform Scanning Proton Beams: A Comparison of TPS Calculation, Measurement and Monte Carlo Simulation

Purpose: Output dependence on field size for uniform scanning beams, and the accuracy of treatment planning system (TPS) calculation are not well studied. The purpose of this work is to investigate the dependence of output on field size for uniform scanning beams and compare it among TPS calculation, measurements and Monte Carlo simulations. Methods: Field size dependence was studied using various field sizes between 2.5 cm diameter to 10 cm diameter. The field size factor was studied for a number of proton range and modulation combinations based on output at the center of spread out Bragg peak normalized to a 10 cm diameter field. Three methods were used and compared in this study: 1) TPS calculation, 2) ionization chamber measurement, and 3) Monte Carlos simulation. The XiO TPS (Electa, St. Louis) was used to calculate the output factor using a pencil beam algorithm; a pinpoint ionization chamber was used for measurements; and the Fluka code was used for Monte Carlo simulations. Results: The field size factor varied with proton beam parameters, such as range, modulation, and calibration depth, and could decrease over 10% from a 10 cm to 3 cm diameter field for a large range proton beam. The XiO TPS predicted the field size factor relatively well at large field size, but could differ from measurements by 5% or more for small field and large range beams. Monte Carlo simulations predicted the field size factor within 1.5% of measurements. Conclusion: Output factor can vary largely with field size, and needs to be accounted for accurate proton beam delivery. This is especially important for small field beams such as in stereotactic proton therapy, where the field size dependence is large and TPS calculation is inaccurate. Measurements or Monte Carlo simulations are recommended for output determination for such cases.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2012

Observational constraints on K-inflation models

We extend the ModeCode software of Mortonson, Peiris and Easther [1] to enable numerical computation of perturbations in K-inflation models, where the scalar field no longer has a canonical kinetic term. Focussing on models where the kinetic and potential terms can be separated into a sum, we compute slow-roll predictions for various models and use these to verify the numerical code. A Markov chain Monte Carlo analysis is then used to impose constraints from WMAP7 data on the addition of a term quadratic in the kinetic energy to the Lagrangian of simple chaotic inflation models. For a quadratic potential, the data do not discriminate against addition of such a term, while for a quartic (λφ{sup 4}) potential inclusion of such a term is actually favoured. Overall, constraints on such a term from present data are found to be extremely weak.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2019

Molten salt reactor core simulation with PROTEUS

The Nuclear Energy Advance Modeling and Simulation Program (NEAMS) of the US Department of Energy (DOE) aims to develop advanced simulation tools to aid the development of advanced nuclear technologies. The high-fidelity PROTEUS code was developed in the NEAMS program for reactor physics analysis. This paper tests the applicability of the PROTEUS code to simulate a Molten Salt Reactor (MSR). The OpenMC code based on the Monte Carlo Method was employed to generate the cross sections and to provide stochastic solutions for verification. The PROTEUS code was successfully employed to simulate the steady-state performance of an MSR represented by the ThorCon core design. The 2D lattice, 2D core, and 3D full core calculations performed by PROTEUS show good agreement with that predicted by OpenMC. Furthermore, for the nodal option within PROTEUS, the precursor drift model was also tested, and this effect resulted in a reduction of the k-eff by 300 pcm.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2015

Using hybrid implicit Monte Carlo diffusion to simulate gray radiation hydrodynamics

This work describes how to couple a hybrid Implicit Monte Carlo Diffusion (HIMCD) method with a Lagrangian hydrodynamics code to evaluate the coupled radiation hydrodynamics equations. This HIMCD method dynamically applies Implicit Monte Carlo Diffusion (IMD) [1] to regions of a problem that are opaque and diffusive while applying standard Implicit Monte Carlo (IMC) [2] to regions where the diffusion approximation is invalid. We show that this method significantly improves the computational efficiency as compared to a standard IMC/Hydrodynamics solver, when optically thick diffusive material is present, while maintaining accuracy. Two test cases are used to demonstrate the accuracy and performance of HIMCD as compared to IMC and IMD. The first is the Lowrie semi-analytic diffusive shock [3]. The second is a simple test case where the source radiation streams through optically thin material and heats a thick diffusive region of material causing it to rapidly expand. We found that HIMCD proves to be accurate, robust, and computationally efficient for these test problems.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2012

The new MCNP6 depletion capability

The first MCNP based in-line Monte Carlo depletion capability was officially released from the Radiation Safety Information and Computational Center as MCNPX 2.6.0. Both the MCNP5 and MCNPX codes have historically provided a successful combinatorial geometry based, continuous energy, Monte Carlo radiation transport solution for advanced reactor modeling and simulation. However, due to separate development pathways, useful simulation capabilities were dispersed between both codes and not unified in a single technology. MCNP6, the next evolution in the MCNP suite of codes, now combines the capability of both simulation tools, as well as providing new advanced technology, in a single radiation transport code. We describe here the new capabilities of the MCNP6 depletion code dating from the official RSICC release MCNPX 2.6.0, reported previously, to the now current state of MCNP6. NEA/OECD benchmark results are also reported. The MCNP6 depletion capability enhancements beyond MCNPX 2.6.0 reported here include: (1) new performance enhancing parallel architecture that implements both shared and distributed memory constructs; (2) enhanced memory management that maximizes calculation fidelity; and (3) improved burnup physics for better nuclide prediction. MCNP6 depletion enables complete, relatively easy-to-use depletion calculations in a single Monte Carlo code. The enhancements described here help provide a powerful capability as well as dictate a path forward for future development to improve the usefulness of the technology. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2019

A reduced model of neutral-plasma interactions in the edge and scrape-off-layer: Verification comparisons with kinetic Monte Carlo simulations

The 2D scrape-off-layer turbulence code (SOLT) is extended to include neutral-plasma interactions. A Boltzmann equation is derived for the evolution of the bi-normally-averaged neutral distribution function, G( x ,vx,t), in the radial dimension, and this evolution is included in the new code (nSOLT). Neutral-plasma interactions are mediated by charge-exchange (CX) and ionization rates based on poloidally-averaged plasma density and temperature. Good agreement is obtained between asymptotically stationary neutral density profiles from nSOLT simulations and those previously obtained from the Monte Carlo neutral transport code DEGAS 2, for time-averaged NSTX H-mode plasma profiles. The sensitivity of the nSOLT neutral profiles to atomic physics parameters, with and without CX physics, is included in the comparison. In addition, nSOLT simulations that evolve the plasma in 1D, using radial diffusion as a proxy for turbulent (blob) transport, illustrate the convergence to a self-consistent neutral-plasma equilibrium sustained by a neutral source at the far-SOL boundary and plasma heating in the core; equilibria consistent with typical NSTX Ohmic L-mode plasmas are described.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
NASA NTRS2022

Parallel Grand-Canonical Monte Carlo (ParaGrandMC) User’s Manual Version 2.0

This manual describes the commands and command line options for the Parallel Grand Canonical Monte Carlo version 2.0 (ParaGrandMC.2.0) simulation code. This is a highly scalable parallel FORTRAN 2003 code for simulating the thermodynamic evolution of materials at the atomic level, and predicting their thermodynamic state, phase diagram, chemical composition and mechanical properties. The code is specifically designed to simulate multi-component alloy systems, predict solid-state phase transformations such as austenite-martensite transformations, precipitate formation, recrystallization, capillary effects at interfaces, surface absorption, etc., which can aid the design of novel metallic alloys. While the software is mainly tailored for modeling metal alloys, it can also be used for other types of solid-state systems, and to some degree for liquid or gaseous systems, including multiphase systems forming solid-liquid-gas interfaces. In addition to performing Monte Carlo (MC) simulations, the code can also perform Molecular Dynamics (MD) and Langevin Dynamics (LD) simulations, which can be combined and interchanged with MC for faster and more efficient system evolution. A detailed description of the MC part of the code is provided in the NASA ParaGrandMC report: NASA/CR–2016-219202; http://www.sti.nasa.gov.

High performance computing↗
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 OSTI2016

SU-F-T-139: Meeting the Challenges of Quality Control in the TOPAS Monte Carlo Simulation Toolkit for Proton Therapy

Purpose: Monte Carlo particle transport simulation (MC) codes have become important tools in proton therapy and biology, both for research and practice. TOPAS is an MC toolkit serving users worldwide (213 licensed users at 95 institutions in 21 countries). It provides unprecedented ease in 4D placement of geometry components, beam sources and scoring through its user-friendly and reproducible parameter file interface. Quality control (QC) of stochastic simulation software is inherently difficult, and the versatility of TOPAS introduces additional challenges. But QC is vital as the TOPAS development team implements new features, addresses user feedback and reacts to upgrades of underlying software (i.e. Geant4). Methods: Whenever code is committed to our repository, over 50 separate module tests are automatically triggered via a continuous integration service. They check that the various module options execute successfully and that their results are statistically consistent with previous reference values. Prior to each software release, longer end-to-end tests automatically validate TOPAS against experimental data and a TOPAS benchmark. These include simulating multiple properties of spread-out Bragg peaks, validating nuclear models, and searching for differences in patient simulations. Results: Continuous integration has proven effective in catching regressions at the time they are introduced, particularly when implementing new features that involve refactoring code (e.g. multithreading and ntuple output). Code coverage statistics highlight untested portions of code and guide development of new tests. The various end-to-end tests demonstrate that TOPAS accurately describes the physics of proton therapy within clinical tolerances. Conclusion: The TOPAS QC strategy of frequent short tests and pre-release long tests has led to a more reliable tool. However, the versatility of TOPAS makes it difficult to predict how users might combine different modules, and so QC ultimately remains a partnership between the developer and the user. This work was funded by National Cancer Institute grant R01 CA 140735.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

Microdosimetry of proton and carbon ions

Purpose: To investigate microdosimetry properties of 160 MeV/u protons and 290 MeV/u{sup 12}C ion beams in small volumes of diameters 10–100 nm. Methods: Energy distributions of primary particles and nuclear fragments in the beams were calculated from simulations with the general purpose code SHIELD-HIT, while energy depositions by monoenergetic ions in nanometer volumes were obtained from the event-by-event Monte Carlo track structure ion code PITS99 coupled with the electron track structure code KURBUC. Results: The results are presented for frequencies of energy depositions in cylindrical targets of diameters 10–100 nm, dose distributionsyd(y) in lineal energy y, and dose-mean lineal energies y{sup ¯}{sub D}. For monoenergetic ions, the y{sup ¯}{sub D} was found to increase with an increasing target size for high-linear energy transfer (LET) ions, but decrease with an increasing target size for low-LET ions. Compared to the depth dose profile of the ion beams, the maximum of the y{sup ¯}{sub D} depth profile for the 160 MeV proton beam was located at ∼0.5 cm behind the Bragg peak maximum, while the y{sup ¯}{sub D} peak of the 290 MeV/u {sup 12}C beam coincided well with the peak of the absorbed dose profile. Differences between the y{sup ¯}{sub D} and dose-averaged linear energy transfer (LET{sub D}) were large in the proton beam for both target volumes studied, and in the {sup 12}C beam for the 10 nm diameter cylindrical volumes. The y{sup ¯}{sub D} determined for 100 nm diameter cylindrical volumes in the {sup 12}C beam was approximately equal to the LET{sub D}. The contributions from secondary particles to the y{sup ¯}{sub D} of the beams are presented, including the contributions from secondary protons in the proton beam and from fragments with atomic number Z = 1–6 in the {sup 12}C beam. Conclusions: The present investigation provides an insight into differences in energy depositions in subcellular-size volumes when irradiated by proton and carbon ion beams. The results are useful for characterizing ion beams of practical importance for biophysical modeling of radiation-induced DNA damage response and repair in the depth profiles of protons and carbon ions used in radiotherapy.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2015

SU-E-T-203: Comparison of a Commercial MRI-Linear Accelerator Based Monte Carlo Dose Calculation Algorithm and Geant4

Purpose: An MRI-linear accelerator is currently being developed by the vendor Elekta™. The treatment planning system that will be used to model dose for this unit uses a Monte Carlo dose calculation algorithm, GPUMCD, that allows for the application of a magnetic field. We tested this radiation transport code against an independent Monte-Carlo toolkit Geant4 (v.4.10.01) both with and without the magnetic field applied. Methods: The setup comprised a 6 MeV mono-energetic photon beam emerging from a point source impinging on a homogeneous water phantom at 100 cm SSD. The comparisons were drawn from the percentage depth doses (PDD) for three different field sizes (1.5 x 1.5 cm{sup 2}, 5 x 5 cm{sup 2}, 10 x 10 cm{sup 2}) and dose profiles at various depths. A 1.5 T magnetic field was applied perpendicular to the direction of the beam. The transport thresholds were kept the same for both codes. Results: All of the normalized PDDs and profiles agreed within ± 1 %. In the presence of the magnetic field, PDDs rise more quickly reducing the depth of maximum dose. Near the beam exit point in the phantom a hot spot is created due to the electron return effect. This effect is more pronounced for the larger field sizes. Profiles selected parallel to the external field show no effect, however, the ones selected perpendicular to the direction of the applied magnetic field are shifted towards the direction of the Lorentz force applied by the magnetic field on the secondary electrons. It is observed that these profiles are not symmetric which indicates a lateral build up of the dose. Conclusion: There is a good general agreement between the PDDs/profiles calculated by both algorithms thus far. We are proceeding towards clinically relevant comparisons in a heterogeneous phantom for polyenergetic beams. Funding for this work has been provided by Elekta.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2012

The New MCNP6 Depletion Capability

The first MCNP based inline Monte Carlo depletion capability was officially released from the Radiation Safety Information and Computational Center as MCNPX 2.6.0. Both the MCNP5 and MCNPX codes have historically provided a successful combinatorial geometry based, continuous energy, Monte Carlo radiation transport solution for advanced reactor modeling and simulation. However, due to separate development pathways, useful simulation capabilities were dispersed between both codes and not unified in a single technology. MCNP6, the next evolution in the MCNP suite of codes, now combines the capability of both simulation tools, as well as providing new advanced technology, in a single radiation transport code. We describe here the new capabilities of the MCNP6 depletion code dating from the official RSICC release MCNPX 2.6.0, reported previously, to the now current state of MCNP6. NEA/OECD benchmark results are also reported. The MCNP6 depletion capability enhancements beyond MCNPX 2.6.0 reported here include: (1) new performance enhancing parallel architecture that implements both shared and distributed memory constructs; (2) enhanced memory management that maximizes calculation fidelity; and (3) improved burnup physics for better nuclide prediction. MCNP6 depletion enables complete, relatively easy-to-use depletion calculations in a single Monte Carlo code. The enhancements described here help provide a powerful capability as well as dictate a path forward for future development to improve the usefulness of the technology.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2011

Changes in dose with segmentation of breast tissues in Monte Carlo calculations for low-energy brachytherapy

Purpose: To investigate the use of various breast tissue segmentation models in Monte Carlo dose calculations for low-energy brachytherapy. Methods: The EGSnrc user-code BrachyDose is used to perform Monte Carlo simulations of a breast brachytherapy treatment using TheraSeed Pd-103 seeds with various breast tissue segmentation models. Models used include a phantom where voxels are randomly assigned to be gland or adipose (randomly segmented), a phantom where a single tissue of averaged gland and adipose is present (averaged tissue), and a realistically segmented phantom created from previously published numerical phantoms. Radiation transport in averaged tissue while scoring in gland along with other combinations is investigated. The inclusion of calcifications in the breast is also studied in averaged tissue and randomly segmented phantoms. Results: In randomly segmented and averaged tissue phantoms, the photon energy fluence is approximately the same; however, differences occur in the dose volume histograms (DVHs) as a result of scoring in the different tissues (gland and adipose versus averaged tissue), whose mass energy absorption coefficients differ by 30%. A realistically segmented phantom is shown to significantly change the photon energy fluence compared to that in averaged tissue or randomly segmented phantoms. Despite this, resulting DVHs for the entire treatment volume agree reasonably because fluence differences are compensated by dose scoring differences. DVHs for the dose to only the gland voxels in a realistically segmented phantom do not agree with those for dose to gland in an averaged tissue phantom. Calcifications affect photon energy fluence to such a degree that the differences in fluence are not compensated for (as they are in the no calcification case) by dose scoring in averaged tissue phantoms. Conclusions: For low-energy brachytherapy, if photon transport and dose scoring both occur in an averaged tissue, the resulting DVH for the entire treatment volume is reasonably accurate because inaccuracies in photon energy fluence are compensated for by inaccuracies in localized dose scoring. If dose to fibroglandular tissue in the breast is of interest, then the inaccurate photon energy fluence calculated in an averaged tissue phantom will result in inaccurate DVHs and average doses for those tissues. Including calcifications necessitates the use of proper tissue segmentation.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2018

Accelerator-Driven Subcritical System for Disposing of the U.S. Spent Nuclear Fuel Inventory

The current United States inventory of the spent nuclear fuel (SNF) is ~80,000 metric tons of heavy metal (MTHM), including ~131 tons of minor actinides (MAs) and ~669 tons of plutonium. This study describes a conceptual design of an accelerator-driven subcritical (ADS) system for disposing of this SNF inventory by utilizing the 131 tons of MAs inventory and a fraction of the plutonium inventory for energy production, and transmuting some long-lived fission products. An ADS system with a homogeneous subcritical fission blanket was first examined. A spallation neutron source is used to drive the blanket and it is produced from the interaction of a 1-GeV proton beam with a lead-bismuth eutectic (LBE) target. The blanket has a liquid mobile fuel using LBE as the fuel carrier. The fuel materials are dissolved, mixed, or suspended in the liquid fuel carrier. Monte Carlo analyses were performed to determine the overall parameters of the concept. Steady-state Monte Carlo simulations were performed for three similar fission blankets. Except for, the loaded amount of actinide materials in the LBE is either 5, 7, or 10% of the total volume of the blanket, respectively. The neutron multiplication factors of the three blankets are ~0.98 and the initial MAs blanket inventories are ~10 tons. In addition, Monte Carlo burnup simulations using the MCB5 code were performed to analyze the performance of the three conceptual ADS systems. During operation, fresh fuel was fed into the fission blanket to adjust its reactivity and to control the system power. The burnup analysis shows that the three ADS concepts consume about 1.2 tons of actinides per full power year and produce 3 GW thermal power, with a proton beam power of 25 MW. For the blankets with 5, 7, or 10% actinide fuel particles loaded in the LBE, assuming that the ADS systems can be operated for 35 full-power years, the total MA materials consumed in the three ADS systems are about 30.6, 35.3, and 37.2 tons, respectively. Thus, the corresponding numbers of ADS systems to utilize the 131 tons of MA materials of the SNF inventory are 4.3, 3.7, or 3.5, respectively. ADS concepts with tube bundles inserted in the fission blanket were analyzed to overcome the disadvantages of the homogeneous blanket concept. The liquid lead is used as the target material, the mobile fuel carrier, and the primary coolant to avoid the polonium production from bismuth. Reactor physics and thermal-hydraulic analyses were coupled to determine the parameters of the heterogeneous fission blanket. The engineering requirements for a satisfactory operation performance of the HT-9 ferritic steel structure material have been realized. Two heterogeneous concepts of the subcritical fission blanket with the liquid lead mobile fuel inside or outside the tube bundles were considered. The heterogeneous configuration with the mobile fuel inside the tubes showed better performance than the configuration with mobile fuel outside the bundle tubes. The Monte Carlo burnup codes, MCB5 and SERPENT were both used to simulate the fuel burnup in the ADS concepts with the mobile fuels inside the tubes. The burnup analyses were carried out for 35 full power years. The results show that 5 ADS systems can dispose of the total United States inventory of the spent nuclear fuel.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2016

A Chemistry Based Criticality Safety Evaluation of the HM-Process Mixer Settlers

The criticality safety of Savannah River Site’s H Canyon two cycle uranium extraction and purification process, called the HM process, is examined. Explicit Monte Carlo models of the multi-stage mixer-settlers are generated for the first time using the KENO-VI module of the SCALE 6.1 code package and spanning normal operating conditions and a wide range of potential upset conditions with stage wise compositions based directly on SEPHIS Mod-4 chemistry models of the same conditions.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
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↗