The MCNP®6 Code: A Decade of Progress
Abstract not provided.
Nuclear Criticality Safety Program (NCSP)↗
Engineering topics
Sweezy, Jeremy Ed
Publications and source records attributed to Sweezy, Jeremy Ed.
Improvements to Contributions from Neutron Inelastic Scattering for Next-Event Estimators in MCNP® Software
Explore the source record for details and available documents.
MCNP, Next-Event Estimators↗
Methods, systems, and apparatuses for calculating global fluence for neutron and photon monte carlo transport using expected value estimators
Global fluence estimators may be calculated on accelerators and processors for neutron and photon Monte Carlo transport. Monte Carlo random walk simulation may be performed on the processors and the calculation of a Volumetric-Ray-Casting (VRC) estimator may be offloaded to the accelerators. The VRC estimator may modify an expected-value estimator to extend a pseudo-particle ray along the direction of the emitted particle from source and collision event through not only the event volume, but also through all volumes that describe the problem geometry. Additionally, many pseudo-particle rays may be sampled per event, rather than just a single pseudo-particle ray per event, in order to provide more complete angular coverage.
Sweezy, Jeremy Ed↗
Improvements to Contributions from Neutron Inelastic Scattering for Next-Event Estimators in MCNP ® Software
An improvement to the handling of contributions from neutron inelastic scattering to next-event estimators has been implemented in the MCNP6 ® software for release with version 6.3.1. The kinematic equations that govern the outgoing energy of inelastic neutron scattering contributions to next-event estimators have two roots. Historically, the implementation in the MCNP coding has only used the upper root to the quadratic equation. This includes all versions predating version 6.3.1 including all versions of MCNP5 and MCNPX software. However, a review of the neutron next-event estimator physics has shown that this does not reproduce the track-length estimator results at low energies. Several examples are presented that test single neutron inelastic scattering reaction types, Level Scattering (Law 3), Tabulated Energy Angle (Law 61), and Kalbach-Mann distribution (Law 44). The test problems compare the track-length estimator (f4 tally) in the MCNP software, with the existing implementation of the neutron next-event estimator (f5 tally), and the modified changes to the neutron next-event estimator implementation. As the MCNP implementation ignores the lower root, the unmodified neutron next-event estimators will generally underestimate the lower energy contribution. However, a second issue with the Kalbach-Mann distribution (Law 44) implementation allows contributions to backward scattering in the center-of-mass frame that is not kinematically possible, thus overestimating backward scattering contributions. A third issue with the way the MCNP implementation handles floating point comparison for scattering directly ahead or directly backward in the center-of-mass frame generally leads to underestimation (except for backwards scattering for Law 44).
73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
Neutron Next-Event Estimators Kinematics (Rev.2)
This paper reviews the kinematics of neutron elastic and inelastic scattering with moving and stationary targets for contributions to Neutron Next-Event Estimators (NEEs). NEEs are often used for simulating the response of detectors in locations that have few random walk particles. Contributions to NEEs from collisions differ from sampling the outgoing particle state in standard random walk collisions as the location of the estimator is fixed, thus fixing the scattering angle between the incoming and outgoing directions. To calculate contributions to NEEs, the outgoing energies and probabilities of scatter toward the estimator must be calculated. This paper presents the most general case that encompasses elastic and inelastic scattering with both moving and stationary targets. The moving target case is required for elastic scattering with thermal motion due to the free gas thermal treatment approximation. The stationary target equations are presented for comparison with equations presented in canonical Monte Carlo texts.
73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
MCNP ® Code V.6.3.0 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. The 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 6.3.0, follows the MCNP6.2.0 version. Since the release of MCNP6.2.0, many changes have been made to the MCNP code. These changes include new or improved features, a new build system, code enhancement and modernization, and bug fixes. The MCNP code, version 6.3.0, theory and user input information is documented in MCNP ® Code Version 6.3.0 Theory & User Manual, the build guidance for various platforms is documented in MCNP ® Code Version 6.3.0 Build Guide, and the verification and validation testing for various application benchmark test suites is documented in MCNP ® Code Version 6.3.0 Verification & Validation Testing.
73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
Performance Analysis of the Expected Track Length Estimator
This work investigates whether performance of the MCNP code is improved by the implementation of the expected track length estimator. The expected track length estimator is known to decrease variance when compared to the track length estimator in many classes of Monte Carlo particle transport simulations. However, the computational cost of computing an exponent for every particle collision within a tallied cell has previously been considered prohibitively expensive. Therefore, the goal of this work is to determine whether the variance improvements seen by the expected track length estimator in most cases makes up for the increase in transport time when implemented in the MCNP code.
73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
FY21 MCNP ® Updates for the Nuclear Criticality Safety Program [Slides]
LANL has devoted a substantial amount effort toward the MCNP6.3 release over the past year by improving many of their processes, infrastructure, and capabilities. Many new resources in support of the MCNP code and DOE NCSP applications are coming online now. LANL is excited to receive feedback on all of the new and improved capabilities that will be packaged with MCNP6.3.
73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
Upcoming MCNP6.3 ® Release: New Features and Improvements [Slides]
The latest version of the MCNP6.3 code will be done and released this year. Many new features, substantial improvements, and bugfixes have made their way into the code. Improved documentation, testing, and peripheral tools will also be delivered.
73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
2021 MCNP ® User Symposium Developer Abstracts
Abstracts are provided from the 1st Annual 2021 MCNP ® User Symposium.
73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
Recent MCNP6 ® Code Developments and Improvements for Nuclear Engineering Applications [Slides]
The Los Alamos MCNP Monte Carlo radiation transport code has been the international gold standard for particle transport applications for over three decades. Many developments to the code have taken place with several significant new feature additions, major improvements, and enhancements to existing features. With significant institutional and programmatic investment in the code since the time of the last public release in 2018, important code development and infrastructure modernization has taken place and remains a high priority for all ongoing efforts across the code development team.
73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
Overview of the MCNP6® SQA Plan and Requirements [Memorandum]
For all X Computational Physics Division (XCP) software under the Associate Laboratory Directorate for Weapons Physics (ALDX), the Weapons Research Services Secure Networks and Assurance Group (WRS-SNA) manages the software quality assurance (SQA) plan, requirements and guidance with respect to development processes and tools to meet the broader LANL SQA requirements. Each XCP software product is categorized into one of three software types: Safety Software, Non-Safety Risk Significant Software, and Non-Safety Commercially Controlled Software. In 2018, using LANL Form 2033, the MCNP6 code was categorized by the XCP division as Non-Safety Commercially Controlled Software, provided in Appendix A. Using WRSFORM- 0001U, the MCNP6 code was graded as a Medium Impact software product, provided in Appendix B. Given these determinations, the WRS-AD-0010U SQA plan is followed for all MCNP6 developments, documentation and code releases.
97 MATHEMATICS AND COMPUTING↗
Integration of Kokkos into MonteRay [Slides]
Monte Carlo Neutron Transport codes are C++-based and simulate interaction of nuclear particles with materials. MonteRay is a library for accelerating Monte Carlo ray-casting tallies with GPUs. Kokkos is commonly used to write performance portable applications for HPC platforms. A goal is to implement Kokkos into the Expected Path Length file that uses Cuda as a backend. The methodology and results are summarized.
73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
Implementation of Doppler Broadening of the Neutron Elastic Scattering Cross Section in MCATK (U)
The ability to Doppler broaden the neutron elastic scattering cross section as a function of cell material temperature has been added to the Monte Carlo Application Toolkit (MCATK). Two different computational methods have been tested for speed and correctness. The implementation has been tested against Doppler broadened nuclear data generated using the NJOY nuclear data processing code. The implementation in MCATK compares well with the tabulated data from NJOY, typically within 0.25%. Note, version 2.0 has clarifying changes only compared to the October 2, 2012 version (LA-UR-12-25645).
73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
A Monte Carlo volumetric-ray-casting estimator for global fluence tallies on GPUs
A Monte Carlo fluence estimator has been designed to take advantage of the computational power of graphical processing units (GPUs). This new estimator, termed the volumetric-ray-casting estimator, is an extension of the expectation estimator. It can be used as a replacement of the track-length estimator for the estimation of global fluence. Calculations for this estimator are performed on the GPU while the Monte Carlo random walk is performed on the central processing unit (CPU). This method lowers the implementation cost for GPU acceleration of existing Monte Carlo particle transport codes as there is little modification of the particle history logic flow. Three test problems have been evaluated to assess the performance of the volumetric-ray-casting estimator for neutron transport on GPU hardware in comparison to the standard track-length estimator on CPU hardware. Evaluation of neutron transport through air in a criticality accident scenario showed that the volumetric-ray-casting estimator achieved 23 times the performance of the track-length estimator using a single core CPU paired with a GPU and 15 times the performance of the track-length estimator using an eight core CPU paired with a GPU. Finally, simulation of a pressurized water reactor fuel assembly showed that the performance improvement was 6 times within the fuel and 7 times within the control rods using an eight core CPU paired with a single GPU.
74 ATOMIC AND MOLECULAR PHYSICS↗
Photon Next-Event Estimator Implementation in MCATK
A photon next-event fluence estimator at a point has been implemented in the Monte Carlo Application Toolkit (MCATK). The next-event estimator provides an expected value estimator for the flux at a point due to all source and collision events. An advantage of the next-event estimator over track-length estimators, which are normally employed in MCATK, is that flux estimates can be made in locations that have no random walk particle tracks. The next-event estimator allows users to calculate radiographs and estimate response for detectors outside of the modeled geometry. The next-event estimator is not yet accessable through the MCATK FlatAPI for C and Fortran. The next-event estimator in MCATK has been tested against MCNP6 using 5 suites of test problems. No issues were found in the MCATK implementation. One issue was found in the exclusion radius approximation in MCNP6. The theory, implementation, and testing are described in this document.
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
Continuous Energy Photon Transport Implementation in MCATK
The Monte Carlo Application ToolKit (MCATK) code development team has implemented Monte Carlo photon transport into the MCATK software suite. The current particle transport capabilities in MCATK, which process the tracking and collision physics, have been extended to enable tracking of photons using the same continuous energy approximation. We describe the four photoatomic processes implemented, which are coherent scattering, incoherent scattering, pair-production, and photoelectric absorption. The accompanying background, implementation, and verification of these processes will be presented.
73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
Integral Criticality Estimators in MCATK
The Monte Carlo Application ToolKit (MCATK) is a component-based software toolset for delivering customized particle transport solutions using the Monte Carlo method. Currently under development in the XCP Monte Carlo group at Los Alamos National Laboratory, the toolkit has the ability to estimate the ke f f and a eigenvalues for static geometries. This paper presents a description of the estimators and variance reduction techniques available in the toolkit and includes a preview of those slated for future releases. Along with the description of the underlying algorithms is a description of the available user inputs for controlling the iterations. The paper concludes with a comparison of the MCATK results with those provided by analytic solutions. The results match within expected statistical uncertainties and demonstrate MCATK’s usefulness in estimating these important quantities.
97 MATHEMATICS AND COMPUTING↗