[en] Particle population control methods are required in both dynamic and static Monte Carlo methods. If no population control is performed the particle population will die out or grow exponentially. A general particle population control method has been derived from splitting and Russian Roulette for dynamic Monte Carlo particle transport. A well-known particle population control method, known as the particle population comb, has been shown to be a special case of this general method. This general method has been incorporated in Los Alamos National Laboratory's Monte Carlo Application Tool-kit (MCATK) and it has been shown that this method is useful in the dynamic simulation of examples of super-critical and sub-critical systems

[en] The technique for handling time varying problems implemented in the Monte Carlo Application tool-kit (MCATK) provides a robust approach for simulating neutral particle behavior. The tool-kit uses a time interval approach that when applied to the entire population creates opportunities for adjusting simulation parameters to account for emergent problems. The current version of the MCATK uses the time edges to apply a population adjustment. While the tracked population will still fluctuate, the PCA (Population Control Algorithm) removes the common requirement that the user have some knowledge of the systems criticality

[en] In order to reduce the user's time and the computer time needed to solve deep penetration problems, an automated variance reduction capability has been developed for the MCNP Monte Carlo transport code. This new variance reduction capability developed for MCNP5 employs the PARTISN multigroup discrete ordinates code to generate mesh-based weight windows. The technique of using deterministic methods to generate importance maps has been widely used to increase the efficiency of deep penetration Monte Carlo calculations. The application of this method in MCNP uses the existing mesh-based weight window feature to translate the MCNP geometry into geometry suitable for PARTISN. The adjoint flux, which is calculated with PARTISN, is used to generate mesh-based weight windows for MCNP. Additionally, the MCNP source energy spectrum can be biased based on the adjoint energy spectrum at the source location. This method can also use angle-dependent weight windows. (authors)

[en] The Monte Carlo Application tool-kit (MCATK) is a C++ component-based software library designed to build specialized applications and to provide new functionality for existing general purpose Monte Carlo radiation transport codes such as MCNP. We will describe MCATK and its capabilities along with presenting some verification and validations results. (authors)

[en] Both neutron images and spectra will diagnose ignition implosions at the National Ignition Facility. From the integrated Hohlraum and capsule calculations of copper-doped beryllium capsules using ∼1 MJ of laser energy we have postprocessed neutron spectra and both energy-gated and time integrated neutron images displaying the observable consequences of two-dimensional Hohlraum asymmetries. If low signal precludes multiple down scattered images, we suggest a 6-12 MeV gate. With asymmetries, it is noted that the neutron yield, spectra, and images vary with observation direction. The yield varies only several percent when observed at different angles. Since most asymmetries are expected about the Hohlraum axis, a perpendicular view has the highest priority. The next most informative view would be along the Hohlraum axis, but may be precluded by target chamber structures. We present images at the available port angles and discuss their utility. To facilitate detailed diagnostic simulations with real pinhole geometries or penumbral apertures, we offer a compact disk containing neutron spectra and gated images from various integrated calculations

[en] A software tool called mcnp_pstudy has been developed to automate the setup, execution, and collection of results from a series of MCNP5 Monte Carlo calculations. This tool provides a convenient means of performing parameter studies, total uncertainty analyses, parallel job execution on clusters, stochastic geometry modeling, and other types of calculations where a series of MCNP5 jobs must be performed with varying problem input specifications. (authors)

[en] The combination of fast neutron therapy and boron neutron capture therapy is currently being studied as a possible treatment for some radio-resistant brain tumours. In an attempt to design a boron-enhanced fast neutron therapy beam for the Fermilab Fast Neutron Therapy Facility, the use of moderating material surrounding the patient's head has been investigated. Graphite, polyethylene, water and heavy water were studied as moderating materials, using MCNP. The use of tungsten, iron, lead and bismuth as materials for a small filter and collimator near the patient's head was investigated. Calculations showed that a filter and collimator made of tungsten with a graphite moderator was capable of producing a dose enhancement of 17.3 ± 0.6% for a 100 μg g^-1 loading of ¹⁰B for a 5.6 cm diameter beam while delivering 1.5 Gy in 7 min. (authors)

[en] A variety of methods employing radiation transport and point-kernel codes have been used to model two skyshine problems. The first problem is a 1 MeV point source of photons on the surface of the earth inside a 2 m tall and 1 m radius silo having black walls. The skyshine radiation downfield from the point source was estimated with and without a 30-cm-thick concrete lid on the silo. The second benchmark problem is to estimate the skyshine radiation downfield from 12 cylindrical canisters emplaced in a low-level radioactive waste trench. The canisters are filled with ion-exchange resin with a representative radionuclide loading, largely ⁶⁰Co, ¹³⁴Cs and ¹³⁷Cs. The solution methods include use of the MCNP code to solve the problem by directly employing variance reduction techniques, the single-scatter point kernel code GGG-GP, the QADMOD-GP point kernel code, the COHORT Monte Carlo code, the NAC International version of the SKYSHINE-III code, the KSU hybrid method and the associated KSU skyshine codes. (authors)

[en] MCNP6 is simply and accurately described as the merger of MCNP5 and MCNPX capabilities, but it is much more than the sum of these two computer codes. MCNP6 is the result of six years of effort by the MCNP5 and MCNPX code development teams. These groups of people, residing in Los Alamos National Laboratory's X Computational Physics Division, Monte Carlo Codes Group (XCP-3) and Nuclear Engineering and Nonproliferation Division, Radiation Transport Modeling Team (NEN-5) respectively, have combined their code development efforts to produce the next evolution of MCNP. While maintenance and major bug fixes will continue for MCNP5 1.60 and MCNPX 2.7.0 for upcoming years, new code development capabilities only will be developed and released in MCNP6. In fact, the initial release of MCNP6 contains numerous new features not previously found in either code. These new features are summarized in this document. Packaged with MCNP6 is also the new production release of the ENDF/B-VII.1 nuclear data files usable by MCNP. The high quality of the overall merged code, usefulness of these new features, along with the desire in the user community to start using the merged code, have led us to make the first MCNP6 production release: MCNP6 version 1. High confidence in the MCNP6 code is based on its performance with the verification and validation test suites, comparisons to its predecessor codes, our automated nightly software debugger tests, the underlying high quality nuclear and atomic databases, and significant testing by many beta testers. (authors)

[en] Highlights: • MCNP6 is simply and accurately described as the merger of MCNP5 and MCNPX capabilities, but it is much more than the sum of these two computer codes. • MCNP6 is the result of six years of effort by the MCNP5 and MCNPX code development teams. • These groups of people, residing in Los Alamos National Laboratory’s X Computational Physics Division, Monte Carlo Codes Group (XCP-3) and Nuclear Engineering and Nonproliferation Division, Radiation Transport Modeling Team (NEN-5) respectively, have combined their code development efforts to produce the next evolution of MCNP. • While maintenance and major bug fixes will continue for MCNP5 1.60 and MCNPX 2.7.0 for upcoming years, new code development capabilities only will be developed and released in MCNP6. • In fact, the initial release of MCNP6 contains numerous new features not previously found in either code. • These new features are summarized in this document. • Packaged with MCNP6 is also the new production release of the ENDF/B-VII.1 nuclear data files usable by MCNP. • The high quality of the overall merged code, usefulness of these new features, along with the desire in the user community to start using the merged code, have led us to make the first MCNP6 production release: MCNP6 version 1. • High confidence in the MCNP6 code is based on its performance with the verification and validation test suites, comparisons to its predecessor codes, our automated nightly software debugger tests, the underlying high quality nuclear and atomic databases, and significant testing by many beta testers. - Abstract: MCNP6 can be described as the merger of MCNP5 and MCNPX capabilities, but it is much more than the sum of these two computer codes. MCNP6 is the result of six years of effort by the MCNP5 and MCNPX code development teams. These groups of people, residing in Los Alamos National Laboratory’s X Computational Physics Division, Monte Carlo Codes Group (XCP-3) and Nuclear Engineering and Nonproliferation Division, Radiation Transport Modeling Team (NEN-5) respectively, have combined their code development efforts to produce the next evolution of MCNP. While maintenance and major bug fixes will continue for MCNP5 1.60 and MCNPX 2.7.0 for upcoming years, new code development capabilities only will be developed and released in MCNP6. In fact, the initial release of MCNP6 contains numerous new features not previously found in either code. These new features are summarized in this document. Packaged with MCNP6 is also the new production release of the ENDF/B-VII.1 nuclear data files usable by MCNP. The high quality of the overall merged code, usefulness of these new features, along with the desire in the user community to start using the merged code, have led us to make the first MCNP6 production release: MCNP6 version 1. High confidence in the MCNP6 code is based on its performance with the verification and validation test suites, comparisons to its predecessor codes, our automated nightly software debugger tests, the underlying high quality nuclear and atomic databases, and significant testing by many beta testers.