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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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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.
MCNP (Monte Carlo N-Particle) is code that is used to model the transport of different particles. To verify in MCNP6.1 that arrays formed with reflective and periodic boundary conditions produce a neutron multiplication factor (keff) that is equal to or higher than an array of the same size with no boundary conditions.
To verify in MCNP that arrays formed with reflective and periodic boundary conditions produce a neutron multiplication factor (keff) that is equal to or higher than an array of the same size with no boundary conditions
The verification of U233-COMP-THERM-004 contributed to the LANL centralized repository that’s currently under development. Revisions made to the MCNP model were statistically significant. Magnitude of bias is relatively small in comparison to other uranium benchmark biases and thus will likely have not a significant effect on the USL.
This report describes a variety of metrics calculated by the MCNP code for a user to assess the quality of an input unstructured mesh (UM). These UM quality metrics are calculated automatically, but the user has the opportunity to opt-out of doing so. At present, only linear tetrahedral elements and linear hexahedral elements have extensive quality metrics calculated and reported.
This report describes the verification performed to ensure that a new HDF5-based MCNP® unstructured mesh (UM) [1] file correctly represents elemental values on the associated mesh geometry.
This is a build guide for the MCNP ® code, version 6.3.0, 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.
This report describes the verification and validation testing performed on MCNP ® code version 6.3.0. 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.
The MCNP® code is a general-purpose radiation transport code developed at LANL over the past 46+ years. This code is capable of modeling the fundamental physics of particles as they move through and interact with materials. It is used in many LANL and worldwide radiation transport applications (see next slide) for example in the Fundamental nuclear physics and data experiments at LANSCE.
In this report the Minimum Detection Threshold (MDT) for the Spent Fuel Rod Counter (SFRC) was determined using Monte Carlo N-Particle transport computer code (MCNP). A model of the SFRC was simulated in addition to a fuel transport pipe and spent Magnox fuel at descending positions down the pipe. The neutron MDT for Magnox fuel free-falling along a discharge pipe was calculated to be equal to 4080 n/s. The gamma MDT for the same free falling fuel as measured by the SFRC’s ionization chamber was determined to be 455 γ/s. These values were determined to be a conservative value to use when the instrument is deployed.
The purpose of this report is to describe two non-trivial MCNP models and execution configurations that are suitable to characterizing computational performance. To that end, this report uses a constructive solid geometry (CSG) representation of the Oak Ridge National Laboratory (ORNL) Pool Critical Assembly (PCA) [1–3] to perform a k-eigenvalue calculation and an unstructured mesh (UM) representation of the International Commission on Radiological Protection (ICRP) publication 145 (ICRP145) male human phantom [4, 5] to perform a fixed-source calculation assuming that a 1 MeV photon source is distributed throughout the phantom’s liver.
The ASCII MCNP output file is converted to an interactive PDF using legacy printer control characters.
Current capabilities in MCNP allow for full modeling of TRISO particles to handle uncertainties.
Fast neutron identification and spectroscopy is of great interest to nuclear physics experiments. Using the neutron elastic scattering, the fast neutron momentum can be measured. Wang and Morris introduced the theoretical concept that the initial fast neutron momentum can be derived from up to three consecutive elastic collisions between the neutron and the target, including the information of two consecutive recoil ion tracks and the vertex position of the third collision or two consecutive elastic collisions with the timing information. Here, we also include the additional possibility of measuring the deposited energies from the recoil ions. In this paper, we simulate the neutron elastic scattering using the Monte Carlo N-Particle Transport Code (MCNP) and study the corresponding neutron detection and tracking efficiency. The corresponding efficiency and the scattering distances are simulated with different target materials, especially natural silicon (92.23%28Si, 4.67%29Si, and 3.1%30Si) and helium-4 (4He). The timing of collision and the recoil ion energy are also investigated, which are important characters for the detector design. We also calculate the ion traveling range for different energies using the software, “The Stopping and Range of Ions in Matter (SRIM)”, showing that the ion track can be most conveniently observed in 4He unless sub-micron spatial resolution can be obtained in silicon.
Analysis is provided in this report of using two head-on magnetohydrodynamic (MHD) shocks to achieve supercritical nuclear fission in an axially elongated cylinder filled with UF4 gas as an energy source for deep space missions. The motivation for each aspect of the design is explained and supported by theory and numerical simulations. A subsequent report will provide detail on relevant experimental work to validate the concept. Here the focus is on the theory of and simulations for the proposed gas core reactor conceptual design from the onset of shock generations to the supercritical state achieved when the shocks collide. The MHD model is coupled to a standard nuclear code (MCNP) to observe the neutron flux and fission power attributed to the supercritical state brought about by the shock collisions. Throughout the modeling, realistic parameters are used for the initial ambient gaseous state and currents to ensure a resulting supercritical state upon shock collisions.
Of great importance in the design of a nuclear power plant is the evaluation of shielding from the ionizing radiation produced in the core of the nuclear reactor. This requires simulation software to predict the dose rates in the areas of interest. These problems are referred to as deep penetration problems, as they involve the reduction of a radiation field by two or three orders of magnitude or more. This makes accurate analyses difficult for Monte Carlo based solutions that rely on enough particles being sampled in an area of interest. Comparing the simulation of the same modeled system with more than one computer application, especially one that uses a different algorithmic approach, is a way to gain confidence in the results. In this research, two models were created, one in Monte Carlo N Particle (MCNP) which uses a stochastic algorithmic approach, and the other in Attila, which uses a deterministic algorithmic approach to test the validity of the model and to estimate the dose rates in the area of interest. Successful comparison of the two models is a way we can validate the model used for that estimation.
Abstracts are provided from the 1st Annual 2021 MCNP ® User Symposium.
The development of a new fixed-source sensitivity tally capability is currently underway in the MCNP code. In recent research and development efforts that utilize machine learning to both seek problematic nuclear data as well as design experiments optimized to improve the nuclear data, the adjoint-weighted k-eigenvalue sensitivity tally capabilities have been heavily essential. In this paper, the motivation to expand the sensitivity tally capabilities beyond k-eigenvalues toward diverse fixed-source problems along with preliminary results and verification will be discussed.