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Title: 3D Geometry Visualization Capability for MCNP


The purpose of this white paper is to demonstrate the need for a 3D visualizer and graphical user interface for the Monte Carlo n-particle (MCNP®) code.

 [1];  [1];  [1]
  1. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Publication Date:
Research Org.:
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
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DOE Contract Number:
Resource Type:
Technical Report
Country of Publication:
United States

Citation Formats

Spencer, Joshua Bradly, Kulesza, Joel A., and Sood, Avneet. 3D Geometry Visualization Capability for MCNP. United States: N. p., 2017. Web. doi:10.2172/1363738.
Spencer, Joshua Bradly, Kulesza, Joel A., & Sood, Avneet. 3D Geometry Visualization Capability for MCNP. United States. doi:10.2172/1363738.
Spencer, Joshua Bradly, Kulesza, Joel A., and Sood, Avneet. Mon . "3D Geometry Visualization Capability for MCNP". United States. doi:10.2172/1363738.
title = {3D Geometry Visualization Capability for MCNP},
author = {Spencer, Joshua Bradly and Kulesza, Joel A. and Sood, Avneet},
abstractNote = {The purpose of this white paper is to demonstrate the need for a 3D visualizer and graphical user interface for the Monte Carlo n-particle (MCNP®) code.},
doi = {10.2172/1363738},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Mon Jun 12 00:00:00 EDT 2017},
month = {Mon Jun 12 00:00:00 EDT 2017}

Technical Report:

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  • In a Monte Carlo criticality calculation, before the tallying of quantities can begin, a converged fission source (the fundamental eigenvector of the fission kernel) is required. Tallies of interest may include powers, absorption rates, leakage rates, or the multiplication factor (the fundamental eigenvalue of the fission kernel, k{sub eff}). Just as in the power iteration method of linear algebra, if the dominance ratio (the ratio of the first and zeroth eigenvalues) is high, many iterations of neutron history simulations are required to isolate the fundamental mode of the problem. Optically large systems have large dominance ratios, and systems containing poormore » neutron communication between regions are also slow to converge. The fission matrix method, implemented into MCNP[1], addresses these problems. When Monte Carlo random walk from a source is executed, the fission kernel is stochastically applied to the source. Random numbers are used for: distances to collision, reaction types, scattering physics, fission reactions, etc. This method is used because the fission kernel is a complex, 7-dimensional operator that is not explicitly known. Deterministic methods use approximations/discretization in energy, space, and direction to the kernel. Consequently, they are faster. Monte Carlo directly simulates the physics, which necessitates the use of random sampling. Because of this statistical noise, common convergence acceleration methods used in deterministic methods do not work. In the fission matrix method, we are using the random walk information not only to build the next-iteration fission source, but also a spatially-averaged fission kernel. Just like in deterministic methods, this involves approximation and discretization. The approximation is the tallying of the spatially-discretized fission kernel with an incorrect fission source. We address this by making the spatial mesh fine enough that this error is negligible. As a consequence of discretization we get a spatially low-order kernel, the fundamental eigenvector of which should converge faster than that of continuous kernel. We can then redistribute the fission bank to match the fundamental fission matrix eigenvector, effectively eliminating all higher modes. For all computations here biasing is not used, with the intention of comparing the unaltered, conventional Monte Carlo process with the fission matrix results. The source convergence of standard Monte Carlo criticality calculations are, to some extent, always subject to the characteristics of the problem. This method seeks to partially eliminate this problem-dependence by directly calculating the spatial coupling. The primary cost of this, which has prevented widespread use since its inception [2,3,4], is the extra storage required. To account for the coupling of all N spatial regions to every other region requires storing N{sup 2} values. For realistic problems, where a fine resolution is required for the suppression of discretization error, the storage becomes inordinate. Two factors lead to a renewed interest here: the larger memory available on modern computers and the development of a better storage scheme based on physical intuition. When the distance between source and fission events is short compared with the size of the entire system, saving memory by accounting for only local coupling introduces little extra error. We can gain other information from directly tallying the fission kernel: higher eigenmodes and eigenvalues. Conventional Monte Carlo cannot calculate this data - here we have a way to get new information for multiplying systems. In Ref. [5], higher mode eigenfunctions are analyzed for a three-region 1-dimensional problem and 2-dimensional homogenous problem. We analyze higher modes for more realistic problems. There is also the question of practical use of this information; here we examine a way of using eigenmode information to address the negative confidence interval bias due to inter-cycle correlation. We apply this method mainly to four problems: 2D pressurized water reactor (PWR) [6], 3D Kord Smith Challenge [7], OECD - Nuclear Energy Agency (NEA) source convergence benchmark fuel storage vault [8], and Advanced Test Reactor (ATR) [9]. We see excellent source convergence acceleration for the most difficult problems: the 3D Kord Smith Challenge and fuel storage vault. Additionally, we examine higher eigenmode results for all these problems. Using part of the eigenvalue spectrum for a one-group 1D problem, we find confidence interval correction factors that are improvements over existing corrections [10].« less
  • SABRINA is a fully interactive three-dimensional geometry-modeling program for MCNP, a Los Alamos Monte Carlo code for neutron and photon transport. In SABRINA, a user constructs either body geometry or surface geometry models and debugs spatial descriptions for the resulting objects. This enhanced capability significantly reduces effort in constructing and debugging complicated three-dimensional geometry models for Monte Carlo analysis. 2 refs., 33 figs.
  • To better predict global climate change, scientists are developing climate models that require interdisciplinary and collaborative efforts in their building. The authors are currently involved in several such projects but will briefly discuss activities in support of two such complementary projects: the Atmospheric Radiation Measurement (ARM) program of the Department of Energy and Sequoia 2000, a joint venture of the University of California, the private sector, and government. The author`s contribution to the ARM program is to investigate the role of clouds on the top of the atmosphere and on surface radiance fields through the data analysis of surface andmore » satellite observations and complex modeling of the interaction of radiation with clouds. One of the first ARM research activities involves the computation of the broadband shortwave surface irradiance from satellite observations. Geostationary satellite images centered over the first ARM observation site are received hourly over the Internet network and processed in real time to compute hourly and daily composite shortwave irradiance fields. The images and the results are transferred via a high-speed network to the Sequoia 2000 storage facility in Berkeley, where they are archived. These satellite-derived results are compared with the surface observations to evaluate the accuracy of the satellite estimate and the spatial representation of the surface observations. In developing the software involved in calculating the surface shortwave irradiance, the authors have produced an environment whereby they can easily modify and monitor the data processing as required. Through the principles of modular programming, they have developed software that is easily modified as new algorithms for computation are developed or input data availability changes. In addition, the software was designed so that it could be run from an interactive, icon-driven, graphical interface, TCL-TK, developed by Sequoia 2000 participants.« less