Operational MCNP GUI (OMG)
North Carolina State University, Raleigh, North Carolina.
Operational MCNP GUI is a program that can set up and run Monte Carlo N-Particle (MCNP) calculations. The user interface contains a text editor for creating input files and buttons/dialog windows to aid in file development and to run common MCNP tasks such as plotting and running input files. The primary feature of this GUI is its integration with P-Study from MCNP, which enables parameters (variables) to be defined within input files. These parameters can be substituted with constants, lists, probability distributions, or mathematical expressions, allowing for four main improvements to MCNP files/models.
First, descriptive naming of parameters can be used to identify various properties such as thicknesses, positions, densities, etc., making the code immediately more readable and easier to edit. Second, parameters defined as mathematical expressions allow users to program within the input file, eliminating the need for external calculations or copy and pasting. This is useful for computing properties dependent on parameters and updating stacked dimensions (or related user definitions). Third, the software enables parameter studies, which allow for one file to expand into many possible configurations with different material properties, chemical compositions, dimensions, etc. representing all the physical possibilities in a given experimental or theoretical system in separate individual files to be run and evaluated. Finally, these parametric perturbations enable total Monte Carlo error propagation when parameters are defined according to the probability distributions (describing the physics of the system) by creating a large number of input stacks randomly perturbed according to the known (user-defined) uncertainties in all contributing portions of a system involving radiation creation, transport, and detection. This fourth improvement enables the mean values and errors provided by MCNP to go beyond Monte Carlo counting errors to that of incorporating all errors from the physical system itself, such as those from uncertainty in dimensions, material properties, chemical compositions, etc.
Once a full file is specified, it can be run directly; or if it contains parameters, these can be substituted into corresponding case files, which can be subsequently run. Afterward, tally results can be automatically extracted from mctal files into CSV format, making results more immediately accessible and usable—either through software such as Microsoft Excel or user scripting. By enabling data extraction from mctal files, OMG enables rapid post-processing from potentially very large numbers of executed input stacks.
An integrated development style (IDE) GUI allows users to create input files for MCNP. Files are created via the built-in text editor. OMG is integrated with the MCNP P-Study tool to allow for parameter definitions within the file. Parameter definitions can be used to define MCNP elements according to constants, lists, probability distributions, or mathematical expressions, thus enabling better code readability, programable input files, parameter studies, and total uncertainty propagations. Buttons are used to trigger common MCNP actions such as plotting, running, creating P-Study cases, and collecting results from mctal files into CSV format. Users are required to purchase and install MCNP as a separate package from RSICC.
Must have a working MCNP6 executable to run created input files.
The interactive running time depends upon the complexity of the input file being created.
Windows, Mac, or Linux systems.
MCNP6, X11 Server (Xming, Xquartz, etc.), Perl Interpreter (Windows recommended from Git Distribution (https://git-scm.com/) )
Python (3.7–3.9) for users who wish to build and run from the source code. Additional Python libraries include the MCNPTools extension (comes with MCNP), PyQt5, PyInstaller and wmi (windows).
PUDELKO R, HAYES RB, Workshop P-Study Tool. ANS 2021 Winter Meeting
R. M. PUDELKO, S. C. HANSON, R. B. HAYES, “An Operational MCNP GUI,” presented at ASME 2021 Nuclear Engineering Virtual Conference ICONE28, August 4–6, 2021.
R. M. PUDELKO, S. C. HANSON, R. B. HAYES, “An Operational MCNP GUI,” presented at MCNP Users Symposium, Los Alamos, NM, July 12–16, 2021.
R. M. PUDELKO, S. C. HANSON, R. B. HAYES, “An Operational MCNP GUI,” presented at Health Physics Society NC Chapter Meeting, Chapel Hill, NC, March 29-30, 2021.
FORREST B. BROWN, JEREMY E. SWEEZY, ROBERT B. HAYES, “Monte Carlo Parameter Studies and Uncertainty Analysis with MCNP5,” Proc. PHYSOR 2004, Chicago, Illinois, April 25–29, 2004, American Nuclear Society, 2004.
This package contains:
1) Prebuilt executables for running OMG for Windows, Mac (Intel & M1 chips), and Linux (Ubuntu & Fedora)
2) User manual
3) ANS 2021 Winter Meeting Workshop files – (recording of the P-Study Workshop, PowerPoint presentation, and workshop MCNP input files)
4) Python source code for OMG
KEYWORDS: MONTE CARLO; MCNP, P-STUDY, MCNP6, PARAMETER STUDY, UNCERTAINTY