# The Physical Models and Statistical Procedures Used in the RACER Monte Carlo Code

## Abstract

This report describes the MCV (Monte Carlo - Vectorized)Monte Carlo neutron transport code [Brown, 1982, 1983; Brown and Mendelson, 1984a]. MCV is a module in the RACER system of codes that is used for Monte Carlo reactor physics analysis. The MCV module contains all of the neutron transport and statistical analysis functions of the system, while other modules perform various input-related functions such as geometry description, material assignment, output edit specification, etc. MCV is very closely related to the 05R neutron Monte Carlo code [Irving et al., 1965] developed at Oak Ridge National Laboratory. 05R evolved into the 05RR module of the STEMB system, which was the forerunner of the RACER system. Much of the overall logic and physics treatment of 05RR has been retained and, indeed, the original verification of MCV was achieved through comparison with STEMB results. MCV has been designed to be very computationally efficient [Brown, 1981, Brown and Martin, 1984b; Brown, 1986]. It was originally programmed to make use of vector-computing architectures such as those of the CDC Cyber- 205 and Cray X-MP. MCV was the first full-scale production Monte Carlo code to effectively utilize vector-processing capabilities. Subsequently, MCV was modified to utilize both distributed-memory [Suttonmore »

- Authors:

- Publication Date:

- Research Org.:
- Knolls Atomic Power Lab. (KAPL), Niskayuna, NY (United States); Albany Research Center (ARC), Albany, OR (United States)

- Sponsoring Org.:
- USDOE

- OSTI Identifier:
- 767449

- Report Number(s):
- KAPL-4840

TRN: US0600268

- DOE Contract Number:
- AC12-76SN00052

- Resource Type:
- Technical Report

- Country of Publication:
- United States

- Language:
- English

- Subject:
- 22 GENERAL STUDIES OF NUCLEAR REACTORS; CRITICALITY; CROSS SECTIONS; EIGENVALUES; GEOMETRY; ISOTOPES; MULTIPLICATION FACTORS; NEUTRON TRANSPORT; REACTION KINETICS; REACTOR PHYSICS; RELIABILITY; SIMULATION; SUPERCOMPUTERS; VECTOR PROCESSING; VERIFICATION

### Citation Formats

```
Sutton, T M, Brown, F B, Bischoff, F G, MacMillan, D B, Ellis, C L, Ward, J T, Ballinger, C T, Kelly, D J, and Schindler, L.
```*The Physical Models and Statistical Procedures Used in the RACER Monte Carlo Code*. United States: N. p., 1999.
Web. doi:10.2172/767449.

```
Sutton, T M, Brown, F B, Bischoff, F G, MacMillan, D B, Ellis, C L, Ward, J T, Ballinger, C T, Kelly, D J, & Schindler, L.
```*The Physical Models and Statistical Procedures Used in the RACER Monte Carlo Code*. United States. https://doi.org/10.2172/767449

```
Sutton, T M, Brown, F B, Bischoff, F G, MacMillan, D B, Ellis, C L, Ward, J T, Ballinger, C T, Kelly, D J, and Schindler, L. 1999.
"The Physical Models and Statistical Procedures Used in the RACER Monte Carlo Code". United States. https://doi.org/10.2172/767449. https://www.osti.gov/servlets/purl/767449.
```

```
@article{osti_767449,
```

title = {The Physical Models and Statistical Procedures Used in the RACER Monte Carlo Code},

author = {Sutton, T M and Brown, F B and Bischoff, F G and MacMillan, D B and Ellis, C L and Ward, J T and Ballinger, C T and Kelly, D J and Schindler, L},

abstractNote = {This report describes the MCV (Monte Carlo - Vectorized)Monte Carlo neutron transport code [Brown, 1982, 1983; Brown and Mendelson, 1984a]. MCV is a module in the RACER system of codes that is used for Monte Carlo reactor physics analysis. The MCV module contains all of the neutron transport and statistical analysis functions of the system, while other modules perform various input-related functions such as geometry description, material assignment, output edit specification, etc. MCV is very closely related to the 05R neutron Monte Carlo code [Irving et al., 1965] developed at Oak Ridge National Laboratory. 05R evolved into the 05RR module of the STEMB system, which was the forerunner of the RACER system. Much of the overall logic and physics treatment of 05RR has been retained and, indeed, the original verification of MCV was achieved through comparison with STEMB results. MCV has been designed to be very computationally efficient [Brown, 1981, Brown and Martin, 1984b; Brown, 1986]. It was originally programmed to make use of vector-computing architectures such as those of the CDC Cyber- 205 and Cray X-MP. MCV was the first full-scale production Monte Carlo code to effectively utilize vector-processing capabilities. Subsequently, MCV was modified to utilize both distributed-memory [Sutton and Brown, 1994] and shared memory parallelism. The code has been compiled and run on platforms ranging from 32-bit UNIX workstations to clusters of 64-bit vector-parallel supercomputers. The computational efficiency of the code allows the analyst to perform calculations using many more neutron histories than is practical with most other Monte Carlo codes, thereby yielding results with smaller statistical uncertainties. MCV also utilizes variance reduction techniques such as survival biasing, splitting, and rouletting to permit additional reduction in uncertainties. While a general-purpose neutron Monte Carlo code, MCV is optimized for reactor physics calculations. It has the capability of performing iterated-source (criticality), multiplied-fixed-source, and fixed-source calculations. MCV uses a highly detailed continuous-energy (as opposed to multigroup) representation of neutron histories and cross section data. The spatial modeling is fully three-dimensional (3-D), and any geometrical region that can be described by quadric surfaces may be represented. The primary results are region-wise reaction rates, neutron production rates, slowing-down-densities, fluxes, leakages, and when appropriate the eigenvalue or multiplication factor. Region-wise nuclidic reaction rates are also computed, which may then be used by other modules in the system to determine time-dependent nuclide inventories so that RACER can perform depletion calculations. Furthermore, derived quantities such as ratios and sums of primary quantities and/or other derived quantities may also be calculated. MCV performs statistical analyses on output quantities, computing estimates of the 95% confidence intervals as well as indicators as to the reliability of these estimates. The remainder of this chapter provides an overview of the MCV algorithm. The following three chapters describe the MCV mathematical, physical, and statistical treatments in more detail. Specifically, Chapter 2 discusses topics related to tracking the histories including: geometry modeling, how histories are moved through the geometry, and variance reduction techniques related to the tracking process. Chapter 3 describes the nuclear data and physical models employed by MCV. Chapter 4 discusses the tallies, statistical analyses, and edits. Chapter 5 provides some guidance as to how to run the code, and Chapter 6 is a list of the code input options.},

doi = {10.2172/767449},

url = {https://www.osti.gov/biblio/767449},
journal = {},

number = ,

volume = ,

place = {United States},

year = {1999},

month = {7}

}