journal article·2019-07-01Open access

Reactor Monte Carlo (RMC) model validation and verification in compare with MCNP for plate-type reactor

Mohammad Alrwashdeh, Saeed A. Alameri

AIP Advances· Volume 9· Issue 7

Abstract

Traditional neutronics calculations and simulation behavior of plate type research reactors pose a challenge comparing with the conventional pressurized water reactors. This is due to its complex geometry, characteristics, heterogeneity, and neutron leakage that lead to modeling difficulties. The Reactor Monte Carlo code (RMC) is employed in stochastic approach for the neutronic analysis in the plate type research reactor. The goal of this report is to examine the capability and validity of the model built using RMC and compare the results with a well-known Monte Carlo code such as MCNP. The results between RMC and MCNP are in a good agreement for effective multiplications factor (keff), radial power peaking factor, total power peaking factor, and integral and differential control rods worth.
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Research context

OpenAlex topics: Nuclear reactor physics and engineering · Nuclear Physics and Applications · Graphite, nuclear technology, radiation studies

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Affiliations

Department of Nuclear Engineering, Khalifa University of Science and Technology , PO Box 127788, Abu Dhabi, United Arab Emirates

References (16)

Show source-provided references
1. RMC—A Monte Carlo code for reactor core analysis10.1016/j.anucene.2014.08.048 ↗
2. RMC—A Monte Carlo code for reactor physics analysis
3. Development of the point-depletion code DEPTH10.1016/j.nucengdes.2013.01.007 ↗
4. 3D neutron transport PWR full-core calculation with RMC code
5. The development and validation of nuclear cross section processing code for reactor-RXSP
6. Implementation of perturbation based Monte Carlo criticality search in RMC
7. A hybrid Monte Carlo and response matrix Monte Carlo method in criticality calculation
8. Research on applying neutron transport Monte Carlo method in materials with continuously varying cross-sections
9. Werner · 2017
10. Research reactor benchmarks10.13182/nse03-a2370 ↗
11. Development of a code FITWR for nuclear cross section statistical analysis10.1016/j.anucene.2014.03.008 ↗
12. U233 data evaluation for criticality study10.1115/1.4032814 ↗
13. Covariance data evaluation for 233U10.1016/j.apradiso.2017.12.020 ↗
14. 239Pu evaluation comparison study10.1016/j.anucene.2018.04.034 ↗
15. Nuclear data statistical treatment
16. 233U evaluation comparison study