2016
DOI: 10.3139/124.110730
|View full text |Cite
|
Sign up to set email alerts
|

Assessing reactor physics codes capabilities to simulate fast reactors on the example of the BN-600 Benchmark

Abstract: This work aims to assess the capabilities of reactor physics codes (initially validated for thermal reactors) to simulate fast sodium cooled reactors. The BFS-62-3A critical experiment from the BN-600 Hybrid Core Benchmark Analyses was chosen for the investigation. Monte-Carlo codes (KENO from SCALE and SERPENT 2.1.23) and the deterministic diffusion code DYN3D-MG are applied to calculate the neutronic parameters. It was found that the multiplication factor and reactivity effects calculated by KENO and SERPENT… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1

Citation Types

0
1
0

Year Published

2018
2018
2022
2022

Publication Types

Select...
4

Relationship

0
4

Authors

Journals

citations
Cited by 4 publications
(1 citation statement)
references
References 3 publications
0
1
0
Order By: Relevance
“…Besides, a comparison between MCNP and TRIPOLI on calculating the multiplication factor for two different core configurations was presented by Henry et al (2015) where Monte Carlo methodology showed a better performance than the deterministic approach in simulating the neutronic behavior. On the other hand, deterministic codes are proved to be less computational time demanding (Ivanov et al, 2016). Time requirements and the available computational power needed to simulate a physical phenomenon is always an issue.…”
Section: Introductionmentioning
confidence: 99%
“…Besides, a comparison between MCNP and TRIPOLI on calculating the multiplication factor for two different core configurations was presented by Henry et al (2015) where Monte Carlo methodology showed a better performance than the deterministic approach in simulating the neutronic behavior. On the other hand, deterministic codes are proved to be less computational time demanding (Ivanov et al, 2016). Time requirements and the available computational power needed to simulate a physical phenomenon is always an issue.…”
Section: Introductionmentioning
confidence: 99%