2022
DOI: 10.1016/j.anucene.2022.109335
|View full text |Cite
|
Sign up to set email alerts
|

VITAS: A multi-purpose simulation code for the solution of neutron transport problems based on variational nodal methods

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1

Citation Types

0
2
0

Year Published

2022
2022
2024
2024

Publication Types

Select...
7

Relationship

0
7

Authors

Journals

citations
Cited by 28 publications
(2 citation statements)
references
References 39 publications
0
2
0
Order By: Relevance
“…Analyses of fast reactors can be accomplished by either deterministic methods or stochastic methods. Deterministic codes that are established on the neutron transport theory can provide both accurate and efficient solutions to fast reactor problems (Zhang et al, 2022). In addition, the Monte Carlo method is flexible in geometrical modeling and is suited for highfidelity and accurate computations.…”
Section: Calculation Programmentioning
confidence: 99%
“…Analyses of fast reactors can be accomplished by either deterministic methods or stochastic methods. Deterministic codes that are established on the neutron transport theory can provide both accurate and efficient solutions to fast reactor problems (Zhang et al, 2022). In addition, the Monte Carlo method is flexible in geometrical modeling and is suited for highfidelity and accurate computations.…”
Section: Calculation Programmentioning
confidence: 99%
“…Until now, the 2D/1D approximation technique has been applied to plentiful advanced neutronics simulation tools, such as DeCART (Joo et al, 2003), MPACT (Larsen et al, 2019), PROTEUS-MOC (Jung et al, 2018), nTRACER (Choi et al, 2018), NECP-X (Chen et al, 2018), OpenMOC (Boyd et al, 2014), and PANDAS-MOC (Tao and Xu, 2022c). Moreover, other deterministic methods, such as the variational nodal method (VNM), also exhibit the capability in handling arbitrarily complicated geometries and is developed and used in some prestigious neutronics codes, such as VITAS (Zhang et al, 2022).…”
Section: Introductionmentioning
confidence: 99%