2013
DOI: 10.1016/j.nucengdes.2013.02.005
|View full text |Cite
|
Sign up to set email alerts
|

Application of the simplified eigenstructure decomposition solver to the simulation of general multifield models

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2

Citation Types

0
2
0

Year Published

2013
2013
2022
2022

Publication Types

Select...
2
1

Relationship

1
2

Authors

Journals

citations
Cited by 3 publications
(2 citation statements)
references
References 9 publications
0
2
0
Order By: Relevance
“…One of the reasons for the large oscillations of the interpolation polynomial P exact is the presence of a cluster of intermediate eigenvalues near 0, which are very close to each other and which are 4 orders of magnitude smaller than the extremal eigenvalues (see (2.40)). In [23], an approximate Roe matrix is generated by treating this cluster of eigenvalues like a single eigenvalue (the largest of them). The method of [23] provides comparable results to the usual Roe method in standard situations.…”
Section: Dynamic Polynomial Interpolationmentioning
confidence: 99%
See 1 more Smart Citation
“…One of the reasons for the large oscillations of the interpolation polynomial P exact is the presence of a cluster of intermediate eigenvalues near 0, which are very close to each other and which are 4 orders of magnitude smaller than the extremal eigenvalues (see (2.40)). In [23], an approximate Roe matrix is generated by treating this cluster of eigenvalues like a single eigenvalue (the largest of them). The method of [23] provides comparable results to the usual Roe method in standard situations.…”
Section: Dynamic Polynomial Interpolationmentioning
confidence: 99%
“…In [23], an approximate Roe matrix is generated by treating this cluster of eigenvalues like a single eigenvalue (the largest of them). The method of [23] provides comparable results to the usual Roe method in standard situations. Inspired by this work, we generate an approximate polynomial which interpolates the extremal eigenvalues λ min , λ max and only one of the intermediate eigenvalues, the largest one, denoted by λ max int (as well as its opposite −λ max int for symmetry reasons).…”
Section: Dynamic Polynomial Interpolationmentioning
confidence: 99%