Add to ORKGPreprint for Finite Volume for Complex Applications 6 (FVCA6)The Multi-dimensional Optimal Order Detection (MOOD) method is an original Very High-Order Finite Volume (FV) method for conservation laws on unstructured meshes. The method is based on an a posteriori degree reduction of local polynomial reconstructions on cells where prescribed stability conditions are not fulfilled. Numerical experiments on advection and Euler equations problems are drawn to prove the efficiency and competitiveness of the MOOD method
We present an a posteriori shock-capturing finite volume method algorithm called GP-MOOD that solves...
In this paper we present a new family of efficient high order accurate direct Arbitrary-Lagrangian-E...
Os datos relativos aos resultados deste artigo poden descargarse desde https://doi.org/10.17862/cran...
Preprint for Finite Volume for Complex Applications 6 (FVCA6)The Multi-dimensional Optimal Order Det...
The Multi-dimensional Optimal Order Detection (MOOD) method has been designed by authors in [5] and ...
The Multi-dimensional Optimal Order Detection (MOOD) method is an original Very High-Order Finite ...
International audienceIn this paper, we investigate an original way to deal with the problems genera...
The Multi-dimensional Optimal Order Detection (MOOD) method for two-dimensional geometries has been ...
In this paper, we investigate an original way to deal with the problems generated by the limitation...
International audienceIn this paper, we investigate the coupling of the Multi-dimensional Optimal Or...
The Multi-dimensional Optimal Order Detection (MOOD) method has been designed by authors in [5] and ...
This paper extends the MOOD method proposed by the authors in [A high-order finite volume method f...
This paper extends the MOOD method proposed by the authors in ["A high-order finite volume method fo...
The Multidimensional Optimal Order Detection (MOOD) method for two-dimensional geometries has been i...
In this paper a relaxed formulation of the a posteriori Multi-dimensional Optimal Order Detection (M...
We present an a posteriori shock-capturing finite volume method algorithm called GP-MOOD that solves...
In this paper we present a new family of efficient high order accurate direct Arbitrary-Lagrangian-E...
Os datos relativos aos resultados deste artigo poden descargarse desde https://doi.org/10.17862/cran...
Preprint for Finite Volume for Complex Applications 6 (FVCA6)The Multi-dimensional Optimal Order Det...
The Multi-dimensional Optimal Order Detection (MOOD) method has been designed by authors in [5] and ...
The Multi-dimensional Optimal Order Detection (MOOD) method is an original Very High-Order Finite ...
International audienceIn this paper, we investigate an original way to deal with the problems genera...
The Multi-dimensional Optimal Order Detection (MOOD) method for two-dimensional geometries has been ...
In this paper, we investigate an original way to deal with the problems generated by the limitation...
International audienceIn this paper, we investigate the coupling of the Multi-dimensional Optimal Or...
The Multi-dimensional Optimal Order Detection (MOOD) method has been designed by authors in [5] and ...
This paper extends the MOOD method proposed by the authors in [A high-order finite volume method f...
This paper extends the MOOD method proposed by the authors in ["A high-order finite volume method fo...
The Multidimensional Optimal Order Detection (MOOD) method for two-dimensional geometries has been i...
In this paper a relaxed formulation of the a posteriori Multi-dimensional Optimal Order Detection (M...
We present an a posteriori shock-capturing finite volume method algorithm called GP-MOOD that solves...
In this paper we present a new family of efficient high order accurate direct Arbitrary-Lagrangian-E...
Os datos relativos aos resultados deste artigo poden descargarse desde https://doi.org/10.17862/cran...