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Direct reconstruction method for discontinuous Galerkin methods on higher-order mixed-curved meshes II. Surface integration.
- Source :
-
Journal of Computational Physics . Sep2020, Vol. 416, pN.PAG-N.PAG. 1p. - Publication Year :
- 2020
-
Abstract
- • DRM has been extended to DG surface integration of Euler/Navier-Stokes equations. • Adaptive MGS process is designed for optimal physical bases of restricted space. • DRM has been verified to significantly reduce computing cost and memory overhead. As the second extension of the direct reconstruction method (DRM), DRM for the surface integration of the discontinuous Galerkin (DG) weak formulation is presented on multi-dimensional high-order mixed meshes. From the previous study, DRM was successfully extended to the volume integration of the DG method, and produced a significant reduction in both the computational cost and memory overhead, particularly for high-order solution approximations on 3-D high-order meshes. DRM has the potential to reduce the computational cost and memory overhead further by treating the surface integration term consistently. To realize this, we design a linear dependency detector and formulate an adaptive orthonormalization process from the modified Gram-Schmidt (MGS) process. DRM is then applied to the restricted function space of the surface integration in an adaptive and optimal manner. The resulting methods, namely the DRM surface integration with the brute force points (BFP) and shape function points (SFP) methods, perform very well on high-order mixed-curved meshes. In addition, the adaptive MGS process is robust and accurate in extracting linear faces/edges from a given set of mixed-curved meshes. Various benchmark tests from the compressible Euler and Navier-Stokes equations verify the impact of DRM for 3-D large-scale computations on mixed-curved meshes. As an example, the DRM framework applied to the 3-D surface and volume integrations with DG- P 3 approximation on P 3-mesh may yield O (10) × speed-up, compared to conventional quadrature-based methods. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 00219991
- Volume :
- 416
- Database :
- Academic Search Index
- Journal :
- Journal of Computational Physics
- Publication Type :
- Academic Journal
- Accession number :
- 143657837
- Full Text :
- https://doi.org/10.1016/j.jcp.2020.109514