Energy-dependent, self-adaptive mesh h(p)-refinement of a constraint-based continuous Bubnov-Galerkin isogeometric analysis spatial discretization of the multi-group neutron diffusion equation with dual-weighted residual error measures
Author(s)
Wilson, SG
Eaton, Matthew
Kophazi, Jozsef
Type
Journal Article
Abstract
Energy-dependent self-adaptive mesh refinement algorithms are developed for a continuous Bubnov-Galerkin spatial discretization of the multi-group neutron diffusion equation using NURBS-based isogeometric analysis (IGA). The spatially self-adaptive algorithms employ both mesh (h) and polynomial degree (p) refinement. Constraint-based equations are established across irregular interfaces with hanging-nodes; they are based upon master-slave relationships and the conservative interpolation between surface meshes. A similar Galerkin projection is employed in the conservative interpolation between volume meshes to evaluate group-to-group source terms over energy-dependent meshes; and to evaluate interpolation-based error measures. Enforcing continuity over an irregular mesh does introduce discretization errors. However, local mesh refinement allows for a better allocation of computational resources; and thus, more accuracy per degree of freedom. Two a posteriori interpolation-based error measures are proposed. The first heuristically minimizes local contributions to the discretization error, which becomes competitive for global quantities of interest (QoIs). However, for localized QoIs, over energy-dependent meshes, certain multi-group components may become under-resolved. The second employs duality arguments to minimize important error contributions, which consistently and reliably reduces the error in the QoI.
Date Issued
2024
Date Acceptance
2024-03-01
Citation
Journal of Computational and Theoretical Transport, 2024, 53 (2), pp.89-152
ISSN
2332-4309
Publisher
Informa UK Limited
Start Page
89
End Page
152
Journal / Book Title
Journal of Computational and Theoretical Transport
Volume
53
Issue
2
Copyright Statement
© 2024 The Author(s). Published with license by Taylor & Francis Group, LLC
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://
creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided
the original work is properly cited. The terms on which this article has been published allow the posting of the Accepted
Manuscript in a repository by the author(s) or with their consent.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://
creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided
the original work is properly cited. The terms on which this article has been published allow the posting of the Accepted
Manuscript in a repository by the author(s) or with their consent.
License URL
Identifier
http://dx.doi.org/10.1080/23324309.2024.2313460
Publication Status
Published
Date Publish Online
2024-03-09