Globally Conservative, Hybrid Self-Adjoint Angular Flux and Least-Squares Method Compatible with Void

TL;DR

This paper introduces a novel hybrid second-order transport method combining SAAF and LS formulations that is globally conservative and compatible with voids, applicable to SN and PN discretizations, and validated through implementation and testing.

Contribution

It develops a new hybrid SAAF-CLS method that maintains conservation and void compatibility for second-order transport equations, especially for PN discretization.

Findings

The hybrid method is globally conservative and void compatible.

Implementation in Rattlesnake demonstrates effectiveness.

The method performs well compared to existing approaches.

Abstract

In this paper, we derive a method for the second-order form of the transport equation that is both globally conservative and compatible with voids, using Continuous Finite Element Methods (CFEM). The main idea is to use the Least-Squares (LS) form of the transport equation in the void regions and the Self-Adjoint Angular Flux (SAAF) form elsewhere. While the SAAF formulation is globally conservative, the LS formulation need a correction in void. The price to pay for this fix is the loss of symmetry of the bilinear form. We first derive this Conservative LS (CLS) formulation in void. Second we combine the SAAF and CLS forms and end up with an hybrid SAAF-CLS method, having the desired properties. We show that extending the theory to near-void regions is a minor complication and can be done without affecting the global conservation of the scheme. Being angular discretization agnostic, this method can be applied to both discrete ordinates (SN) and spherical harmonics (PN) methods. However, since a globally conservative and void compatible second-order form already exists for SN (Wang et al. 2014), but is believed to be new for PN, we focus most of our attention on that latter angular discretization. We implement and test our method in Rattlesnake within the Multiphysics Object Oriented Simulation Environment (MOOSE) framework. Results comparing it to other methods are presented.