A POD reduced order model for resolving angular direction in neutron/photon transport problems

JOURNAL OF COMPUTATIONAL PHYSICS

卷:296

Article

Buchan, A. G.¹  Calloo, A. A.¹  Goffin, M. G.¹  Dargaville, S.¹  Fang, F.¹  Pain, C. C.¹  Navon, I. M.² 

[1] Univ London Imperial Coll Sci Technol & Med, Appl Modelling & Computat Grp, Dept Earth Sci & Engn, London SW7 2AZ, England

[2] Florida State Univ, Dept Sci Comp, Tallahassee, FL 32306 USA

关键词: Neutron transport;    Photon transport;    Boltzmann transport equation;    Reduced order model;    Proper orthogonal decomposition;   

DOI  :  10.1016/j.jcp.2015.04.043

来源: Elsevier

【 摘 要 】

This article presents the first Reduced Order Model (ROM) that efficiently resolves the angular dimension of the time independent, mono-energetic Boltzmann Transport Equation (BTE). It is based on Proper Orthogonal Decomposition (POD) and uses the method of snapshots to form optimal basis functions for resolving the direction of particle travel in neutron/photon transport problems. A unique element of this work is that the snapshots are formed from the vector of angular coefficients relating to a high resolution expansion of the BTE's angular dimension. In addition, the individual snapshots are not recorded through time, as in standard POD, but instead they are recorded through space. In essence this work swaps the roles of the dimensions space and time in standard POD methods, with angle and space respectively. It is shown here how the POD model can be formed from the POD basis functions in a highly efficient manner. The model is then applied to two radiation problems; one involving the transport of radiation through a shield and the other through an infinite array of pins. Both problems are selected for their complex angular flux solutions in order to provide an appropriate demonstration of the model's capabilities. It is shown that the POD model can resolve these fluxes efficiently and accurately. In comparison to high resolution models this POD model can reduce the size of a problem by up to two orders of magnitude without compromising accuracy. Solving times are also reduced by similar factors. (C) 2015 Elsevier Inc. All rights reserved.

【 授权许可】

Free