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We present a novel acceleration technique for improving the convergence ofsource iteration for discrete ordinates transport calculations. Our approachuses the idea of the dynamic mode decomposition (DMD) to estimate the slowlydecaying modes from source iteration and remove them from the solution. Thememory cost of our acceleration technique is that the scalar flux for a numberof iterations must be stored; the computational cost is a singular valuedecomposition of a matrix comprised of those stored scalar fluxes.On 1-D slabgeometry problems we observe an order of magnitude reduction in the number oftransport sweeps required compared to source iteration and that the number ofsweeps required is independent of the scattering ratio in the problem. Theseobservations hold for an extremely heterogeneous problem and a 2-D problem. In2-D we do observe that the effectiveness of the approach slowly degrades as themesh is refined, but is still about one order of magnitude faster than sourceiteration.

Acceleration of Source Iteration using the Dynamic Mode Decomposition