Xu, Leidong2019-11-152019-11-152019-12-01http://hdl.handle.net/2097/40267An algorithm based on dynamic mode decomposition (DMD) is presented for acceleration of the power method (PM) and fattened power method (FPM) that takes advantage of prediction from a restarted DMD process to correct an unconverged solution. The power method is a simple iterative scheme for determining the dominant eigenmode, and its variants, such as fattened power method, have long been used to solve the k-eigenvalue problem in reactor analysis. DMD is a data driven technique that extracts dynamics information from time-series data with which a reduced-order surrogate model can be constructed. DMD accelerated PM (DMD-PM) and DMD-accelerated FPM (DMD-FPM) generate “snapshots” from a few iterations and extrapolate space in “fictitious time” to produce a more accurate estimate of the dominant mode. This process is repeated until the solution is converged to within a suitable tolerance. To illustrate the performance of both two schemes, a 1-D test problem designed to resemble a boiling water reactor (BWR) and the well-studied 2-D C5G7 benchmark were analyzed. Compared to the PM without acceleration, these tests have demonstrated that DMD-PM and DMD-FPM method can reduce the number of iterations significantly.en-USPower methodDynamic Mode DecompositionAccelerationNuclear EngineeringNumerical SimulationEigenvalue ProblemThesis