Fast solution methods of parameterized high-fidelity models

Abstract

High-fidelity simulation based on solving partial differential equations has become an indispensable approach in innovative research of nuclear energy. However, due to the prohibitively high computation cost, it is yet unfeasible to run simulations on the whole reactor core level with all the physics resolved on fine meshes, especially in the analysis which need carry out many times of calculations. Model order reduction strategies are promising ways to achieve significant speedups by replacing the original large-scale model by a reduced order model of substantially smaller scale. Two effective model order reduction techniques, i.e., the reduced basis finite element method and the POD-Galerkin method, are studied and applied in the present study to solve the transient thermo-elastic problem and the fluid dynamics problem, respectively. For the former, the boundary conditions, the physical parameters and the body heat source are treated as parameters. The greedy-POD algorithm is applied to construct reduced basis space and the for the latter, the Reynolds number is treated as a parameter. The pure POD method is applied to construct the basis space. During on the online stage, three to five orders of computation speed up has been achieved by using the built reduced order models compared with that by the full order models while the accuracy of the results is well ensured. It is demonstrated that model order reduction techniques will be a practical way to deploy numerical reactors.